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Unified, portable, and flexible, Apache Beam simplifies and streamlines large-scale data processing to help businesses generate important analytical insights. Learn Apache Beam with online courses delivered through edX.

What is Apache Beam?

Apache Beam is an open-source unified model for defining data processing pipelines. A data processing pipeline moves information from one database to another while changing its format and correcting any errors. Pipelines are then executed by back-ends like Apache Flink, Apache Spark, and Google Cloud Dataflow.1 This is an essential step that helps facilitate seamless data analysis.

Data and software engineers can use one or more language software development kit (SDK) to build powerful, versatile pipelines on Apache Beam. Python, Java, SQL, and Go are just a few programming languages that offer users variety and flexibility.2

Apache Beam focuses on defining two types of data parallel processing pipelines: batch and streaming. Batch-data processing collects large amounts of information over time, then performs deep analysis. This is convenient for companies that don't need immediate insights, and for use cases like payroll or billing.3 Streaming data processing analyzes data continuously and in real time, which can be useful for more sensitive, urgent uses like fraud detection.4

With Apache Beam, users can worry less about the logistics of parallel processing and rest assured that every task is executed properly.5 As a result, businesses can analyze large data sets and discover key insights in a more efficient manner.6

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Apache Beam tutorial course curriculum

An Apache Beam course may first explore the concept of big data frameworks and why they’re important. Companies often have to work with large data sets; a big data framework is a powerful tool that helps them process information quickly and at scale.

Then, learners can dive into Apache Beam by reviewing its basic concepts and functions. They may practice setting up data pipelines via different SDKs, whether it’s batch, streaming, or both. Some courses may also explain how to use data processing engines on Apache Spark or Flink, and get learners accustomed to real-world scenarios where Apache Beam is most prevalently used.

Jobs that use Apache Beam

Apache Beam is primarily used for data processing and analytics. Data analysts, data scientists, and even developers use Apache Beam to perform various data processing tasks. They build and maintain pipelines, extract important findings, and mitigate future performance issues.

If you’re looking to pursue a career in these fields, you may want to begin by building a strong educational foundation. edX offers a variety of online educational opportunities for learners interested in expanding their knowledge in these fields. You can earn a relevant bachelor’s degree as well as a master’s degree — or search for a boot camp that provides an intensive overview of specialized subjects.

How to use Apache Beam for data processing

If you want to learn Apache Beam, it’s important to thoroughly understand the basics of data processing. Data processing allows companies to collect information from various sources. Before running an analysis, companies must ensure that data is transformed and formatted in a way that helps them easily identify insights.7 Learners should be familiar with every step of this process. For instance, data professionals begin by installing a Beam SDK in their preferred programming language. They then set up a data pipeline and run it on an execution engine, which parses through the data and analyzes its patterns.

It may also be beneficial to learn about machine learning, anomaly detection, and different types of analytics to gain a bigger perspective of how effectively Apache Beam works.8 You can begin sharpening your data skills by pursuing a bachelor’s degree in data science or enrolling in a data analytics boot camp to put your learning into practice.

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    Frequently Asked Questions

    What is Apache Beam used for?

    The Apache Beam framework is used to define batch- and streaming-data processing pipelines, which determine how and when data is collected. It’s a unified programming model that makes it easier for data engineers to process large-scale data and discover important business insights.

    Why use Apache Beam?

    Apache Beam gives data engineers the freedom to choose their preferred execution engine, whether it’s Apache Flink, Apache Spark, Google Cloud Dataflow, or AWS KDA. This makes it a highly portable and flexible tool for their data analysis needs.

    What is the difference between Apache Beam and Spark?

    Although Apache Beam and Apache Spark are both used for large data sets, they still have a few differences. While Apache Beam is used for bulk and streaming data processing, Apache Spark provides an expansive library for SQL, bulk processing, graph processing, and machine comprehension.

    What is the difference between Apache Beam and Airflow?

    Though both are open-source and under the ASF, Apache Beam and Airflow have very different uses. While Apache Beam is a model for batch- and streaming-data processing, Airflow focuses on monitoring and managing workflow.

    What is the difference between Apache Beam and Kafka?

    Apache Beam is a unified programming model that defines data processing pipelines. Apache Kafka is an event streaming platform for data pipelines, streaming analytics, and data integration.

    What is the difference between Apache Beam and Flink?

    Apache Beam and Flink have many similarities, like strong data processing capabilities. However, unlike Flink, Apache Beam works with other execution engines like Google Cloud Dataflow or Apache Spark.

    1. About: Why Apache Beam? (2023). Beam Machine Learning. Retrieved January 13, 2023.

    2. About: Why Apache Beam? (2023). Beam Machine Learning. Retrieved January 13, 2023.

    3. Big Data 101: Dummy’s Guide to Batch vs. Streaming Data. (2022). Precisely. Retrieved January 13, 2023.

    4. What is Stream Processing? | The Complete Guide for 2023. (2022). Hevo Data. Retrieved January 13, 2023.

    5. Programming Model for Apache Beam. (2023). Google Cloud. Retrieved January 13, 2023.

    6. Data Pipelines: The What, Why, and How (2022). Confluent. Retrieved January 13, 2023.

    7. Data Processing: Steps, Types, and More. (2022). Express Analytics. Retrieved January 13, 2023.

    8. About: Why Apache Beam? (2023). Beam Machine Learning. Retrieved January 13, 2023.