International Journal of Multidisciplinary Research and Growth Evaluation www.allmultidisciplinaryjournal.

com

International Journal of Multidisciplinary Research and Growth Evaluation

ISSN: 2582-7138
Received: 26-11-2020; Accepted: 27-12-2020
www.allmultidisciplinaryjournal.com
Volume 1; Issue 5; November-December 2020; Page No. 120-124

Oracle ETL Tools and AI Integration: New Data Management Approach

Kiran Veernapu
Independent Research, USA
Corresponding Author: Kiran Veernapu
DOI: https://doi.org/10.54660/.IJMRGE.2020.1.5-120-124

Abstract
Industries like Healthcare produce enormous amounts of data is managed and processed. Oracle’s ETL tools, like
data. Collecting, cleaning, and processing the data to make Oracle Data Integrator (ODI) and Oracle GoldenGate, are key
the data available for deep insights is a greater need in today’s in handling large data movements and transformations. As AI
competitive world. This process of data integration and data technologies advance, these tools are now including smart
management is called Extract, Transform, Load (ETL). There features to automate, optimize, and improve the ETL
are several products and tools in the market to accomplish processes. This paper looks into how AI can be added to
this task. The focus of this paper is on Oracle data Oracle ETL tools, discussing the advantages, challenges, and
management tools, the Oracle ETL tool set. Combining future possibilities of these integrations for better data
Artificial Intelligence (AI) with ETL tools is changing how processing, decision-making, and business analysis.

Keywords: Oracle ETL, Oracle data integrator, ODI, ETL, AI, Oracle GoldenGate, change data capture, data integration models

1. Introduction
The value of data capital in the United States is measured as $8 trillion in intangible assets including data, 84% of the market
value of S&P companies has intangible assets including data [1]. Major technology giants like Google, Amazon, Netflix, and
Facebook have all realized that data is not just a record of something, and data is a raw material that produces new kinds of
value. Generating return from data capital is not simply a matter of adding new technology to the enterprise. It’s a question of
integrating that technology with the existing enterprise architecture to create sustainable competitive advantage. Companies with
this kind of huge data capital use industry-proven tools that can provide data security and integrity through their cloud platforms.
Data integration is essential in today’s data environments, and ETL processes are vital for this. Tools like Oracle Data Integrator
(ODI) and GoldenGate, lead in automating data workflows for Big Data and they simplify the process of reshaping diverse data
for a variety of endpoint algorithms, analytics, and applications helping organizations manage large amounts of both structured
and unstructured data. These tools are built with prebuilt connectors to connect and extract data from diversified data source
systems, allowing data to be moved to targets while applying changes to maintain data quality and uniformity. As data and
business contexts become more complicated, AI can enhance ETL processes. Integrating AI into ETL tools allows for smarter
data processing, better automation, improved data quality control, predictive analytics, and enhanced decision-making.
This paper analyzes the joining of Oracle ETL tools with AI, investigating how AI technologies—like machine learning (ML),
natural language processing (NLP), and automation—can be used to enhance ETL processes based on Oracle systems.

2. Overview of Oracle ETL Tools

ETL is a set of database functions that helps extract data from the source systems that produce data. To avoid the processing of
huge volumes of data by connecting to the source system data is transferred to a dedicated database with a highly efficient model
and optimized for a better analytical perspective. The extracted data is cleansed transformed and loaded to the high-performance
database. Oracle product supports several features for this purpose, and the product is called Oracle Data Integrator (ODI) [2].
The technology and the products evolve to perform in a better way and the new product offerings are introduced based on the
market's emerging needs, products like Oracle goldenGate to address real-time data integration needs.

2.1 Oracle Data Integrator (ODI)

Oracle Data Integrator (ODI) is a robust data integration tool known for its high performance and flexible ETL processes. ODI
was introduced to the market in 2000 with its many limitations and performance issues. ODI is re-engineered with its database-

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agnostic tool features [2].

ODI product is designed to work with an E-LT architecture organized in a modular repository, which is accessed in a
to optimize the performance and scalability and also to lower client-server mode by a component called ODI studio and
the cost. It has the changed data capture (CDC) framework to execution agents that are written in Java. ODI studio comes
process changes at a faster pace [4]. ODI architecture is with the following components [3]:

Fig 1: Basic Architecture of Oracle GoldenGate

• Designer: Designer defines declarative rules for data ability to manage complex transformations, process batches,
transformation and integrity. and perform real-time data integration has made it a common
• Operator: Manages and monitors data integration choice in enterprise data setups.
process, and shows the execution log with errors, number
of rows processed, and execution statistics. 2.2 Oracle GoldenGate
• Topology: Defines physical and logical architecture of Oracle GoldenGate is another important tool that helps data
the infrastructure like registered servers, database integration with its efficient features like real-time data
schemas, and catalogs. replication, high performance, reliability, heterogeneity,
• Security: Manages user profiles, roles, and their conflict detection, data encryption, routing and
privileges. compressions, and differed apply which is the choice of
applying the change immediately or with latency [5]. Figure 2
ODI works with various data sources, making it suitable for demonstrates the basic architecture of Oracle GoldenGate
integration into both Oracle and non-Oracle settings. Its data integration and replication.

Fig 2: Basic Architecture of Oracle GoldenGate

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Oracle GoldenGate consists of the following components the data anomalies.

[6]
:
• Extract: The Extract is a capture process that captures 3.2 AI for Data Transformation
an insert, update, or delete operation performed on the • Predictive Transformation: Machine learning models
source database schema. There are primarily three types can identify the best transformation rules by analyzing
of extracts: local extracts, data pumps, and initial load data patterns. patterns of historical transformation,
extracts. allowing AI to recommend or automate transformation
• Data pump: Data pumps are the secondary extract rules based on new data, which cuts down on manual
processes that read from source trails written by the local design time [8].
extract and pump them over the network through • Automated Data Quality Checks: AI can find and fix
TCP/IP. data issues automatically, such as missing entries,
• Replicat: Replicat is the delivery process configured on outliers, or inconsistencies across datasets. These checks
the target system to read the trails and apply the changes can be integrated into the ETL transformation phase,
to the target system, the changes are applied to the target ensuring that the data is reliable before entering the
database in the same order as they are committed in the target system.
source database. • Natural Language Processing (NLP): NLP can make
• Trails: The extract process captures the data and writes it easier to interact with ETL tools. Users might use
the committed transactions sequentially into files called simple language to set transformation rules or filter
trails. These trails are sent across the network when they criteria, making it easier for those without technical
are written on the remote machine. backgrounds and boosting the flexibility of ETL
• Collector: The collector is a process that runs in the processes [9].
background on the target system, receives trails from
extracts, and writes them locally into remote trails for 3.3 AI for Data Loading
processing by the replicant. • Smart Loading and Scheduling: AI-based scheduling
• Manager: The manager is the main process that runs and can improve when and how data is loaded into systems.
controls all other GoldenGate processes and Machine learning can analyze past data loading habits
components. It maintains port numbers for and system performance to find the best times to load
communication, starts and stops extracts, contains data, avoiding slowdowns and boosting efficiency [10].
control parameters, and manages trail files. • Real-time Loading Optimization: In real-time ETL
• Checkpoints: Checkpoints are the way GoldenGate processes, AI can foresee changes in incoming data and
keeps track of which transaction it has replicated. adjust loading methods accordingly. AI can predict
Checkpoint facilitates data recovery and ensures data upcoming data trends, like seasonal increases, and
consistency. enhance data flow and speed [11].

GoldenGate moves transactional data between database 4. Integration of AI with Oracle ETL Tools
systems, ensuring data consistency across various Oracle has started to add AI and machine learning features to
environments. It is particularly useful for real-time its ETL tools, showing major gains in automation and
synchronization, database migration, and disaster recovery. performance:

3. The Role of AI in ETL Processes 4.1 Oracle Autonomous Data Integration

AI can be used in ETL processes to enhance stages of data Oracle's Autonomous Data Integration (ADI) service uses AI
integration, from extraction, through transformation, to to automate ETL workflows and simplify data integration.
loading. Below is how AI can benefit each phase: ADI offers an AI-powered data pipeline that automatically
3.1 AI for Data Extraction responds to changes in data and workload, making integration
• Smart Data Crawling: AI can navigate various data processes more efficient [12].
sources (both structured and unstructured) to With AI-powered metadata management and automatic
autonomously find relevant data for integration. This schema discovery, ADI aids organizations in minimizing the
minimizes manual effort and ensures vital information is complexity of data extraction and transformation by reducing
not missed [7]. The AI-powered automatic data mapping manual tasks and enabling AI to adjust workflows as data
suggestions are built into the ODI product to make the characteristics change.
development process easy and error-free. This helped
developers reduce the time and save money for the 4.2 Oracle Data Integrator and Machine Learning
organization. Oracle Data Integrator (ODI) can be boosted with machine
• Data Discovery: AI models, especially those in machine learning models for better data transformation and
learning, can find new patterns or changes in source data, integration. ODI’s link with Oracle Machine Learning
confirming that ETL operations work on accurate data (OML) allows for easy integration of machine learning
sets. The complex data relationships are handled with models into ETL tasks, automating functions like anomaly
ease to improve the data quality [7]. detection, classification, and regression within ODI [13].
• Automated Data Cleansing: With AI algorithms, the This enables companies to carry out regular data integration
data extraction process can automatically spot and while also using AI to enhance the data being processed,
eliminate duplicates, inconsistencies, or errors before making ODI a robust tool for both classic ETL and AI-
transformation, reducing the need for extensive manual powered data handling.
work. The machine learning algorithms detect and clean
4.3 AI Integration in Oracle GoldenGate

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Oracle GoldenGate can leverage AI, particularly in real-time transparency is crucial to avoid biases and keep data
data replication. Oracle GoldenGate integration with AI is transformations accurate.
just starting to boom. Oracle is working on real-time data
replication with AI-generated data like vector embeddings 7. Conclusion
significantly enhancing the integration of AI applications Combining AI with Oracle ETL tools offers promising ways
with real-time data pipelines. AI may spot trends in to improve data integration processes. By using AI to
transactional data to foresee changes and use predictive automate tasks, boost data quality, and enhance efficiency,
analytics to enhance data synchronization. AI can also businesses can reshape their ETL workflows to tackle the
oversee replication tasks to ensure potential issues are fixed growing challenges of today's data landscapes. Oracle's ETL
before they hinder performance [14]. tools, like Oracle Data Integrator and Oracle GoldenGate,
along with AI capabilities, lead to smarter, more independent,
5. Benefits of AI Integration in ETL and flexible data integration solutions. As AI progresses,
Combining AI with Oracle ETL tools presents multiple even stronger and adaptable ETL systems will likely arise,
benefits: helping organizations make better decisions and achieve
• Automation of Routine Tasks: AI can lessen the need business success.
for human input in ETL processes, automating the
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