๐ท Apache Pig โ Overview and Use Cases in the Hadoop Ecosystem
๐ 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
๐ ๏ธ Core Components
Component | Description |
---|---|
Pig Latin | Data flow language for expressing data transformation |
Grunt Shell | Interactive shell to run Pig Latin commands |
Pig Engine | Compiles Pig Latin into MapReduce jobs and executes |
๐งพ Pig vs Hive
Feature | Pig | Hive |
---|---|---|
Language | Procedural (Pig Latin) | Declarative (SQL-like: HiveQL) |
Primary Use | ETL, pipeline development | Querying and reporting |
Execution Engine | MapReduce (can integrate Tez) | MapReduce, Tez, Spark |
Schema Flexibility | More flexible | Schema-on-read with strict schema |
โ 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.
๐ Pig Execution Flow
- Pig Latin script written
- Parser generates Logical Plan
- Optimizer applies transformations (e.g., projection pushdown)
- Physical Plan generated
- Translated into MapReduce jobs
- Results returned to HDFS or console
๐ง Integration with Hadoop Ecosystem
Tool | Role |
---|---|
HDFS | Storage layer for input/output |
MapReduce | Underlying execution engine |
HCatalog | Metadata sharing with Hive and other tools |
Oozie | Workflow scheduler to run Pig scripts |
๐ง 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
โ 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
๐ซ 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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