Using ETL checkpoints in Glue for resilience

In modern data pipelines, resilience and fault tolerance are critical. Enterprises rely on robust ETL (Extract, Transform, Load) processes to move and prepare data across platforms reliably. AWS Glue, a fully managed serverless ETL service, provides scalable data integration. One essential technique to make Glue jobs more resilient and fault-tolerant is by using ETL checkpoints.

In this blog, we’ll explore how to implement ETL checkpoints in AWS Glue, and how they improve resilience, especially in long-running or failure-prone jobs.


🧠 What Are ETL Checkpoints?

ETL checkpoints refer to intermediate save points in your ETL process. They allow you to persist processed data at various stages of the pipeline so that if a job fails, you can resume from the last successful step rather than restarting from scratch. In AWS Glue, you can implement checkpoints manually by writing intermediate datasets to Amazon S3 or a database before proceeding to the next transformation or load step.


🔍 Why Use Checkpoints in Glue?

Glue jobs may fail due to:

Data quality issues

Timeouts or memory limits

Schema mismatches

External service errors

Checkpoints provide partial recovery, reduce data reprocessing time, and support incremental loading. They're especially useful in long-running or multi-stage jobs.


✅ When to Use Checkpoints

Use ETL checkpoints when:

You’re processing large datasets in stages

You’re running Glue jobs on a schedule or in batches

Downstream transformations depend on successful prior steps

You need to debug complex ETL logic without rerunning the entire job


🛠️ How to Implement ETL Checkpoints in AWS Glue

Let’s walk through a sample scenario using Glue’s PySpark interface:

Step 1: Read and Clean Raw Data

python


raw_df = glueContext.create_dynamic_frame.from_catalog(

    database="sales_db",

    table_name="raw_transactions"

)

cleaned_df = raw_df.drop_fields(["unnecessary_column"])


Step 2: Save Intermediate Checkpoint

python


glueContext.write_dynamic_frame.from_options(

    frame=cleaned_df,

    connection_type="s3",

    connection_options={"path": "s3://my-etl-checkpoints/cleaned/"},

    format="parquet"

)

This saves your progress. If a failure happens later, you can resume from this checkpoint.


Step 3: Load from Checkpoint (Optional Restart)

python


checkpoint_df = glueContext.create_dynamic_frame.from_options(

    connection_type="s3",

    connection_options={"paths": ["s3://my-etl-checkpoints/cleaned/"]},

    format="parquet"

)


Step 4: Perform Transformation and Load to Target

python


transformed_df = ApplyMapping.apply(frame=checkpoint_df, mappings=[

    ("order_id", "string", "order_id", "string"),

    ("amount", "double", "order_total", "double")

])


glueContext.write_dynamic_frame.from_catalog(

    frame=transformed_df,

    database="analytics_db",

    table_name="final_sales"

)


📊 Best Practices

Use partitioned S3 paths (e.g., by date) for checkpoints to keep data organized.

Clean up outdated checkpoints using AWS Lambda or lifecycle rules.

Store metadata logs about checkpoint creation (e.g., timestamp, record count) in DynamoDB for tracking.

Enable job bookmarks if suitable — AWS Glue provides bookmarks for automatic tracking of previously processed data.


🧪 Limitations

Checkpoints consume storage — balance cost vs. recovery needs.

Manual checkpointing requires additional logic.

AWS Glue job bookmarks can conflict with manual checkpoints if not managed properly.


🚀 Conclusion

ETL checkpoints in AWS Glue are a practical strategy to improve the resilience and efficiency of data pipelines. By saving intermediate data stages, you reduce risk, avoid unnecessary reprocessing, and speed up recovery after failures. Whether you’re handling large datasets or mission-critical ETL workflows, incorporating checkpoints is a smart move for robust data engineering on AWS.


Learn AWS Data Engineer Training

Read More: Deploying Spark applications using AWS EMR Serverless

Read More: Applying data masking in Redshift views

Read More: Building KPI dashboards using Redshift and QuickSight

Visit IHUB Training Institute Hyderabad
Get Direction

Comments

Post a Comment

Popular posts from this blog

How to Use Tosca's Test Configuration Parameters

Tosca by Tricentis is a powerful model-based test automation tool widely used in enterprise environments for end-to-end testing. One of its most useful features is Test Configuration Parameters (TCPs), which provide flexibility and control in managing test execution. TCPs enable testers to create reusable test cases by passing dynamic values without hardcoding them. This significantly enhances test maintenance, scalability, and efficiency. In this blog, we’ll explore what Test Configuration Parameters are, how to create and use them, and the best practices to follow when working with TCPs in Tosca. What Are Test Configuration Parameters? Test Configuration Parameters are custom-defined key-value pairs that can be associated with test cases, test case folders, or execution lists in Tosca. They help manage configuration data such as environment URLs, browser types, credentials, or any other variable data that may differ across test runs. By using TCPs, you can avoid duplicating test case...
Read more

Top 5 UX Portfolios You Should Learn From

A great UX portfolio is more than a collection of beautiful screens — it’s a curated story of how you think, solve problems, and create meaningful user experiences. Whether you’re a student, a junior designer, or a seasoned UX professional, learning from the best can elevate your own portfolio to the next level. In this blog, we’ll explore five outstanding UX portfolios that stand out for their clarity, storytelling, and user-centered approach — and share what you can learn from each. 1. Elizabeth Lin https://elizabethlin.com Elizabeth Lin’s portfolio is a masterclass in clean, focused storytelling. She keeps things simple yet powerful. Her case studies are well-structured, balancing visuals with thoughtful explanations of her design process. Each project begins with a clear problem statement and ends with measurable outcomes — a format that hiring managers love. What to Learn: Clear UX storytelling Prioritizing impact over visuals Effective use of typography and whitespace 2. Simon Pa...
Read more

Using Playwright with Electron-Based Applications

Electron has become a popular framework for building cross-platform desktop applications using web technologies like JavaScript, HTML, and CSS. Applications such as Visual Studio Code, Slack, and Discord are built on Electron. As these apps grow in complexity, the need for automated end-to-end testing becomes essential. Playwright, a powerful end-to-end testing framework developed by Microsoft, is a great fit — even for Electron apps. In this blog, we’ll explore how to use Playwright to automate testing for Electron-based applications, including setup, code examples, and best practices. Why Use Playwright for Electron? While Electron apps run on desktops, they’re essentially Chromium-based browsers running inside a native shell. Playwright is built to automate Chromium, Firefox, and WebKit — making it ideal for interacting with the web contents of Electron apps. Key benefits of using Playwright for Electron: Full browser control including Chromium (used by Electron) Built-in support fo...
Read more