What is Apache Beam?
It implements batch and streaming data processing jobs that run on any execution engine. It executes pipelines on multiple execution environments.
Apache Beam is a tool in the Workflow Manager category of a tech stack.
Who uses Apache Beam?
Companies
37 companies reportedly use Apache Beam in their tech stacks, including Shopify, BlaBlaCar, and XTRM-Data.
Developers
138 developers on StackShare have stated that they use Apache Beam.
Apache Beam Integrations
Pros of Apache Beam
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Decisions about Apache Beam
Here are some stack decisions, common use cases and reviews by companies and developers who chose Apache Beam in their tech stack.
Balaji Kasala
I have to build a data processing application with an Apache Beam stack and Apache Flink runner on an Amazon EMR cluster. I saw some instability with the process and EMR clusters that keep going down. Here, the Apache Beam application gets inputs from Kafka and sends the accumulative data streams to another Kafka topic. Any advice on how to make the process more stable?
Apache Beam Alternatives & Comparisons
What are some alternatives to Apache Beam?
Apache Spark
Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
Kafka Streams
It is a client library for building applications and microservices, where the input and output data are stored in Kafka clusters. It combines the simplicity of writing and deploying standard Java and Scala applications on the client side with the benefits of Kafka's server-side cluster technology.
Kafka
Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.
Airflow
Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command lines utilities makes performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress and troubleshoot issues when needed.
Google Cloud Dataflow
Google Cloud Dataflow is a unified programming model and a managed service for developing and executing a wide range of data processing patterns including ETL, batch computation, and continuous computation. Cloud Dataflow frees you from operational tasks like resource management and performance optimization.
