Getting Started with Apache Beam and Dataflow

Are you looking to process large amounts of data in a scalable and efficient manner? Do you want to build data pipelines that can handle real-time and batch processing? Look no further than Apache Beam and Google Cloud Dataflow!

Apache Beam is an open-source unified programming model for batch and streaming data processing. It provides a simple and flexible API that allows you to write data processing pipelines in your language of choice (Java, Python, Go, or others) and run them on a variety of execution engines, including Apache Flink, Apache Spark, and Google Cloud Dataflow.

Google Cloud Dataflow is a fully-managed service for executing Apache Beam pipelines on Google Cloud Platform. It provides a powerful and scalable infrastructure for processing data in real-time or batch mode, with automatic scaling, fault tolerance, and monitoring.

In this article, we will walk you through the basics of Apache Beam and Dataflow, and show you how to build your first data pipeline using these powerful tools.


Before we dive into the details of Apache Beam and Dataflow, let's make sure you have everything you need to get started.

If you don't have a Google Cloud Platform account yet, you can sign up for a free trial at Once you have a project set up, you can create a new Dataflow job and start processing data.

Installing Apache Beam

To get started with Apache Beam, you need to install the Beam SDK for your language of choice. You can find the latest releases of the SDK on the Apache Beam website at

For Java developers, you can use Maven or Gradle to manage your dependencies. Here's an example of how to add the Beam SDK to your Maven project:


For Python developers, you can use pip to install the Beam SDK:

pip install apache-beam

Once you have installed the SDK, you can start writing your first Beam pipeline.

Writing Your First Beam Pipeline

Let's start with a simple example of a Beam pipeline that reads a text file, counts the occurrences of each word, and writes the results to a file.

Java Example

import org.apache.beam.sdk.Pipeline;
import org.apache.beam.sdk.transforms.Count;
import org.apache.beam.sdk.transforms.Flatten;
import org.apache.beam.sdk.transforms.MapElements;
import org.apache.beam.sdk.values.KV;
import org.apache.beam.sdk.values.PCollection;
import org.apache.beam.sdk.values.TypeDescriptors;

public class WordCount {
  public static void main(String[] args) {
    Pipeline pipeline = Pipeline.create();

    PCollection<String> lines = pipeline.apply("input.txt"));

    PCollection<String> words = lines.apply(MapElements.into(TypeDescriptors.strings())
        .via((String line) -> line.split("[^\\p{L}]+"))).apply(Flatten.iterables());

    PCollection<KV<String, Long>> wordCounts = words.apply(Count.perElement());

        .via((KV<String, Long> wordCount) -> wordCount.getKey() + ": " + wordCount.getValue()))

This pipeline reads a text file named "input.txt", splits each line into words, counts the occurrences of each word, and writes the results to a file named "output.txt". The pipeline is executed using the method, which blocks until the pipeline completes.

Python Example

import apache_beam as beam
from import ReadFromText, WriteToText
from apache_beam.transforms import Count, Map, FlatMap
from apache_beam.typehints import KV

with beam.Pipeline() as pipeline:
    lines = pipeline | ReadFromText('input.txt')

    words = lines | FlatMap(lambda line: line.split())

    word_counts = words | Count.PerElement()

    formatted_counts = word_counts | Map(lambda word_count: f'{word_count[0]}: {word_count[1]}')

    formatted_counts | WriteToText('output.txt')

This pipeline does the same thing as the Java example, but in Python. It reads a text file named "input.txt", splits each line into words, counts the occurrences of each word, and writes the results to a file named "output.txt".

Running Your Beam Pipeline on Dataflow

Now that you have written your first Beam pipeline, let's run it on Dataflow. To do this, you need to create a Dataflow job and submit your pipeline to it.

Creating a Dataflow Job

To create a Dataflow job, go to the Google Cloud Console and navigate to the Dataflow section. Click on the "Create job from template" button and select the "WordCount" template.

Create Dataflow job

In the job configuration, specify the following parameters:

Configure Dataflow job

Once you have configured your job, click on the "Create" button to create your Dataflow job.

Submitting Your Pipeline to Dataflow

To submit your pipeline to Dataflow, you need to package your code and dependencies into a JAR file (for Java) or a ZIP file (for Python) and upload it to Google Cloud Storage.

For Java, you can use Maven or Gradle to build your JAR file:

mvn package

For Python, you can use the apache-beam[gcp] package to create a ZIP file:

pip install apache-beam[gcp]
python -m apache_beam.examples.wordcount --output gs://<BUCKET>/output.txt --runner DataflowRunner --project <PROJECT> --region <REGION> --setup_file ./

Once you have built your package, upload it to Google Cloud Storage and specify the path to your package in the Dataflow job configuration.

Upload package

Once you have uploaded your package, click on the "Run job" button to submit your pipeline to Dataflow.

Run job

Your pipeline will now be executed on Dataflow, and you can monitor its progress in the Google Cloud Console.


Congratulations! You have now learned how to write a simple Apache Beam pipeline and run it on Google Cloud Dataflow. With Apache Beam and Dataflow, you can process large amounts of data in a scalable and efficient manner, and build data pipelines that can handle real-time and batch processing.

To learn more about Apache Beam and Dataflow, check out the official documentation at and Happy coding!

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