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DP-700 Master Cheat Sheet

Implement and manage an analytics solution (30–35%)
Configure Microsoft Fabric workspace settings
Configure Spark Workspace Settings

• Purpose: Spark workspaces provide an environment for developing and executing Spark
applications within Microsoft Fabric. These settings control how Spark jobs are executed,
resource allocation, and other crucial parameters.

• Key Configuration Areas:

o Cluster Configuration:

▪ Cluster Type: Choose between serverless (auto-scaling) or dedicated

clusters. Serverless is ideal for ad-hoc jobs, while dedicated clusters offer
more control and performance for long-running workloads.

▪ Node Types: Select the type of Spark nodes (e.g., memory-optimized,

compute-optimized) based on your application's requirements.

▪ Node Count: Determine the number of nodes in your cluster. This impacts
processing power and cost.

o Spark Configuration:

▪ Spark Properties: Fine-tune Spark behavior by setting specific properties

(e.g., driver memory, executor cores) to optimize performance and resource
utilization.

▪ Libraries: Specify any external libraries or dependencies required by your

Spark applications.

o Security:

▪ Access Control: Configure user permissions and access levels to control who
can create and execute Spark jobs within the workspace.

▪ Integration with Azure Active Directory: Integrate with Azure AD for

centralized identity and access management.

Configure Domain Workspace Settings

• Purpose: Domains in Microsoft Fabric provide a way to organize and manage resources,
users, and data access within a workspace. Domain settings control how these resources are
managed and secured.

• Key Configuration Areas:

o Data Governance:

▪ Data Classification: Define data sensitivity levels and apply classification

labels to data assets within the domain.

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▪ Data Lineage: Track the origin and transformations of data to improve data
quality and traceability.

▪ Data Quality Rules: Implement rules to monitor and enforce data quality
standards.

o User Management:

▪ Roles and Permissions: Define roles with specific permissions within the
domain, allowing for fine-grained access control.

▪ User Groups: Create groups of users and assign them to specific roles or
permissions.

o Resource Management:

▪ Resource Quotas: Set limits on resource consumption (e.g., compute,

storage) for users or groups within the domain.

▪ Cost Control: Monitor and control costs associated with resource usage
within the domain.

In Summary:

• Both Spark workspace settings and domain workspace settings play crucial roles in managing
and optimizing data engineering workloads within Microsoft Fabric.

• Spark workspace settings focus on configuring the Spark environment for optimal job
execution, while domain workspace settings focus on broader governance, security, and
resource management aspects.

• By carefully configuring these settings, organizations can ensure efficient, secure, and cost-
effective data processing within their Microsoft Fabric environment.

Note: These are general explanations. The specific configuration options and their impact may vary
depending on the version of Microsoft Fabric and your specific use case. Refer to the official
Microsoft Fabric documentation for the most up-to-date and detailed information.

Configure OneLake Workspace Settings

• OneLake is Microsoft Fabric's unified data storage layer. It's a single, scalable, and secure
repository for all your organization's data, regardless of its source or format.

• Workspace Settings within OneLake primarily focus on:

o Security and Access Control:

▪ External App Access: This setting allows or restricts access to OneLake data
by applications outside of the Microsoft Fabric environment.

▪ Data Sync with OneLake File Explorer: Enables or disables the use of the
OneLake File Explorer, a tool for interacting with OneLake data.

• Key Considerations:

o Security: Carefully evaluate and configure external app access to ensure that only
authorized applications can interact with your sensitive data.

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o Usability: Determine if the OneLake File Explorer is necessary for your organization's
workflows and enable it accordingly.

Configure Data Workflow Workspace Settings

• Data Workflows are the core of data processing and transformation within Microsoft Fabric.
They orchestrate tasks like data ingestion, cleaning, transformation, and loading into target
systems.

• Workspace Settings for Data Workflows often involve:

o Resource Allocation:

▪ Compute Resources: Configure the amount of compute power (e.g., CPU,

memory) allocated to data workflow executions. This impacts performance
and cost.

▪ Storage Resources: Define storage limits and quotas for data used in
workflows.

o Scheduling and Triggers:

▪ Schedule Definitions: Set up recurring schedules for automated workflow

execution (e.g., daily, hourly).

▪ Event-Based Triggers: Configure workflows to trigger based on specific

events, such as data arrival in a source system.

o Monitoring and Logging:

▪ Logging Level: Adjust the level of detail captured in logs for troubleshooting
and performance analysis.

▪ Alerting: Configure alerts to notify you of workflow failures or other critical

events.

• Key Considerations:

o Performance: Optimize resource allocation to achieve the desired performance for

your data processing needs while minimizing costs.

o Reliability: Implement robust scheduling and triggering mechanisms to ensure

timely and reliable data processing.

o Observability: Configure comprehensive monitoring and logging to gain insights into

workflow execution and identify potential issues.

In Summary

Both OneLake and Data Workflow workspace settings play crucial roles in establishing a secure,
efficient, and reliable data environment within Microsoft Fabric. By carefully configuring these
settings, organizations can optimize data processing performance, enhance data security, and gain
better control over their data workflows.

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Implement lifecycle management in Fabric

Configure Version Control in Microsoft Fabric

• Purpose:

o Track changes to your data engineering assets (pipelines, dataflows, notebooks, etc.).

o Collaborate effectively within teams by allowing multiple developers to work on the

same assets simultaneously.

o Enable rollback to previous versions in case of errors or unexpected changes.

o Improve code quality and maintainability through code reviews and history tracking.

• Integration with Git:

o Fabric integrates seamlessly with Git repositories like GitHub and Azure DevOps.

o You can connect your Fabric workspace to a Git repository and synchronize your
assets.

o Changes made in Fabric are reflected in the Git repository, and vice versa.

• Workflow:

Connect to Git: Establish a connection between your Fabric workspace and the chosen Git
repository.

Initialize: Create a new repository or connect to an existing one.

Synchronize: Push changes from Fabric to Git or pull changes from Git to Fabric.

Branching and Merging: Utilize Git branching strategies (e.g., GitFlow) for parallel
development and feature releases.

Code Reviews: Conduct code reviews within Git to ensure quality and adherence to best
practices.

Implement Database Projects in Microsoft Fabric

• Purpose:

o Define and manage database objects (tables, views, stored procedures, etc.) as a
unit.

o Simplify database development and deployment by tracking changes and

dependencies.

o Automate database deployments to different environments (development, testing,

production).

o Improve database consistency and reduce errors during deployment.

• Key Concepts:

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o Project File: A single file that contains all the database objects and their definitions.

o Build Process: The process of compiling and validating the project file, generating
deployment scripts.

o Deployment Process: The process of applying the deployment scripts to a target

database.

• Workflow:

Create a Project: Define a new database project and add the necessary database objects.

Develop and Test: Develop and test database objects within the project environment.

Build: Build the project to generate deployment scripts.

Deploy: Deploy the database objects to the target environment using the generated scripts.

Version Control: Integrate database projects with version control systems (like Git) to track
changes and enable rollbacks.

Benefits of Using Version Control and Database Projects in Fabric:

• Improved Collaboration: Enhanced teamwork and code sharing among data engineers.

• Reduced Errors: Minimized human errors during development and deployment.

• Increased Efficiency: Streamlined development and deployment processes.

• Better Code Quality: Improved code maintainability and adherence to best practices.

• Enhanced Control: Better tracking of changes and ability to rollback to previous versions.

Create and configure deployment pipelines

In the context of Microsoft Fabric, deployment pipelines are a crucial mechanism for managing the
lifecycle of your data engineering solutions. They enable you to:

• Move items between workspaces: This includes dataflows, notebooks, datasets, and other
artifacts.

• Control the flow of changes: Ensure that changes are thoroughly tested before being
deployed to production environments.

• Maintain consistency: Standardize the deployment process across different environments.

• Reduce errors: Minimize the risk of human error during manual deployments.

Key Steps in Creating and Configuring Deployment Pipelines:

1. Define Stages:

o Create a sequence of stages that represent different environments, such as

Development, Test, and Production.

o Each stage typically corresponds to a separate Fabric workspace.

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2. Add Workspaces:

o Associate each stage with the corresponding Fabric workspace.

3. Configure Deployment Rules:

o Specify which items should be deployed between stages.

o Define conditions for successful deployment, such as passing unit tests or manual
approvals.

4. Automate Deployments:

o Schedule automatic deployments between stages.

o Trigger deployments based on events, such as code check-ins or successful test runs.

5. Monitor and Manage:

o Track the progress of deployments.

o Investigate and resolve any deployment issues.

o Manage and update the deployment pipeline as needed.

Example Scenario:

Imagine you have a dataflow that extracts data from a source, transforms it, and loads it into a target
table. You can create a deployment pipeline with the following stages:

• Development: This is where you develop and test the dataflow locally.

• Test: The dataflow is deployed to a test environment where it is tested against a

representative dataset.

• Staging: The dataflow is deployed to a staging environment that closely resembles the
production environment.

• Production: The final deployment to the production environment where the dataflow
processes real-time data.

Benefits of Using Deployment Pipelines:

• Improved Quality: Thorough testing in each stage reduces the risk of errors in production.

• Increased Efficiency: Automated deployments save time and effort compared to manual
processes.

• Better Collaboration: Deployment pipelines facilitate collaboration between data engineers

and other stakeholders.

• Enhanced Control: Centralized management of deployments provides better visibility and

control over the process.

By effectively creating and configuring deployment pipelines, you can streamline your data
engineering workflows, improve the quality of your solutions, and accelerate the time to market for
your data-driven initiatives.

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Note: This is a general overview. Specific implementation details may vary depending on your
specific requirements and the complexity of your data engineering solutions.

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Configure security and governance

Implement Workspace-Level Access Controls

• Purpose: To control who can access and interact with a specific Microsoft Fabric workspace.
This is the first layer of security.

• Methods:

o Workspace Roles: Assign predefined roles (e.g., Viewer, Contributor, Admin) to users
or groups. Each role has specific permissions within the workspace.

o Azure Active Directory (Azure AD) Integration: Leverage existing Azure AD groups
and roles for seamless access management.

o Custom Roles: Create custom roles with tailored permissions to fine-tune access for
specific needs.

Implement Item-Level Access Controls

• Purpose: To control access to individual items (e.g., datasets, pipelines, notebooks) within a
workspace. This provides more granular security.

• Methods:

o Permissions on Items: Set permissions (e.g., Read, Write, Execute) on specific items
for individual users or groups.

o Data Classification: Apply sensitivity labels to data to enforce access controls based
on the sensitivity level.

o Dynamic Data Masking: Restrict access to sensitive data within tables or columns
based on user roles or other criteria.

In essence:

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• Workspace-level controls act as a broad gatekeeper, determining who can even enter the
workspace.

• Item-level controls provide fine-grained control within the workspace, allowing you to
specify who can access and perform specific actions on individual data assets.

By effectively implementing both workspace-level and item-level access controls, you can ensure that
your data remains secure and that only authorized users can access and work with it.

Implementing Access Controls in Microsoft Fabric

Microsoft Fabric provides robust security features to control access to data at various levels:

• Row-Level Security (RLS):

o Restricts access to specific rows within a table based on the user's role or attributes.

o Implemented using security predicates (e.g., WHERE clause in SQL) that filter data
dynamically for each user.

o Example: A sales manager can only see sales data for their specific region.

• Column-Level Security (CLS):

o Controls access to specific columns within a table.

o Prevents users from viewing sensitive information like social security numbers or
credit card details.

o Configured using security policies that define which columns are visible to different
user groups.

• Object-Level Security:

o Controls access to specific objects within Fabric, such as datasets, dataflows,

pipelines, and notebooks.

o Implemented through role-based access control (RBAC), where users are assigned
roles with specific permissions on objects.

• File-Level Security (for OneLake):

o Controls access to files and folders within the OneLake data lake.

o Leveraged through Azure Data Lake Storage permissions and integration with Azure
Active Directory.

Implementing Dynamic Data Masking

• Dynamic Data Masking:

o Hides sensitive data from unauthorized users while allowing authorized users to see
the full data.

o Implemented by masking sensitive data (e.g., credit card numbers, social security
numbers) with placeholder characters (e.g., 'X') when accessed by users without the
necessary permissions.

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o Helps protect sensitive data while still enabling data analysis and reporting for
authorized users.

Key Considerations for Implementing Access Controls and Data Masking:

• Least Privilege Principle: Grant users only the minimum necessary permissions to perform
their job duties.

• Regular Reviews: Regularly review and update access controls to ensure they remain aligned
with business needs and security best practices.

• Data Classification: Classify data based on sensitivity levels (e.g., public, internal,
confidential) to guide access control decisions.

• Monitoring and Auditing: Monitor access activities and audit logs to detect and respond to
potential security threats.

Benefits of Implementing Robust Access Controls and Data Masking:

• Enhanced Data Security: Protects sensitive data from unauthorized access and misuse.

• Improved Compliance: Helps organizations comply with data privacy regulations (e.g., GDPR,
CCPA).

• Increased Trust and Confidence: Builds trust with customers and employees by
demonstrating a commitment to data security.

• Improved Data Governance: Provides better control over data access and usage within the
organization.

Applying Sensitivity Labels to Items in Microsoft Fabric

• Purpose:

o Data Classification: Categorize data based on its sensitivity (e.g., "Confidential,"

"Public," "Internal").

o Access Control: Restrict access to sensitive data based on the label.

o Data Loss Prevention (DLP): Prevent unauthorized sharing or modification of

sensitive information.

o Compliance: Meet regulatory requirements (e.g., GDPR, HIPAA) by ensuring proper

handling of sensitive data.

• How to Apply:

o From the Flyout Menu:

1. Locate the sensitivity indication in the item header.

2. Click the flyout menu.

3. Select the desired sensitivity label.

o In Item Settings:

1. Open the item's settings.

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2. Find the "Sensitivity" section.

3. Choose the desired label.

• Key Considerations:

o Label Definitions: Clearly define the criteria for each sensitivity label within your
organization.

o User Permissions: Ensure appropriate users have the necessary permissions to apply
and modify sensitivity labels.

o Label Inheritance: Understand how labels are inherited from data sources and how
downstream inheritance impacts labeling decisions.

o Protected Labels: Be aware of the restrictions associated with protected labels,

which have file protection policies associated with them.

Endorsing Items in Microsoft Fabric

• Purpose:

o Data Quality: Signify that an item has been reviewed and approved for use.

o Trust and Reliability: Build trust among users by indicating that an item is of high
quality and can be relied upon.

o Collaboration: Facilitate collaboration by highlighting endorsed items for others to

use.

• How to Endorse:

o Manual Endorsement:

1. Locate the item you want to endorse.

2. Click the "Endorse" button (if available).

o Automated Endorsement:

1. Define criteria for automated endorsement (e.g., successful data quality

checks, specific label).

2. Configure automated workflows to apply endorsements based on these

criteria.

• Key Considerations:

o Endorsement Criteria: Establish clear and objective criteria for endorsing items.

o Communication: Communicate the endorsement process and criteria to all relevant

users.

o Version Control: Consider how endorsements are handled across different versions
of an item.

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In Summary

Applying sensitivity labels and endorsing items are crucial for data governance and security within
Microsoft Fabric. By effectively implementing these features, organizations can:

• Protect sensitive data from unauthorized access and misuse.

• Ensure data quality and reliability.

• Improve data collaboration and trust.

• Comply with relevant regulations and industry standards.

Orchestrate processes
Choosing Between a Pipeline and a Notebook in Microsoft Fabric

In Microsoft Fabric, both pipelines and notebooks are valuable tools for data engineering tasks, but
they serve different purposes:

• Pipelines:

o Orchestration: Primarily designed for orchestrating complex data workflows. They

can chain together various activities like data ingestion, transformation, loading, and
more.

o Modularity: Break down large tasks into smaller, manageable units (activities).

o Reusability: Easily reuse and modify existing pipelines for different scenarios.

o Scheduling and Triggers: Built-in support for scheduling and triggering pipelines
based on events (e.g., file arrival, database changes).

o Monitoring and Logging: Provide comprehensive monitoring and logging capabilities

for tracking pipeline execution and identifying issues.

• Notebooks:

o Interactive Development: Ideal for exploratory data analysis, prototyping, and

iterative development of data transformation logic.

o Flexibility: Support various languages (Python, Scala, SQL) and libraries for data
manipulation and analysis.

o Collaboration: Facilitate collaboration among data scientists and engineers through

shared notebooks and version control.

o Visualization: Integrate visualizations and dashboards directly within the notebook

environment.

When to Choose a Pipeline:

• Complex Workflows: When you need to orchestrate multiple steps, such as ingesting data
from various sources, transforming it using different tools, and loading it into multiple
destinations.

• Repetitive Tasks: For tasks that need to be executed regularly or triggered by external
events.

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• Data Pipelines: To create robust and maintainable data pipelines that can be easily
monitored and managed.

When to Choose a Notebook:

• Exploratory Data Analysis: For initial data exploration, cleaning, and transformation.

• Prototyping: To quickly prototype data transformation logic and algorithms.

• Iterative Development: For tasks that require iterative development and refinement.

• Custom Analysis: When you need to perform custom analysis or use specialized libraries not
readily available in pipeline activities.

Design and Implement Schedules and Event-Based Triggers

In Microsoft Fabric, you can schedule pipelines and trigger them based on various events to
automate data processing tasks. Here's a breakdown:

Scheduling Pipelines:

• Recurrence: Schedule pipelines to run at regular intervals:

o Daily: Run once or multiple times per day.

o Weekly: Run on specific days of the week.

o Monthly: Run on specific dates or the nth day of the month.

o Custom: Define more complex recurrence patterns using cron expressions.

• Time Zone: Specify the time zone for scheduled runs.

• Start/End Time: Define a specific start and end time for the schedule.

Event-Based Triggers:

• File System Events: Trigger pipelines when files are added, modified, or deleted in specific
locations (e.g., Azure Blob Storage, local file system).

• Database Events: Trigger pipelines when changes occur in databases (e.g., new rows
inserted, data updated).

• Webhooks: Trigger pipelines in response to events from external systems via webhooks.

• Manual Triggers: Trigger pipelines manually on demand.

Implementing Schedules and Triggers:

1. Create a Pipeline: Design and build your data pipeline using the desired activities.

2. Configure Triggers:

o Scheduled Triggers: In the pipeline settings, define the recurrence pattern, time
zone, and start/end time.

o Event-Based Triggers: Select the appropriate trigger type (e.g., file system, database)
and configure the event parameters.

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3. Deploy and Monitor: Deploy the pipeline and monitor its execution to ensure it's running as
expected.

Implement orchestration patterns with notebooks and pipelines, including parameters and
dynamic expressions

Orchestration Patterns

• What is Orchestration?

o In the context of data science and machine learning, orchestration refers to the
automated and coordinated execution of a series of tasks or processes. This can
involve:

▪ Data ingestion and transformation

▪ Model training and evaluation

▪ Model deployment and monitoring

▪ Experiment tracking and versioning

• Why is Orchestration Important?

o Reproducibility: Ensures that data science experiments can be reliably repeated with
the same results.

o Scalability: Allows for scaling data processing and model training to handle larger
datasets and increased demand.

o Efficiency: Automates repetitive tasks, freeing up data scientists to focus on higher-

level analysis and model development.

o Collaboration: Facilitates collaboration among team members by providing a shared

framework for managing and executing projects.

Implementing Orchestration with Notebooks and Pipelines

• Notebooks:

o Advantages:

▪ Interactive environment for exploratory data analysis, experimentation, and

prototyping.

▪ Excellent for iterative development and debugging.

▪ Can be easily shared and versioned.

o Limitations:

▪ Can become complex and difficult to maintain for large or intricate

workflows.

▪ May not be ideal for production-level deployments due to potential

performance bottlenecks.

• Pipelines:

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o Advantages:

▪ Designed specifically for defining and executing complex workflows.

▪ Provide a structured and visual representation of the data flow.

▪ Offer features like task scheduling, dependency management, and error

handling.

▪ Well-suited for production environments and continuous

integration/continuous delivery (CI/CD) pipelines.

o Examples:

▪ Azure Machine Learning Pipelines: A cloud-based service for building,

deploying, and managing machine learning pipelines.

▪ MLflow Pipelines: An open-source platform for managing the complete

machine learning lifecycle, including experimentation, reproducibility, and
deployment. 1

1. granulate.io

granulate.io

Parameters and Dynamic Expressions

• Parameters:

o Allow you to define configurable values that can be easily modified without changing
the underlying code.

o Examples:

▪ Data paths

▪ Model hyperparameters

▪ Resource configurations

o Enable you to easily experiment with different configurations and tune model
performance.

• Dynamic Expressions:

o Allow you to dynamically generate values based on other parameters, data, or

system information.

o Examples:

▪ Constructing file paths based on dates or other variables.

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▪ Selecting specific data subsets based on conditions.

▪ Adapting resource allocation based on workload demands.

Example: Orchestrating a Model Training Pipeline

1. Data Ingestion: Define a pipeline step to extract data from a data source (e.g., Azure Blob
Storage, SQL Database).

2. Data Transformation: Include steps for data cleaning, preprocessing, and feature
engineering.

3. Model Training: Train a machine learning model using a chosen algorithm (e.g., scikit-learn,
TensorFlow, PyTorch).

4. Model Evaluation: Evaluate the trained model's performance using appropriate metrics (e.g.,
accuracy, precision, recall).

5. Model Deployment: Deploy the trained model to a suitable environment (e.g., Azure
Container Instances, Azure Kubernetes Service).

Key Considerations for DP-700

• Azure Machine Learning Pipelines: Understand the core components of Azure Machine
Learning Pipelines, including pipelines, components, datasets, and compute targets.

• Parameterization: Learn how to define and use parameters effectively within pipelines and
components.

• Dynamic Expressions: Explore how to leverage dynamic expressions to create flexible and
adaptable pipelines.

• Debugging and Monitoring: Understand how to debug pipeline executions and monitor
pipeline performance.

In Summary

Orchestration plays a crucial role in streamlining and automating the machine learning lifecycle. By
effectively utilizing notebooks, pipelines, parameters, and dynamic expressions, data scientists can
build robust and efficient solutions that can be easily scaled and deployed in production
environments.

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Ingest and transform data (30–35%)

Design and implement loading patterns
Design and Implement Full and Incremental Data Loads

• Full Data Load:

o Definition: This involves loading the entire dataset from the source system into the
data warehouse or data mart.

o Use Cases:

▪ Initial data loading when a data warehouse is first implemented.

▪ Periodic refreshes to ensure data accuracy and completeness.

▪ Situations where data quality issues are suspected.

o Implementation:

▪ Extract: Data is extracted from the source system using various methods like
database queries, APIs, file transfers (e.g., flat files, CSV), or ETL tools.

▪ Transform: Data is transformed to match the target data warehouse schema,

including data cleaning, type conversions, and aggregations.

▪ Load: Data is loaded into the target tables using efficient techniques like bulk
loading, parallel processing, and change data capture (CDC).

• Incremental Data Load:

o Definition: This involves loading only the changes that have occurred in the source
system since the last data load.

o Use Cases:

▪ Minimize data movement and processing time, especially for large datasets.

▪ Improve data freshness and responsiveness to business needs.

▪ Reduce the load on source systems.

o Implementation:

▪ Change Data Capture (CDC): Identify and capture changes in the source
system (insertions, updates, deletions) using techniques like:

▪ Log-based CDC: Monitor database logs for changes.

▪ Trigger-based CDC: Use database triggers to capture changes.

▪ Query-based CDC: Periodically query the source system for changes.

▪ Extract, Transform, Load (ETL): Process the captured changes and load them
into the target system.

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Prepare Data for Loading into a Dimensional Model

• Dimensional Modeling: A data warehousing technique that organizes data into two main
types of tables:

o Fact Tables: Store numerical measurements (e.g., sales, revenue, costs).

o Dimension Tables: Store descriptive attributes about the facts (e.g., customer,
product, time, location).

• Data Preparation Steps:

o Data Cleansing:

▪ Handle missing values: Impute missing values using appropriate methods

(e.g., mean, median, mode, interpolation).

▪ Correct data inconsistencies: Identify and correct errors, duplicates, and

inconsistencies.

▪ Address data quality issues: Remove outliers, standardize data formats, and
ensure data accuracy.

o Data Transformation:

▪ Data type conversions: Convert data types to match the target data
warehouse schema (e.g., strings to dates, integers to decimals).

▪ Data aggregation: Summarize data at different levels of granularity (e.g.,

daily, monthly, yearly).

▪ Data enrichment: Add additional information to the data (e.g., customer

demographics, product categories).

o Conformance:

▪ Granularity: Ensure that the data is at the correct level of detail for analysis.

▪ Consistency: Ensure that data is consistently defined and used throughout

the data warehouse.

▪ Accuracy: Ensure that the data is accurate and reflects the real-world
situation.

• Key Considerations:

o Business requirements: Understand the specific analytical needs of the business.

o Data source characteristics: Analyze the structure and quality of the source data.

o Performance requirements: Design the data warehouse to support efficient query

processing.

Tools and Technologies:

• ETL Tools: Informatica PowerCenter, Talend, SSIS (SQL Server Integration Services)

• Data Warehousing Platforms: Snowflake, Amazon Redshift, Google BigQuery

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• Programming Languages: Python, SQL, Java

• Cloud Services: AWS, Azure, GCP

By carefully designing and implementing full and incremental data loads, and by preparing data
effectively for a dimensional model, organizations can build robust and efficient data warehouses
that provide valuable insights for decision-making.

Design and implement a loading pattern for streaming data

Understanding Streaming Data

• Continuous Flow: Streaming data arrives in a continuous, real-time stream, unlike batch data
which is processed in discrete chunks.

• High Volume & Velocity: Streaming data often involves high volumes of data arriving at rapid
speeds.

• Low Latency Requirements: Many streaming applications require low latency processing to
provide timely insights or react quickly to events.

Designing a Loading Pattern

1. Data Source:

o Identify the source of your streaming data (e.g., IoT devices, social media feeds,
financial market data).

o Understand the data format (e.g., JSON, Avro, Protobuf).

o Determine the data volume and velocity.

2. Data Ingestion:

o Choose an appropriate streaming engine:

▪ Microsoft Fabric Dataflow: Offers built-in connectors for various streaming

sources and supports Spark Structured Streaming for processing.

▪ Azure Event Hubs: A high-throughput, low-latency event ingestion service

that can handle millions of events per second.

▪ Azure IoT Hub: Specifically designed for IoT device data, enabling
bidirectional communication and device management.

o Data Preprocessing:

▪ Handle data cleaning, transformation, and enrichment during ingestion.

▪ Consider using data pipelines or stream processing libraries for efficient

processing.

3. Data Storage:

o Choose a suitable storage solution:

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▪ Azure Data Lake Storage Gen2: Highly scalable and cost-effective for storing
large volumes of streaming data.

▪ Azure Synapse Analytics: A unified analytics service that combines data

warehousing and data lake capabilities.

▪ Azure Cosmos DB: A globally distributed database service that supports

various data models, including document, graph, and time series.

4. Data Processing:

o Stream Processing:

▪ Use Spark Structured Streaming, KQL (Kusto Query Language), or other

stream processing frameworks to perform real-time analysis, aggregations,
and transformations.

o Windowing Functions:

▪ Apply windowing functions (e.g., tumbling, sliding, session) to group data

into time-based intervals for processing.

5. Data Consumption:

o Real-time Dashboards and Visualizations:

▪ Use Power BI or other visualization tools to create real-time dashboards for

monitoring and analyzing streaming data.

o Machine Learning Models:

▪ Feed streaming data to machine learning models for real-time predictions,

anomaly detection, and other insights.

o Alerting and Notifications:

▪ Set up alerts and notifications based on real-time events or anomalies.

Example: IoT Sensor Data

1. Data Source: IoT sensors generating temperature and humidity data.

2. Ingestion: Use Azure IoT Hub to ingest sensor data, perform initial data validation, and route
data to an Event Hub.

3. Storage: Store the processed data in Azure Data Lake Storage Gen2.

4. Processing: Use Spark Structured Streaming to:

o Calculate average temperature and humidity per hour.

o Detect anomalies (e.g., sudden temperature spikes).

o Generate real-time alerts for critical conditions.

5. Consumption:

o Display real-time sensor data and alerts on a Power BI dashboard.

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o Feed the processed data to a machine learning model for predictive maintenance.

Key Considerations

• Scalability: Design a system that can handle increasing data volumes and velocities.

• Fault Tolerance: Implement mechanisms to ensure data reliability and recover from failures.

• Security: Secure your data pipelines and storage solutions to prevent unauthorized access.

• Monitoring: Continuously monitor your streaming data pipelines to identify and resolve
issues.

By carefully designing and implementing a loading pattern for streaming data, you can effectively
leverage the power of real-time data to gain valuable insights and make data-driven decisions.

Ingest and transform batch data

Choosing an Appropriate Data Store

In the context of the DP-700 exam, which focuses on Microsoft Fabric, the choice of data store
depends heavily on the specific needs and characteristics of your data. Here are some key factors to
consider:

• Data Type:

o Structured: Relational data with well-defined schemas (e.g., customer databases,

financial transactions).

▪ Suitable Stores: Data Warehouse, Datamarts

o Semi-structured: Data with some structure but not strictly adhering to a fixed
schema (e.g., JSON, XML files).

▪ Suitable Stores: Lakehouse

o Unstructured: Data with no predefined structure (e.g., images, videos, text

documents).

▪ Suitable Stores: Lakehouse

• Data Volume and Velocity:

o High Volume: Large datasets requiring scalable storage and processing.

▪ Suitable Stores: Lakehouse, Data Warehouse

o High Velocity: Data arriving at high speeds (e.g., streaming data).

▪ Suitable Stores: Lakehouse, Event Hub (for real-time ingestion)

• Data Access Patterns:

o Frequent, ad-hoc queries: Data Warehouse, Datamarts

o Data exploration and discovery: Lakehouse

o Real-time analytics: Event Hub, Stream Analytics

• Data Governance and Security:

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o Sensitivity: Consider data classification and encryption requirements.

o Access control: Implement appropriate security measures to protect sensitive data.

Key Data Stores in Microsoft Fabric:

• Lakehouse: A unified storage layer combining the flexibility of a data lake with the structure
of a data warehouse. Ideal for diverse data types, high volume, and various access patterns.

• Data Warehouse: Optimized for high-performance analytics on structured data. Supports

complex queries and aggregations.

• Datamart: A smaller, focused data store for specific business needs. Provides faster query
performance for targeted analyses.

Choosing Between Dataflows, Notebooks, and T-SQL for Data Transformation

The choice of transformation method depends on the complexity of the transformations, the skills of
your team, and the desired level of control and flexibility.

• Dataflows:

o Strengths:

▪ User-friendly interface: Visual drag-and-drop interface for building data

transformation pipelines.

▪ Scalability: Leverages Spark for distributed processing.

▪ Built-in transformations: Wide range of pre-built transformations for

common data manipulation tasks.

o Use Cases:

▪ ETL/ELT processes involving complex data transformations.

▪ Data cleaning, shaping, and enrichment.

▪ Preparing data for loading into data warehouses or datamarts.

• Notebooks:

o Strengths:

▪ Flexibility: Supports various programming languages (e.g., Python, R, Scala)

for custom transformations.

▪ Interactivity: Allows for exploratory data analysis and iterative development.

▪ Code reusability: Enables the creation of reusable functions and libraries.

o Use Cases:

▪ Advanced data science and machine learning tasks.

▪ Custom data transformations that require specialized programming logic.

▪ Building interactive data exploration and visualization dashboards.

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• T-SQL:

o Strengths:

▪ Familiarity: Widely used and well-understood language for relational

databases.

▪ Performance: Optimized for performing complex queries and

transformations within a relational database environment.

▪ Integration: Seamlessly integrates with SQL Server and other relational

databases.

o Use Cases:

▪ Data transformations within a relational database context.

▪ Creating views and stored procedures for data access and manipulation.

▪ Data warehousing tasks, such as data cleansing and aggregation.

In Summary:

• Carefully assess your data characteristics and business requirements to select the most
appropriate data store.

• Choose the transformation method based on the complexity of the transformations, the
skills of your team, and the desired level of control and flexibility.

• Consider using a combination of methods to leverage the strengths of each approach. For
example, use dataflows for initial data preparation and then use notebooks or T-SQL for
more advanced or custom transformations.

Create and Manage Shortcuts to Data

• What are Shortcuts?

o In the context of data engineering, shortcuts within Microsoft Fabric provide a way
to quickly access and work with data located in various sources without physically
moving or copying the data itself.

o They essentially act as pointers or references to the original data.

• Benefits of Using Shortcuts:

o Improved Data Governance: Centralized access control and management become

easier as you don't need to duplicate data across different locations.

o Enhanced Performance: Queries can often be executed more efficiently as shortcuts

can leverage optimized data access paths within the source system.

o Reduced Storage Costs: Eliminating the need to store multiple copies of the same
data significantly reduces storage requirements.

o Increased Flexibility: Easily point shortcuts to different data sources as your needs
evolve, without affecting the underlying data.

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• Types of Shortcuts (may vary depending on the specific tools and technologies used):

o Linked Services: Connections to external data sources (e.g., databases, file systems)
that allow you to query and access data without the need for data movement.

o Views: Virtual tables that provide a specific view or subset of data from a larger
table.

o External Tables: Point to data stored in external formats (e.g., Parquet, Avro) and
allow you to query them as if they were regular tables.

• Creating and Managing Shortcuts:

o Define the Source: Identify the location and format of the data you want to create a
shortcut for.

o Establish Connectivity: Ensure you have the necessary permissions and credentials
to access the source data.

o Create the Shortcut: Use the appropriate tools or interfaces within your data
engineering environment (e.g., data catalog, data integration tools) to define the
shortcut.

o Manage and Maintain: Regularly review and update shortcuts as data sources
change or new requirements arise.

Implement Mirroring

• What is Data Mirroring?

o Data mirroring is a high-availability and disaster recovery technique where a

complete copy of a database is maintained on a separate server.

o Changes made to the primary database are continuously replicated to the secondary
server.

• Key Concepts:

o Primary Database: The original database where data is actively modified.

o Secondary Database: The replica database that maintains an exact copy of the
primary.

o Replication: The process of copying data changes from the primary to the secondary.

o Failover: The process of switching to the secondary database as the active database
if the primary becomes unavailable.

• Benefits of Data Mirroring:

o High Availability: Minimize downtime by quickly failing over to the secondary

database in case of primary server failure.

o Disaster Recovery: Protect against data loss due to hardware failures, natural
disasters, or other unforeseen events.

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o Improved Performance: Distribute read traffic across multiple servers, improving

query performance and reducing load on the primary database.

• Implementing Data Mirroring:

o Choose a Mirroring Solution: Select the appropriate mirroring technology or service

based on your specific requirements (e.g., database-specific features, cloud-based
solutions).

o Configure Mirroring: Establish the connection between the primary and secondary
servers, configure replication settings (e.g., synchronization frequency, transaction
log shipping), and test failover procedures.

o Monitor and Maintain: Regularly monitor the mirroring process, ensure data
integrity, and perform necessary maintenance tasks (e.g., applying updates to both
servers).

Note: The specific implementation details and terminology may vary depending on the database
system (e.g., SQL Server, Oracle) and the chosen mirroring solution.

Ingest Data by Using Pipelines

• Pipelines: In the context of data engineering, pipelines are automated workflows that
orchestrate the movement and transformation of data from various sources to their
destinations. They provide a structured way to define and execute data processing tasks.

• Key Concepts:

o Data Sources: Pipelines can ingest data from diverse sources, including databases
(relational and NoSQL), cloud storage (Azure Blob Storage, AWS S3), data lakes, APIs,
and more.

o Data Transformations: Pipelines can incorporate data transformations, such as data

cleaning, enrichment, and feature engineering, to prepare data for analysis.

o Data Destinations: Pipelines deliver transformed data to various destinations,

including data warehouses, data lakes, data marts, and analytical tools.

o Orchestration: Pipelines orchestrate the sequence of tasks, handling dependencies

and error handling to ensure smooth data flow.

o Scheduling: Pipelines can be scheduled to run on a regular basis (e.g., daily, hourly)
or triggered by events (e.g., file arrival, API call).

• Benefits:

o Automation: Reduce manual effort and human error by automating data movement
and transformation tasks.

o Scalability: Handle large volumes of data efficiently by parallelizing tasks and

leveraging distributed processing.

o Reliability: Ensure data quality and consistency by implementing data validation and
error handling mechanisms.

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o Modularity: Break down complex data pipelines into smaller, reusable components
for better maintainability.

o Traceability: Track data lineage and understand the origin and transformations of
data.

Transform Data by Using PySpark, SQL, and KQL

• Data Transformation: The process of converting raw data into a structured, clean, and usable
format for analysis.

• Key Techniques:

o PySpark:

▪ A Python API for Apache Spark, a powerful distributed computing

framework.

▪ Enables distributed data processing and manipulation using Python.

▪ Provides high-level abstractions for common data operations like filtering,

aggregation, joining, and machine learning.

o SQL (Structured Query Language):

▪ A declarative language for querying and manipulating data in relational

databases.

▪ Widely used for data extraction, transformation, and loading (ETL)

operations.

▪ Offers a standardized and familiar syntax for data manipulation.

o KQL (Kusto Query Language):

▪ A powerful query language designed for analyzing large volumes of time-

series data.

▪ Optimized for fast and efficient querying of data stored in Azure Data
Explorer.

▪ Provides advanced features for time-series analysis, pattern recognition, and

anomaly detection.

In Summary:

• Pipelines are essential for automating and managing data ingestion and transformation
processes.

• PySpark, SQL, and KQL are powerful tools for transforming data within pipelines.

• The choice of tools and techniques depends on the specific data sources, transformation
requirements, and target destinations.

Denormalize Data

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• Normalization: Involves organizing data into tables with minimal redundancy, often resulting
in multiple tables linked by foreign keys.

• Denormalization: The process of adding redundant data to improve query performance and
data retrieval speed.

When to Denormalize:

• Frequent Joins: If you frequently join tables, denormalization can reduce the number of joins
needed, leading to faster query execution.

• Read-Heavy Workloads: When the primary focus is on reading data quickly, denormalization
can be beneficial.

• Data Warehousing: Data warehouses often use denormalized structures (e.g., star schema)
for efficient reporting and analysis.

Example:

Instead of having separate tables for Customers, Orders, and Order Details, you could combine
relevant information (customer name, order date, order details) into a single table.

Group and Aggregate Data

• Grouping: Divides data into subsets based on specific criteria (e.g., grouping customers by
region, orders by product category).

• Aggregation: Calculates summary statistics for each group (e.g., sum, average, count, min,
max).

Common Aggregation Functions:

• SUM: Calculates the total of a column.

• AVG: Calculates the average value of a column.

• COUNT: Counts the number of rows or non-null values.

• MIN: Finds the minimum value in a column.

• MAX: Finds the maximum value in a column.

Example:

Calculate the total sales for each product category, the average order value for each customer, or the
number of orders placed in each month.

Handle Duplicate, Missing, and Late-Arriving Data

• Duplicates:

o Identification: Use techniques like DISTINCT or GROUP BY to identify and remove

duplicate rows.

o Handling:

▪ Deletion: Remove duplicates entirely.

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▪ Deduplication: Keep only the first occurrence or select the most recent
entry.

• Missing Data:

o Identification: Identify missing values using techniques like IS NULL or checking for
empty strings.

o Handling:

▪ Imputation: Replace missing values with estimated values (e.g., mean,

median, mode).

▪ Removal: Remove rows or columns with missing values.

▪ Flag Missing Values: Create a new column to indicate missing values.

• Late-Arriving Data:

o Handling:

▪ Incremental Updates: Update existing data with the latest information.

▪ Change Data Capture: Track changes to source systems and apply them to
the target data store.

▪ Data Warehousing Techniques: Implement mechanisms to handle late-

arriving data in data warehousing scenarios.

Key Considerations:

• Data Quality: Ensure data accuracy and consistency throughout the data engineering
process.

• Performance: Optimize data transformations and storage for efficient query processing.

• Scalability: Design solutions that can handle growing data volumes and increasing processing
demands.

• Security: Implement appropriate security measures to protect sensitive data.

Ingest and transform streaming data

Choosing an Appropriate Streaming Engine

In the context of the DP-700 exam (Implementing Data Engineering Solutions Using Microsoft Fabric),
the choice of streaming engine often revolves around Microsoft Fabric's own capabilities. Here's a
breakdown of key considerations:

• Fabric's Built-in Streaming Capabilities:

o Spark Structured Streaming: This is a powerful and versatile engine within Fabric. It
leverages Apache Spark's distributed processing power to handle high-volume, real-
time data streams.

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▪ Strengths: Scalability, fault tolerance, ability to handle complex

transformations.

▪ Use Cases: Ideal for various streaming scenarios, including data ingestion
from IoT devices, social media feeds, and financial market data.

o Kusto Query Language (KQL): While primarily known for querying data in Azure Data
Explorer, KQL can also be used for stream processing within Fabric.

▪ Strengths: Powerful query language, good for real-time analytics and

monitoring.

▪ Use Cases: Suitable for stream processing tasks that involve filtering,
aggregation, and real-time analysis of streaming data.

• Factors Influencing the Choice:

o Data Volume and Velocity: High-volume, high-velocity streams might favor Spark
Structured Streaming for its scalability.

o Processing Requirements: Complex transformations or the need for advanced

stream processing features often point towards Spark.

o Real-time Analytics Needs: If real-time insights are crucial, KQL's query capabilities
can be valuable.

o Integration with Other Fabric Components: Consider how the chosen engine will
interact with other parts of your Fabric solution, such as data lakes, data
warehouses, and machine learning models.

Processing Data by Using Event Streams

• What are Event Streams?

o Event streams are a fundamental concept in real-time data processing. They

represent an ordered sequence of events that occur over time.

o Examples: Sensor readings, website clicks, financial transactions.

• Processing with Event Streams in Fabric:

o Event Hubs Integration: Fabric can seamlessly integrate with Azure Event Hubs, a
highly scalable and reliable event ingestion service.

o Stream Processing with Spark or KQL: Once data is ingested into Event Hubs, you
can use Spark Structured Streaming or KQL within Fabric to process the incoming
stream.

o Key Processing Steps:

▪ Data Ingestion: Capture events from various sources (IoT devices,

applications, etc.) and stream them to Event Hubs.

▪ Data Transformation: Apply transformations like filtering, aggregation,

windowing, and enrichment to the streaming data.

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▪ Data Loading: Load processed data into downstream systems like data lakes,
data warehouses, or real-time dashboards.

• Example Scenario:

o Imagine a scenario where you're monitoring website traffic. You can:

▪ Ingest website clickstream data into Event Hubs.

▪ Use Spark Structured Streaming to:

▪ Filter out irrelevant events.

▪ Calculate real-time metrics (e.g., page views per minute, user

sessions).

▪ Aggregate data into hourly or daily summaries.

▪ Load the processed data into a data lake for further analysis or a data
warehouse for reporting.

Process data by using Spark Structured Streaming

• What it is: Spark Structured Streaming is a framework within Apache Spark designed for
processing continuous streams of data. It extends the concepts of Spark SQL and DataFrames
to handle real-time data.

• Key Concepts:

o Data Sources: Ingest data from various sources like Kafka, Kinesis, message queues,
or files.

o DataFrames and Datasets: Represent streaming data in a structured format,

enabling efficient processing and transformations.

o Transformations: Apply various operations on streaming data, such as filtering,

mapping, joining, aggregating, and windowing.

o Output Sinks: Write processed data to destinations like databases, filesystems, or

other streaming systems.

o Exactly-Once Semantics: Ensure data is processed exactly once, even in the presence
of failures.

• Benefits:

o High Throughput: Handles large volumes of streaming data efficiently.

o Fault Tolerance: Resilient to failures with automatic recovery mechanisms.

o Scalability: Easily scales horizontally to handle increasing data volumes.

o Ease of Use: Provides a high-level API for processing streaming data, making it easier
to develop and maintain streaming applications.

Process data by using KQL

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• What it is: Kusto Query Language (KQL) is a powerful query language used in Azure Data
Explorer and other Azure services like Log Analytics and Application Insights. It's designed for
high-performance analytics on large volumes of time-series data.

• Key Features:

o Time Series Analysis: Excellent for analyzing data with timestamps, enabling time-
based aggregations, filtering, and visualizations.

o Scalability: Handles massive datasets efficiently due to its distributed architecture.

o Flexibility: Supports a wide range of data types and offers a rich set of operators for
data manipulation and analysis.

o Integration: Seamlessly integrates with other Azure services, enabling end-to-end

data pipelines.

• Use Cases:

o Log Analysis: Analyzing application logs, security logs, and system logs for
troubleshooting, monitoring, and security investigations.

o IoT Data Analysis: Processing and analyzing data from IoT devices, such as sensor
readings and telemetry data.

o Web Analytics: Analyzing website traffic logs to understand user behavior, identify
trends, and improve user experience.

o Operational Monitoring: Monitoring the performance and health of applications and

infrastructure.

Create Windowing Functions

• What it is: Windowing functions group data into time-based intervals or ranges, enabling
calculations within those specific windows. This is crucial for analyzing streaming data where
you need to aggregate or perform calculations over specific time periods.

• Types of Windows:

o Tumbling Window: Divides data into fixed-size, non-overlapping intervals (e.g., every
1 minute, every hour).

o Sliding Window: Divides data into overlapping intervals (e.g., a 5-minute window
sliding every 2 minutes).

o Session Window: Groups data based on inactivity periods, creating windows

between periods of activity.

• Applications:

o Calculating Metrics: Calculate metrics like average, sum, count, and other
aggregations within specific time windows.

o Detecting Anomalies: Identify unusual patterns or spikes in data within certain time
frames.

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o Real-time Monitoring: Track key performance indicators (KPIs) and monitor system
health in real-time.

In summary:

• Spark Structured Streaming is a powerful framework for processing real-time data streams
with high throughput, fault tolerance, and scalability.

• KQL is a query language optimized for time-series data analysis, particularly useful for log
analysis, IoT data processing, and operational monitoring.

• Windowing functions are essential for analyzing streaming data by grouping data into time-
based intervals, enabling meaningful calculations and insights.

These concepts are fundamental for data engineers working with streaming data and building real-
time analytics solutions.

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Monitor and optimize an analytics solution (30–35%)

Monitor Fabric items
Monitor Data Ingestion

• What it entails:

o Tracking the flow of data as it enters your Fabric environment.

o Ensuring data is being ingested from various sources (databases, files, APIs, etc.) as
expected.

o Identifying and resolving any issues that might hinder the smooth flow of data.

• Key considerations:

o Data Source Monitoring:

▪ Track the availability and health of data sources.

▪ Monitor data volume and velocity from each source.

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▪ Identify any data quality issues at the source (e.g., missing values, incorrect
data types).

o Ingestion Pipeline Monitoring:

▪ Monitor the performance of data ingestion pipelines (e.g., dataflows,

notebooks).

▪ Track pipeline execution times, success rates, and error logs.

▪ Identify and troubleshoot pipeline failures promptly.

o Data Volume and Velocity Monitoring:

▪ Track the amount of data being ingested over time.

▪ Monitor data ingestion rates to ensure they align with expected volumes.

▪ Identify any sudden spikes or drops in data volume.

o Data Quality Monitoring:

▪ Implement data quality checks during ingestion.

▪ Validate data against predefined rules and constraints.

▪ Identify and handle data anomalies (e.g., outliers, duplicates).

• Tools and Techniques:

o Fabric Monitoring Tools: Utilize built-in monitoring features within Fabric to track
pipeline executions, identify errors, and view performance metrics.

o Logging and Monitoring Services: Integrate with external logging and monitoring
services (e.g., Azure Monitor, Splunk) for advanced analysis and alerting.

o Data Profiling: Use data profiling tools to analyze data characteristics and identify
potential issues.

Monitor Data Transformation

• What it entails:

o Tracking the process of transforming raw data into a usable format for analysis.

o Ensuring data transformations are executed correctly and efficiently.

o Identifying and resolving any data quality or performance issues during

transformation.

• Key considerations:

o Transformation Pipeline Monitoring:

▪ Monitor the execution of data transformation pipelines (dataflows,

notebooks, Spark jobs).

▪ Track transformation performance (e.g., execution time, resource utilization).

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▪ Identify and troubleshoot transformation errors and exceptions.

o Data Quality Monitoring:

▪ Continuously monitor data quality after transformations.

▪ Validate transformed data against business rules and expectations.

▪ Identify and address any data inconsistencies or anomalies.

o Performance Monitoring:

▪ Monitor the performance of data transformation processes.

▪ Identify performance bottlenecks and optimize transformations for speed

and efficiency.

▪ Track resource utilization (e.g., CPU, memory) during transformations.

o Lineage Tracking:

▪ Track the origin and transformations of data elements.

▪ Understand the impact of changes in source data on downstream processes.

▪ Facilitate data impact analysis and troubleshooting.

• Tools and Techniques:

o Fabric Monitoring Tools: Utilize Fabric monitoring features to track transformation

pipelines, identify errors, and view performance metrics.

o Data Profiling: Perform data profiling on transformed data to assess its quality and
identify potential issues.

o Data Lineage Tools: Utilize lineage tracking capabilities to understand the flow of
data and the impact of transformations.

o Performance Tuning: Analyze performance metrics and identify opportunities to

optimize transformation logic.

In summary:

Both data ingestion and transformation monitoring are crucial for ensuring the reliability, accuracy,
and efficiency of your data pipelines. By implementing effective monitoring strategies, you can
proactively identify and resolve issues, improve data quality, and optimize the performance of your
data engineering solutions in Microsoft Fabric.

Monitor Semantic Model Refresh

• Within the Workspace:

o Refresh History: Directly within your Microsoft Fabric workspace, you can view the
refresh history of a specific semantic model. This provides details like:

▪ Start and End Times: Track the duration of each refresh.

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▪ Status: See if refreshes were successful (Completed), failed (Failed), or are

still in progress (Running).

▪ Refresh Type: Identify if a full or incremental refresh was performed.

o Monitor Hub: The Monitor hub offers a centralized view of all activities within your
workspace, including semantic model refreshes. You can filter and sort activities
based on various criteria, such as status, time, and more.

• Pipeline Output: If your semantic model refresh is triggered by a data pipeline:

o Activity Output: Within the pipeline activity itself, you can view detailed output for
the semantic model refresh, including the status of individual tables and partitions.

Configure Alerts

• Purpose: Alerts proactively notify you of critical events related to your semantic model
refreshes, such as failures or unexpected delays. This enables timely intervention and
minimizes potential data quality issues.

• Configuration:

o Alert Rules: Define specific conditions that trigger alerts. For example:

▪ Refresh Failure: Trigger an alert if a refresh fails for any reason.

▪ Refresh Timeout: Trigger an alert if a refresh takes longer than a specified

time limit.

▪ Data Quality Issues: Trigger an alert if certain data quality checks (e.g., row
count discrepancies) fail during the refresh.

o Notification Channels: Choose how you want to receive alerts:

▪ Email: Receive notifications directly to your email inbox.

▪ Azure Monitor: Integrate with Azure Monitor for more advanced alerting
and monitoring capabilities.

▪ Other Integrations: Explore other potential integrations with communication

platforms like Slack or Teams.

Key Considerations

• Monitoring Frequency: Determine how often you need to monitor refreshes based on the
criticality of the data and your business requirements.

• Alert Thresholds: Carefully set alert thresholds to avoid excessive notifications while
ensuring timely awareness of important issues.

• Root Cause Analysis: When an alert is triggered, investigate the root cause of the issue
promptly to ensure quick resolution and prevent future occurrences.

Benefits of Monitoring and Alerting

• Proactive Issue Detection: Identify and address potential problems before they significantly
impact data quality or downstream processes.

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• Improved Data Reliability: Ensure the accuracy and timeliness of your data by monitoring
the success of refreshes.

• Faster Troubleshooting: Quickly pinpoint the source of issues and resolve them efficiently.

• Enhanced Data Governance: Demonstrate a strong commitment to data quality and

compliance by implementing robust monitoring and alerting mechanisms.

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Identify and resolve errors

Identify and Resolve Pipeline Errors

• Pipelines in Microsoft Fabric orchestrate data movement and transformation tasks. They act
as the backbone of data engineering workflows, connecting various data sources and
destinations.

• Identifying Pipeline Errors:

o Monitoring: Utilize Fabric's monitoring tools to track pipeline executions, identify

failed activities, and review error logs.

o Activity Logs: Examine detailed logs for each activity within a pipeline to pinpoint
the specific source of the error.

o Visualizations: Leverage graphical representations of pipeline executions to quickly

identify bottlenecks and failed stages.

• Resolving Pipeline Errors:

o Data Quality Issues: Address data inconsistencies, missing values, or incorrect data
types that might cause pipeline failures.

o Connectivity Problems: Troubleshoot network connectivity issues, authentication

errors, or access control problems with data sources or destinations.

o Configuration Errors: Correct any misconfigurations in pipeline activities, such as

incorrect parameters, data mappings, or transformation logic.

o Resource Limitations: Increase resource allocations (e.g., compute power, memory)

if the pipeline is exceeding resource limits.

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o Debugging: Utilize debugging tools within the Fabric environment to step through
pipeline execution and identify the exact point of failure.

Identify and Resolve Dataflow Errors

• Dataflows are a core component of data transformation within Fabric. They enable data
cleansing, shaping, and enrichment using a visual interface.

• Identifying Dataflow Errors:

o Data Preview: Inspect the data at various stages of the dataflow to identify
unexpected results or data quality issues.

o Transformation Logic: Review the transformation logic within each dataflow step to
ensure it is correct and aligns with business requirements.

o Error Handling: Utilize built-in error handling mechanisms within dataflows, such as
data validation rules and exception handling, to identify and address potential issues.

• Resolving Dataflow Errors:

o Data Quality: Implement data cleansing techniques to handle missing values,

duplicates, and outliers.

o Transformation Logic: Correct any errors in the transformation logic, such as

incorrect formulas, joins, or aggregations.

o Data Type Issues: Ensure data types are correctly defined and handled throughout
the dataflow.

o Performance Tuning: Optimize dataflow performance by adjusting partitioning

strategies, using efficient data types, and minimizing data movement.

Key Considerations:

• Proactive Error Prevention: Implement robust data quality checks, thorough testing, and
version control to minimize the occurrence of errors.

• Root Cause Analysis: Investigate the root cause of errors to prevent recurrence and improve
the overall data quality and reliability of your data pipelines.

• Documentation: Maintain clear and concise documentation of data pipelines and dataflows,
including error handling procedures and troubleshooting steps.

Identify and Resolve Notebook Errors

• Notebooks in Microsoft Fabric are interactive environments where data scientists and
engineers can write and execute code, visualize data, and collaborate on projects. They
support various languages like Python, SQL, and Scala.

• Common Notebook Errors:

o Syntax Errors: Incorrect code structure, missing punctuation, or invalid keywords.

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o Runtime Errors: Issues that occur during code execution, such as:

▪ Type Errors: Mismatched data types in operations (e.g., trying to add a string
to an integer).

▪ Value Errors: Invalid input values (e.g., dividing by zero).

▪ Attribute Errors: Attempting to access an attribute that doesn't exist.

▪ ModuleNotFoundError: Missing or incorrectly installed libraries.

▪ Connection Errors: Problems connecting to data sources (databases, files,

etc.).

o Logical Errors: The code runs without crashing but produces incorrect results due to
flaws in the algorithm or logic.

• Resolving Notebook Errors:

o Thorough Error Messages: Carefully read error messages. They often provide
valuable clues about the issue and the line of code where it occurred.

o Debugging Tools: Utilize debugging tools within the notebook environment (e.g.,
breakpoints, step-by-step execution) to inspect variables and identify the source of
the problem.

o Code Review: Carefully review the code for any potential issues, such as incorrect
variable names, missing or extra parentheses, or logical inconsistencies.

o Testing: Run tests on smaller subsets of data to isolate and pinpoint the source of
errors.

o Community Support: Seek help from online communities, forums, or documentation

for the specific programming language or library you are using.

Identify and Resolve Eventhouse Errors

• Eventhouses in Microsoft Fabric are event-driven processing systems that react to real-time
events from various sources (e.g., IoT devices, streaming platforms). They can trigger actions
like running pipelines, sending notifications, or updating data stores.

• Common Eventhouse Errors:

o Event Processing Failures: Errors during event ingestion, transformation, or delivery.

o Trigger Configuration Issues: Incorrectly configured triggers that fail to activate or

fire as expected.

o Action Execution Errors: Problems executing the actions associated with the event,
such as pipeline failures or service unavailability.

o Event Schema Mismatches: Inconsistent data structures between the event source
and the Eventhouse, leading to data processing errors.

• Resolving Eventhouse Errors:

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o Monitoring and Logging: Monitor event processing metrics and logs for signs of
errors or performance issues.

o Event Stream Inspection: Examine the raw event data to identify any anomalies or
inconsistencies.

o Trigger Verification: Verify that the trigger conditions are met and that the trigger is
configured correctly.

o Action Troubleshooting: Investigate the specific actions associated with the event to
identify and resolve any underlying issues.

o Event Schema Validation: Ensure that the event schema is correctly defined and that
all incoming events adhere to the schema.

Key Considerations for Both Notebook and Eventhouse Errors:

• Error Handling: Implement robust error handling mechanisms within your code or workflows
to gracefully handle unexpected situations and prevent cascading failures.

• Logging and Monitoring: Implement comprehensive logging and monitoring to track events,
identify errors, and gain insights into system behavior.

• Testing and Validation: Thoroughly test your notebooks and eventhouses in different
scenarios to identify and address potential issues before deployment.

• Continuous Improvement: Regularly review and refine your error handling and
troubleshooting processes to improve the reliability and maintainability of your data
engineering solutions.

Identifying and Resolving Eventstream Errors

• Understanding Eventstreams:

o Eventstreams are a core component of Azure Data Factory pipelines. They enable
real-time data ingestion and processing by capturing and streaming data changes
from various sources.

o They are crucial for building data pipelines that react quickly to real-time events,
such as IoT sensor data, financial transactions, and social media feeds.

• Common Eventstream Errors:

o Connection Issues:

▪ Problems connecting to the source system (e.g., database, queue, event

hub).

▪ Network connectivity issues between the source and Data Factory.

▪ Incorrect credentials or authentication configurations.

o Schema Mismatches:

▪ Inconsistent data structures between the source and destination.

▪ Missing or extra fields in the incoming data stream.

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▪ Data type mismatches (e.g., integer vs. string).

o Data Integrity Issues:

▪ Corrupted or invalid data within the event stream.

▪ Duplicate or missing data records.

▪ Data transformation errors during processing.

o Throttling and Backpressure:

▪ The source system or downstream processing cannot keep up with the

incoming data volume.

▪ Data accumulates in the event stream, leading to delays and potential data
loss.

o Monitoring and Logging Issues:

▪ Insufficient monitoring and logging of eventstream activity.

▪ Difficulty in identifying the root cause of errors.

• Resolving Eventstream Errors:

o Thorough Monitoring:

▪ Utilize Data Factory monitoring tools to track eventstream activity, including

data volume, latency, and error rates.

▪ Set up alerts for critical errors and performance issues.

o Detailed Logging:

▪ Enable detailed logging for eventstreams to capture error messages, stack

traces, and other relevant information.

▪ Analyze log files to pinpoint the root cause of issues.

o Connection Verification:

▪ Test connections to source systems and ensure proper authentication.

▪ Verify network connectivity between the source and Data Factory.

o Schema Validation:

▪ Carefully review and validate the schema of the incoming data stream.

▪ Use data mapping tools to ensure proper data transformation and avoid
schema mismatches.

o Data Quality Checks:

▪ Implement data quality checks to identify and correct invalid or corrupted

data.

▪ Use data validation rules and transformations to ensure data integrity.

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o Resource Throttling:

▪ Adjust resource allocation (e.g., number of worker nodes) to handle

increased data volume.

▪ Implement backpressure mechanisms to prevent data overload.

o Error Handling and Retries:

▪ Implement robust error handling mechanisms, such as retries and dead-

letter queues.

▪ Configure appropriate retry policies to handle transient errors.

Identifying and Resolving T-SQL Errors

• Understanding T-SQL:

o T-SQL (Transact-SQL) is the standard language for accessing and manipulating data in
SQL Server and other relational databases.

o It is used extensively in Data Factory pipelines for data transformation, data loading,
and other data manipulation tasks.

• Common T-SQL Errors:

o Syntax Errors:

▪ Incorrect or missing keywords, punctuation, or operators.

▪ Incorrectly formatted SQL statements.

o Semantic Errors:

▪ Logical errors in the SQL code, such as incorrect joins, filters, or aggregations.

▪ References to non-existent tables, columns, or objects.

o Data Type Mismatches:

▪ Attempting to perform operations on incompatible data types.

▪ For example, trying to add a string to an integer.

o Access Violations:

▪ Insufficient permissions to access specific tables, views, or stored

procedures.

o Deadlocks:

▪ Two or more transactions are waiting for each other to release resources,
resulting in a deadlock.

• Resolving T-SQL Errors:

o Thorough Testing:

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▪ Test T-SQL code thoroughly in a development or test environment before

deploying it to production.

▪ Use unit tests and integration tests to validate the correctness of the code.

o Error Messages:

▪ Carefully examine the error messages generated by the SQL Server engine.

▪ Error messages often provide valuable clues about the source of the
problem.

o Debugging Tools:

▪ Utilize debugging tools to step through the T-SQL code line by line and
identify the source of errors.

o Code Reviews:

▪ Conduct code reviews to identify potential issues and improve code quality.

o Database Documentation:

▪ Maintain accurate and up-to-date database documentation, including table

definitions, stored procedures, and indexes.

o Performance Tuning:

▪ Optimize T-SQL code for performance using techniques such as indexing,

query optimization, and parameterization.

Key Considerations for DP-700:

• Data Factory Monitoring: Leverage Data Factory monitoring tools to track pipeline
executions, identify bottlenecks, and troubleshoot issues.

• Debugging Tools: Utilize debugging tools provided by Data Factory to step through pipeline
executions and inspect data flow.

• Best Practices: Adhere to best practices for data integration and data quality to minimize the
occurrence of errors.

• Continuous Improvement: Continuously monitor and analyze pipeline performance to

identify areas for improvement and proactively address potential issues.

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Optimize performance
Optimize a Lakehouse Table

• Data Partitioning:

o Purpose: Divide large tables into smaller, more manageable chunks based on a
specific column (e.g., date, region).

o Benefits:

▪ Improves query performance by allowing the system to scan only relevant

partitions.

▪ Reduces data scanned during queries, leading to faster execution times.

▪ Enables efficient data loading and unloading operations.

• Data Caching:

o Purpose: Store frequently accessed data in a faster storage tier (e.g., memory, SSD).

o Benefits:

▪ Significantly reduces query latency for hot data.

▪ Minimizes the need to read data from slower storage, improving overall
performance.

• Data Compression:

o Purpose: Reduce the physical size of data on disk.

o Benefits:

▪ Lower storage costs.

▪ Faster data transfer and loading times.

▪ Improved query performance by reducing the amount of data that needs to

be processed.

• Data Skipping:

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o Purpose: Allow the query engine to quickly identify and skip irrelevant data.

o Benefits:

▪ Improves query performance by reducing the amount of data scanned.

▪ Enables faster data processing for analytical workloads.

• Columnar Storage:

o Purpose: Store data in column-oriented format instead of row-oriented format.

o Benefits:

▪ Optimized for analytical queries that typically access a subset of columns.

▪ Improved query performance for columnar scans.

• Data Indexing:

o Purpose: Create indexes on frequently queried columns.

o Benefits:

▪ Significantly speeds up data retrieval for specific queries.

▪ Enables efficient filtering and sorting operations.

Optimize a Pipeline

• Data Profiling and Quality Checks:

o Purpose: Identify and address data quality issues early in the pipeline.

o Benefits:

▪ Prevent downstream errors and data inconsistencies.

▪ Improve data accuracy and reliability.

• Data Transformation Optimization:

o Use Efficient Algorithms: Choose algorithms that are optimized for the specific data
and processing requirements.

o Parallel Processing: Leverage distributed computing frameworks (e.g., Spark) to

process data in parallel.

o Data Caching: Cache intermediate results to avoid redundant computations.

• Pipeline Scheduling and Orchestration:

o Optimize Task Dependencies: Minimize dependencies between tasks to maximize

parallelism.

o Dynamic Workload Balancing: Adjust resource allocation based on workload

demands.

o Error Handling and Recovery: Implement robust error handling and recovery
mechanisms to minimize downtime.

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• Monitoring and Performance Tuning:

o Monitor Pipeline Performance: Track key metrics (e.g., execution time, resource
utilization) to identify bottlenecks.

o Analyze Performance Logs: Use log analysis to identify and troubleshoot

performance issues.

o Regularly Tune Pipeline Parameters: Adjust parameters (e.g., batch size,

concurrency) to optimize performance based on changing data volumes and
processing requirements.

Key Considerations for Both Table and Pipeline Optimization:

• Workload Characteristics: Understand the specific workloads that will be running on the
lakehouse and the pipeline. This includes query patterns, data volumes, and performance
requirements.

• Cost-Benefit Analysis: Evaluate the cost and benefits of each optimization technique. Choose
the techniques that provide the greatest performance improvement for the given
investment.

• Continuous Monitoring and Improvement: Regularly monitor the performance of the

lakehouse tables and pipelines. Continuously identify and implement optimizations to
improve performance and efficiency over time.

Optimize a Data Warehouse in Microsoft Fabric

• Data Modeling:

o Star Schema: Design your data warehouse around a central fact table and multiple
dimension tables. This normalized structure improves query performance and data
analysis.

o Snowflake Schema: A variation of the star schema, adding intermediate dimension

tables for further granularity and flexibility.

• Data Loading:

o Incremental Loads: Load only changed data instead of the entire dataset each time.
This significantly reduces loading time and processing overhead.

o Parallel Loading: Utilize parallel processing techniques to load data concurrently,

speeding up the overall loading process.

• Data Partitioning:

o Hash Partitioning: Distribute data evenly across multiple partitions based on a hash
of a column value.

o Range Partitioning: Partition data based on a range of values in a specific column.

• Data Compression:

o Columnar Compression: Compress data column-by-column, as most queries access

only a subset of columns.

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• Indexing:

o Create Indexes: Create appropriate indexes on frequently queried columns to speed

up data retrieval.

• Query Optimization:

o Analyze Query Plans: Use query execution plans to identify performance bottlenecks
and optimize queries accordingly.

o Parameterization: Use parameterized queries to improve query performance and

reduce compilation overhead.

o Caching: Utilize query caching to store frequently executed queries and their results,
reducing the need to re-execute them.

Optimize Event Streams and Event Houses in Microsoft Fabric

• Event Stream Configuration:

o Throughput: Adjust throughput settings based on the expected volume of incoming

events.

o Retention: Configure appropriate retention policies to balance data availability and

storage costs.

o Schema Validation: Enforce schema validation to ensure data quality and

consistency.

• Event Processing:

o Stream Processing: Use stream processing techniques like windowing, aggregation,

and filtering to process events in real-time.

o Change Data Capture (CDC): Capture changes to data sources and stream them to
the event stream for real-time processing.

• Event House Configuration:

o Indexing: Create indexes on frequently queried fields in the event house to improve
query performance.

o Data Partitioning: Partition data in the event house based on relevant attributes to
improve query performance and scalability.

o Data Retention: Configure appropriate retention policies to manage data storage

costs.

• Query Optimization:

o Use Kusto Query Language (KQL): Leverage KQL's powerful features, such as
filtering, aggregation, and joins, to efficiently query event data.

o Optimize Queries: Analyze query execution plans and optimize queries to minimize
resource utilization and improve performance.

Key Considerations:

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• Monitoring: Continuously monitor the performance of your data warehouse and event
streams to identify and address any issues.

• Resource Allocation: Allocate appropriate resources (compute, storage, memory) to ensure

optimal performance.

• Regular Maintenance: Perform regular maintenance tasks, such as data compaction,

statistics updates, and index rebuilds, to maintain optimal performance.

Optimize a Data Warehouse in Microsoft Fabric

• Data Modeling:

o Star Schema: Design your data warehouse around a central fact table and multiple
dimension tables. This normalized structure improves query performance and data
analysis.

o Snowflake Schema: A variation of the star schema, adding intermediate dimension

tables for further granularity and flexibility.

• Data Loading:

o Incremental Loads: Load only changed data instead of the entire dataset each time.
This significantly reduces loading time and processing overhead.

o Parallel Loading: Utilize parallel processing techniques to load data concurrently,

speeding up the overall loading process.

• Data Partitioning:

o Hash Partitioning: Distribute data evenly across multiple partitions based on a hash
of a column value.

o Range Partitioning: Partition data based on a range of values in a specific column.

• Data Compression:

o Columnar Compression: Compress data column-by-column, as most queries access

only a subset of columns.

• Indexing:

o Create Indexes: Create appropriate indexes on frequently queried columns to speed

up data retrieval.

• Query Optimization:

o Analyze Query Plans: Use query execution plans to identify performance bottlenecks
and optimize queries accordingly.

o Parameterization: Use parameterized queries to improve query performance and

reduce compilation overhead.

o Caching: Utilize query caching to store frequently executed queries and their results,
reducing the need to re-execute them.

Optimize Event Streams and Event Houses in Microsoft Fabric

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• Event Stream Configuration:

o Throughput: Adjust throughput settings based on the expected volume of incoming

events.

o Retention: Configure appropriate retention policies to balance data availability and

storage costs.

o Schema Validation: Enforce schema validation to ensure data quality and

consistency.

• Event Processing:

o Stream Processing: Use stream processing techniques like windowing, aggregation,

and filtering to process events in real-time.

o Change Data Capture (CDC): Capture changes to data sources and stream them to
the event stream for real-time processing.

• Event House Configuration:

o Indexing: Create indexes on frequently queried fields in the event house to improve
query performance.

o Data Partitioning: Partition data in the event house based on relevant attributes to
improve query performance and scalability.

o Data Retention: Configure appropriate retention policies to manage data storage

costs.

• Query Optimization:

o Use Kusto Query Language (KQL): Leverage KQL's powerful features, such as
filtering, aggregation, and joins, to efficiently query event data.

o Optimize Queries: Analyze query execution plans and optimize queries to minimize
resource utilization and improve performance.

Key Considerations:

• Monitoring: Continuously monitor the performance of your data warehouse and event
streams to identify and address any issues.

• Resource Allocation: Allocate appropriate resources (compute, storage, memory) to ensure

optimal performance.

• Regular Maintenance: Perform regular maintenance tasks, such as data compaction,

statistics updates, and index rebuilds, to maintain optimal performance.

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