How to backfill data in Kinesis and Glue jobs

 Backfilling data is a common requirement in data engineering pipelines. It refers to the process of loading historical data into a data pipeline or system after it has already been running in production. When working with Amazon Kinesis and AWS Glue, backfilling can be complex due to the streaming nature of Kinesis and the batch or ETL nature of Glue. However, with the right approach, you can design your pipelines to support smooth, efficient, and safe backfills.

In this blog, we’ll walk through strategies and best practices for backfilling data using Kinesis and Glue jobs.


Why Backfill Data?

Backfilling is needed when:

There’s a new transformation logic that should be applied to historical data.

Historical data needs to be reprocessed for analytics or compliance.

A bug in the original pipeline caused data loss or corruption.

You onboard a new destination (e.g., S3, Redshift) and need past data.

Kinesis is designed for real-time streaming, not historical processing.

Data in Kinesis is retained for a limited time (default: 24 hours, max: 7 days).

Glue streaming jobs consume real-time data and are not built for processing old datasets.

There's risk of duplicate processing if backfill and live streams aren’t managed properly.

To backfill correctly, you must separate historical batch data from live streaming data, and manage data consistency during the transition.


Strategy 1: Backfilling with Glue Batch Jobs

If your historical data is stored in S3 or another system (like an RDS snapshot or Redshift export), the simplest approach is to create a Glue batch job to process it.


Steps:

Extract historical data from the source system and load it into S3.

Create a new AWS Glue job (batch) that reads from this historical data source.

Apply the same transformations used in your live pipeline.

Write the results to the same or a different destination (e.g., S3 or Redshift).    

This approach avoids touching Kinesis entirely and is best when data already exists outside the stream.


Strategy 2: Backfilling into a Kinesis Stream

If you must send historical data through a Kinesis stream (e.g., to mimic live processing or support downstream consumers), follow this method:

Steps:

Extract and prepare historical data from your original source or archive.

Use the AWS Kinesis Data Streams PutRecord/PutRecords API or the Kinesis Producer Library (KPL) to push historical data into the stream.


python


import boto3

import json


kinesis = boto3.client('kinesis')


def send_to_kinesis(data, stream_name):

    for record in data:

        kinesis.put_record(

            StreamName=stream_name,

            Data=json.dumps(record),

            PartitionKey="backfill"

        )

Make sure your Glue streaming job is idempotent, so repeated processing doesn’t lead to duplicates or errors.


Optionally, tag or flag backfill records so they can be identified downstream.


Strategy 3: Dual Path with Merge

Some teams maintain two separate paths:

One for real-time streaming (Kinesis + Glue Streaming Job)

One for backfill (Glue Batch Job)

Data is eventually merged in a common storage layer (e.g., partitioned S3 bucket or a data warehouse). This ensures minimal impact on live streams and greater control.


Best Practices

Use partitions wisely: When writing to S3, use date-based partitions (year/month/day) to distinguish backfill vs live data.

Avoid duplicate ingestion: Include deduplication logic (e.g., hash keys or UUIDs) in your Glue jobs.

Log and monitor: Use CloudWatch logs and metrics to monitor the backfill process.

Time-based filtering: In Glue scripts, apply filters to process only specific timeframes.


Conclusion

Backfilling data in Kinesis and Glue requires a careful balance between real-time and historical processing. Depending on your architecture, you can choose to backfill through batch Glue jobs, re-ingest into Kinesis, or design a hybrid model. Whichever method you choose, maintaining data integrity, consistency, and traceability is essential to ensuring a successful and accurate backfill process.

Learn AWS Data Engineer with Data Analytics

Read More: AWS Glue with JDBC connections: Best practices
Read More: Creating dynamic dashboards in QuickSight from Athena
Read More: Using AWS CloudShell for quick data engineering tasks

Visit Quality Thought Training Institute in Hyderabad
Get Direction

Comments

Post a Comment

Popular posts from this blog

Tosca vs Selenium: Which One to Choose?

Choosing the right test automation tool can make or break the efficiency of your QA process. Two widely discussed tools in the test automation space are Tosca by Tricentis and Selenium, the open-source web automation framework. While both tools aim to automate testing and accelerate software delivery, they cater to different needs and teams. In this blog, we’ll provide a comprehensive comparison between Tosca and Selenium to help you decide which one suits your project best. Overview Selenium is an open-source framework primarily used for automating web applications. It supports multiple languages (Java, Python, C#, etc.) and browsers, making it ideal for custom, flexible automation solutions. Tosca, on the other hand, is a commercial test automation tool that supports model-based testing, API testing, mobile, desktop, and even SAP testing. It's designed for enterprise-grade testing with minimal scripting. Key Comparison: Tosca vs Selenium Feature          ...
Read more

Flask API Optimization: Using Content Delivery Networks (CDNs)

When building APIs with Flask, performance and speed are key metrics that directly affect user experience and application scalability. One powerful but often overlooked way to enhance these metrics is through the use of Content Delivery Networks (CDNs). While CDNs are traditionally associated with delivering static assets like images, CSS, and JavaScript, they can also play a significant role in optimizing your Flask-based APIs. What Is a CDN? A Content Delivery Network is a globally distributed network of proxy servers that cache content closer to end-users. Instead of every user hitting your central Flask server, a CDN enables them to access cached responses or assets from a nearby server, significantly reducing latency and improving response times. Benefits of Using CDNs with Flask APIs 1. Reduced Latency Latency is the time it takes for a request to travel from the client to your server and back. By caching static or semi-static responses at edge locations, CDNs reduce the distance...
Read more

Using ID and Name Locators in Selenium Python

When automating browser tasks with Selenium in Python, one of the first challenges is identifying and interacting with web elements. Selenium offers multiple strategies for locating elements, and two of the simplest and most effective methods are using the ID and Name locators. These locators are fast, reliable, and easy to use, making them ideal for beginners and experienced testers alike. In this blog post, we’ll dive into how ID and Name locators work in Selenium Python, provide examples, and share best practices to help you write clean and maintainable test scripts. What Are Locators in Selenium? Locators in Selenium are methods used to find elements on a web page. Selenium supports several locator types, including: ID Name Class Name Tag Name Link Text CSS Selector XPath Among these, ID and Name locators are often the most efficient because browsers optimize them for speed and accuracy. Setting Up Selenium with Python Before using locators, ensure you have Selenium installed: bash...
Read more