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Apache Beam vs Freeter: What are the differences?
Apache Beam: A unified programming model. It implements batch and streaming data processing jobs that run on any execution engine. It executes pipelines on multiple execution environments; Freeter: Gather all the things you need for work in one place and have quick access to them. It is a productivity app that allows you to gather everything you need to work in one place and access them quickly and easily. It lets you work on as many projects as you want. Use the project dropdown to switch between them easily.
Apache Beam and Freeter belong to "Workflow Manager" category of the tech stack.
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Pros of Apache Beam
- Open-source5
- Cross-platform5
- Portable2
- Unified batch and stream processing2
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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.
What is Freeter?
It is a productivity app that allows you to gather everything you need to work in one place and access them quickly and easily. It lets you work on as many projects as you want. Use the project dropdown to switch between them easily.
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What are some alternatives to Apache Beam and Freeter?
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.