Exploring the Power of Apache Beam

A Unified Data Processing Solution

Spanish version here!

Beam comes with a number of I/O Connectors that read or write data to various external storage systems.

I recently came across this thought-provoking post by Shweta Jaiswal as she ventured into the world of Apache Beam. She raised a question that’s been echoing in the minds of many data engineers — “Why don’t we see more adoption of Apache Beam in the industry?” 🤔

After delving into the reasons behind this, I couldn’t help but share my perspective on the incredible benefits of Apache Beam, and I believe it’s a framework worth considering for your data engineering needs. Here are a few compelling reasons why one should choose Apache Beam:

1️⃣ Unified API for Batch & Streaming

Unlike many other parallel processing frameworks, Apache Beam offers a single, unified API for both batch and streaming data processing. Say goodbye to the hassle of juggling different APIs for different use cases.

Apache Beam simplifies your workflow and ensures you focus on your data, not the tools.

2️⃣ Abundance of Transformations

Apache Beam offers a rich set of pre-built transformations such as ParDo, GroupByKey, Map, Flatten, and more. Even better, you can create your custom transformations, giving you the flexibility to design data pipelines tailored to your unique requirements.

You’re not obliged to write boilerplate code but you can also do that if necessary.

3️⃣ Windowing & Watermarking

Apache Beam comes equipped with built-in support for event time processing and windowing, making it indispensable for handling data streams with time-based operations.

It streamlines complex time-based data processing, saving you valuable time and effort.

4️⃣ Seamless Integration

Apache Beam’s integration capabilities extend far and wide, connecting effortlessly with various tools and storage systems like Apache Kafka, MongoDB, Cassandra, GCP, and more.

This means you can leverage the power of your existing data ecosystem with ease.

5️⃣ Write Once, Run Anywhere

Apache Beam’s versatility allows you to write your code in your language of choice, be it Java, Python, or any other supported language. Plus, it supports various execution engines like Spark, Flink, DataFlow, AWS Kinesis, and more.

This flexibility empowers developers and organizations to choose the right stack for their specific needs without relearning a new language.

Beam has the concept of runners. On the left image you’ll see examples of runners. On the right you’ll see all that you need to learn in order to execute your code in any of those runners — just Apache Beam.

Let’s continue this engaging conversation in the comments. Also, I’ve been creating content about Apache Beam on Instagram, Threads, Twitter (X), and LinkedIn. You can learn more in those posts.

Downloadable Documents

• Apache Beam — The Future of Data Processing
• Apache Beam — Direct Runner
• Apache Beam — Dataflow Runner
• Apache Beam — Flink Runner

Further Reading

• Apache Beam — Running a Python job on a Spark cluster on Kubernetes
• LinkedIn’s journey from Spark & Samza to Apache Beam
• Introducing the Beam College
• Apache Beam at Lyft
• Apache Beam at Intuit
• Apache Beam at Yelp
• Apache Beam at LinkedIn
• Say Goodbye to the Lambda Architecture
• Moving Beyond Lambda: The Unified Apache Beam Model for Simplified Data Processing
• Apache Beam for Real-time ETL-Integration
• Apache Beam & Dataflow for Real-time Marketing Intelligence
• Apache Beam & Dataflow for Real-time ML & Generative AI
• Apache Beam, Dataflow ML and Gemma for real-time sentiment analysis

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