๐Ÿท Apache Pig โ€“ Overview and Use Cases in the Hadoop Ecosystem


๐Ÿ” What is Apache Pig?

Apache Pig is a high-level platform for processing large datasets in the Hadoop ecosystem. It uses a scripting language called Pig Latin which simplifies the development of MapReduce programs.

  • Developed by Yahoo!
  • Runs on Hadoop, utilizing MapReduce as the execution engine
  • Designed for ETL, data preparation, and data transformation

๐Ÿ› ๏ธ Core Components

ComponentDescription
Pig LatinData flow language for expressing data transformation
Grunt ShellInteractive shell to run Pig Latin commands
Pig EngineCompiles Pig Latin into MapReduce jobs and executes

๐Ÿงพ Pig vs Hive

FeaturePigHive
LanguageProcedural (Pig Latin)Declarative (SQL-like: HiveQL)
Primary UseETL, pipeline developmentQuerying and reporting
Execution EngineMapReduce (can integrate Tez)MapReduce, Tez, Spark
Schema FlexibilityMore flexibleSchema-on-read with strict schema

โœ… Use Cases of Apache Pig in Hadoop Ecosystem

1. ETL (Extract, Transform, Load) Pipelines

Pig is widely used to build data pipelines where raw data is cleaned, transformed, and then loaded into structured formats or other systems.

Example:

raw_data = LOAD '/logs/data.csv' USING PigStorage(',') AS (id:int, name:chararray, status:chararray);
filtered = FILTER raw_data BY status == 'active';
STORE filtered INTO '/cleaned/active_users' USING PigStorage(',');

2. Log Processing

Common use case in tech companies like Yahoo, LinkedIn, etc., for parsing and aggregating server logs, ad click logs, etc.

  • Extract IPs, timestamps, and click info
  • Group and count clicks per user/session

3. Data Preparation for Machine Learning

Pig is used to:

  • Aggregate historical data
  • Normalize and flatten nested structures
  • Feed into ML tools (e.g., Mahout, Spark MLlib)

4. Ad-Hoc Data Analysis

For data engineers familiar with scripting, Pig offers fast prototyping capabilities for:

  • Filtering large datasets
  • Custom transformations
  • Aggregating metrics

5. Data Sampling and Validation

Pig can sample data subsets to validate data quality before ingestion into a data warehouse or model.


๐Ÿ”„ Pig Execution Flow

  1. Pig Latin script written
  2. Parser generates Logical Plan
  3. Optimizer applies transformations (e.g., projection pushdown)
  4. Physical Plan generated
  5. Translated into MapReduce jobs
  6. Results returned to HDFS or console

๐Ÿ”ง Integration with Hadoop Ecosystem

ToolRole
HDFSStorage layer for input/output
MapReduceUnderlying execution engine
HCatalogMetadata sharing with Hive and other tools
OozieWorkflow scheduler to run Pig scripts

๐Ÿง  Real-World Example

Use Case: Mobile Data Processing at Telecom

  • Logs collected from millions of mobile devices daily
  • Use Pig to:
    • Clean invalid records
    • Transform logs into device usage metrics
    • Aggregate session times by user, region, or time window
  • Output stored in Hive or pushed to a dashboard

โœ… When to Use Pig

  • You need quick data flow scripting without deep Java/Scala knowledge
  • Your workload is batch-oriented ETL
  • You want control over data processing flow
  • You want an alternative to verbose MapReduce code

๐Ÿšซ When Not to Use Pig

  • You need real-time processing (use Spark or Flink)
  • You prefer SQL and reporting-style querying (use Hive)
  • You need interactive analytics or BI integration

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