Apache Beam vs Microsoft Power Automate

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Apache Beam

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360
+ 1
14
Microsoft Power Automate

151
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+ 1
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Apache Beam vs Microsoft Power Automate: 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; Microsoft Power Automate: Streamline repetitive tasks and paperless processes. It is a service that helps you create automated workflows between your favorite apps and services to synchronize files, get notifications, collect data and more.

Apache Beam and Microsoft Power Automate belong to "Workflow Manager" category of the tech stack.

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Pros of Apache Beam
Pros of Microsoft Power Automate
  • 5
    Open-source
  • 5
    Cross-platform
  • 2
    Portable
  • 2
    Unified batch and stream processing
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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 Microsoft Power Automate?

    Microsoft Power Automate is a cloud-first, comprehensive automation platform, powered by low-code and AI. Modernize business processes, integrate operations at scale, and maintain visibility and control with enterprise-grade governance.

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    Jobs that mention Apache Beam and Microsoft Power Automate as a desired skillset
    What companies use Apache Beam?
    What companies use Microsoft Power Automate?
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    What tools integrate with Apache Beam?
    What tools integrate with Microsoft Power Automate?

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    What are some alternatives to Apache Beam and Microsoft Power Automate?
    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.
    See all alternatives