twtech deep dive into AWS Glue.
Scope:
- The Concept: AWS Glue,
- Key Highlights,
- Key Components of AWS Glue,
- Glue Architecture,
- AWS Glue ETL Process (Step-by-Step),
- Key Features and Advanced Concepts,
- Pricing Model,
- Use Cases,
- Best Practices.
1. The Concept: AWS
Glue
- AWS Glue is a fully managed ETL (Extract, Transform, Load) service provided by AWS.
- AWS Glue allows twtech to prepare and transform data for analytics without managing servers.
- Glue integrates well with other AWS services like S3, Redshift, Athena, RDS, and many more.
Key Highlights:
- Serverless: No infrastructure to manage.
- Supports structured, semi-structured, and unstructured data.
- Can handle large-scale datasets.
- Provides a data catalog for metadata management.
2. Key Components of AWS Glue
a) Glue Data Catalog
- Centralized metadata repository.
- Stores metadata like table definitions, schema, and location of data.
- Acts as a central catalog for Athena, Redshift Spectrum, EMR, and Glue ETL jobs.
- Features:
- Database and table management.
- Schema versioning.
- Crawlers for automatic metadata extraction.
b) Glue Crawlers
- Automatically scan twtech data sources.
- Infer schema and store it in the Data Catalog.
- Can crawl S3, JDBC, or other supported data stores.
- Example: Scan an S3 bucket of JSON files → Create a table in Glue Data Catalog.
c) Glue Jobs
- ETL scripts that process and transform data.
- Supports Python (PySpark) or Scala.
- Can run on a serverless Spark environment managed by Glue.
Job Types:
- Spark ETL Jobs – large-scale transformations.
- Python Shell Jobs – lightweight scripts for small tasks.
- Streaming Jobs – process data in near real-time (with Glue Streaming).
d) Glue Triggers
- Schedule or run jobs based on events.
- Types:
- On-demand
– manually start jobs.
- Scheduled – cron-like scheduling.
- Event-based – triggered by S3 object creation or job completion.
e) Glue Workflows
- Orchestrates complex ETL pipelines.
- Can integrate multiple jobs, crawlers, and triggers.
3. Glue Architecture
- Data Sources: S3,
RDS, Redshift, DynamoDB, JDBC.
- Glue Crawlers: Extract metadata → populate Data Catalog.
- Glue Jobs: Transform and clean data using Spark.
- Data Targets: Store transformed data in S3, Redshift, or other warehouses.
- Triggers & Workflows: Automate ETL processes.
Architecture Note:
AWS Glue is serverless Spark under the hood, so scaling is handled automatically.
4. AWS Glue ETL Process (Step-by-Step):
- Crawl twtech data →
Create metadata tables.
- Author ETL Jobs:
- Transform data (clean,
enrich, join, filter).
- Use DynamicFrames (AWS
Glue abstraction) or Spark DataFrames.
- Run & Monitor Jobs:
- Check job runs, logs in CloudWatch.
- Store or Load Data:
- Write to S3, Redshift, or external DBs.
5. Key Features and Advanced Concepts
a) DynamicFrames
- Glue-specific abstraction over Spark DataFrames.
- Handles semi-structured data more flexibly.
- Built-in schema evolution and transformations.
b) Schema Evolution
- Glue supports changes in schema over time.
- DynamicFrames can handle missing fields or type changes automatically.
c) Glue Studio
- Visual interface for building ETL pipelines.
- Drag-and-drop transformations.
- Easier for non-coders.
d) Glue Elastic Views
- Combines data from multiple sources into materialized
views.
- Useful for analytics without manual ETL coding.
e) Glue DataBrew
- No-code visual data preparation.
- Clean, normalize, and enrich data.
- Generate transformation recipes.
6. Pricing Model
- Glue Jobs: Pay
per Data Processing Unit (DPU) hour.
- Glue Crawlers & Data Catalog: Based on number of objects crawled and tables stored.
- Serverless → no upfront cost; cost depends on job execution and scale.
7. Use Cases
- Data Lake ETL:
Transform raw S3 data to analytics-ready formats.
- Data Warehousing: Load and transform data for Redshift.
- Streaming ETL: Real-time data processing (Kinesis → S3/Redshift).
- Schema Discovery: Automatically catalog structured and semi-structured datasets.
- Cross-account/region ETL: Combine datasets across AWS environments.
8. Best Practices
- Use crawlers to automatically keep metadata updated.
- Use DynamicFrames for semi-structured data; DataFrames for structured.
- Partition S3 data for performance.
- Monitor jobs in CloudWatch.
- Keep ETL code modular and use Glue Workflows for orchestration.
No comments:
Post a Comment