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Apache Beam

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Wrangle

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Wrangle vs Apache Beam: What are the differences?

Wrangle: Workflow automation for growing teams. It is easy drag-and-drop process automation for busy teams. We improve efficiency for recurring team-to-team handoffs, like customer and employee on-boarding, sales quote approval, and contract management. We do this by documenting, automating, and tracking your core processes, keeping the workflow moving via Slack, email, and over 2000 other apps. Wrangle ensures faster-decision making, accountability, and better performance in any process, all with no coding necessary; 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.

Wrangle and Apache Beam can be categorized as "Workflow Manager" tools.

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Pros of Apache Beam
Pros of Wrangle
  • 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 Wrangle?

    It is easy drag-and-drop process automation for busy teams. We improve efficiency for recurring team-to-team handoffs, like customer and employee on-boarding, sales quote approval, and contract management. We do this by documenting, automating, and tracking your core processes, keeping the workflow moving via Slack, email, and over 2000 other apps. Wrangle ensures faster-decision making, accountability, and better performance in any process, all with no coding necessary.

    Need advice about which tool to choose?Ask the StackShare community!

    Jobs that mention Apache Beam and Wrangle as a desired skillset
    What companies use Apache Beam?
    What companies use Wrangle?
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      What tools integrate with Apache Beam?
      What tools integrate with Wrangle?
      What are some alternatives to Apache Beam and Wrangle?
      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