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Unit 5

The document outlines key frameworks in the Hadoop ecosystem for big data applications, including Pig, Hive, HBase, and IBM Big Data tools. Pig is a high-level platform for processing large datasets with an interactive shell and data flow language, while Hive provides SQL-like access to data and converts queries into MapReduce jobs. HBase is a NoSQL database designed for real-time read/write operations, and IBM offers a suite of tools for big data storage, analysis, and visualization.

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6 views4 pages

Unit 5

The document outlines key frameworks in the Hadoop ecosystem for big data applications, including Pig, Hive, HBase, and IBM Big Data tools. Pig is a high-level platform for processing large datasets with an interactive shell and data flow language, while Hive provides SQL-like access to data and converts queries into MapReduce jobs. HBase is a NoSQL database designed for real-time read/write operations, and IBM offers a suite of tools for big data storage, analysis, and visualization.

Uploaded by

ishantjaiswal2004
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© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
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Download as PDF, TXT or read online on Scribd
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Hadoop Ecosystem Frameworks:

Applications on Big Data

1. Pig
Introduction to Pig

• High-level platform for processing large data sets.


• Developed by Yahoo; runs on Hadoop.

Execution Modes

• Local Mode: Runs on a single machine.


• MapReduce Mode: Distributes processing across Hadoop cluster.

Comparison with Databases

• Schema-less vs. schema-based.


• Suitable for semi-structured data.

Grunt

• Interactive shell for Pig.


• Supports command execution and script testing.

Pig Latin

• Data flow language.


• Includes constructs like LOAD, FILTER, FOREACH, JOIN.

User Defined Functions (UDFs)

• Extend Pig’s capabilities using Java, Python, or other languages.

Data Processing Operators

• Filtering (FILTER), grouping (GROUP), joining (JOIN), sorting (ORDER BY), etc.
2. Hive
Apache Hive Architecture

• Built on Hadoop to provide SQL-like access to data.


• Converts HiveQL queries into MapReduce jobs.

Hive Components

• Hive Shell
• Hive Services (Driver, Compiler, Execution Engine)
• Metastore: Stores schema and metadata.

Comparison with Traditional Databases

• Schema-on-read vs schema-on-write.
• Optimized for batch processing, not OLTP.

HiveQL

• SQL-like language to write queries.

Tables and Querying

• Supports managed and external tables.


• Standard SQL queries for data retrieval.

User Defined Functions (UDFs)

• Customize operations like filters and transformations.

Advanced Query Features

• Sorting, aggregation, joins, subqueries.


• MapReduce integration.

3. HBase
HBase Concepts

• NoSQL database modeled after Google’s Bigtable.


• Column-oriented storage.
Clients and Examples

• Java API, REST, Thrift clients.

HBase vs RDBMS

• Schema-less, horizontal scalability, real-time read/write vs. structured schema and ACID.

Advanced Usage

• Time-stamped versioning, compression, sharding.

Schema Design

• Design based on access patterns, not normalization.

Advanced Indexing

• Custom secondary indexes via Phoenix or Coprocessors.

Zookeeper

• Coordinates distributed systems.


• Used for monitoring HBase clusters.

4. IBM Big Data Tools


IBM Big Data Strategy

• End-to-end platform for big data storage, analysis, and visualization.

Infosphere

• Platform for information integration and governance.

BigInsights

• Enterprise Hadoop solution with additional tooling.

Big Sheets

• Spreadsheet-like interface for analyzing big data.


Big SQL

• SQL engine to query Hadoop data using ANSI-compliant SQL.

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