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Pros of dbt
Pros of Apache Spark
  • 1
    Easy for SQL programmers to learn
  • 56
    Open-source
  • 45
    Fast and Flexible
  • 7
    One platform for every big data problem
  • 6
    Easy to install and to use
  • 6
    Great for distributed SQL like applications
  • 3
    Works well for most Datascience usecases
  • 2
    Machine learning libratimery, Streaming in real
  • 2
    In memory Computation
  • 0
    Interactive Query

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Cons of dbt
Cons of Apache Spark
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    • 0
      Speed

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    - No public GitHub repository available -

    What is dbt?

    dbt - Documentation

    What is 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.

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    What companies use Apache Spark?

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    What tools integrate with Apache Spark?

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    Blog Posts

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    What are some alternatives to dbt and Apache Spark?
    act
    Rather than having to commit/push every time you want test out the changes you are making to your .github/workflows/ files (or for any changes to embedded GitHub actions), you can use this tool to run the actions locally. The environment variables and filesystem are all configured to match what GitHub provides.
    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.
    Looker
    We've built a unique data modeling language, connections to today's fastest analytical databases, and a service that you can deploy on any infrastructure, and explore on any device. Plus, we'll help you every step of the way.
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    Spring Data
    It makes it easy to use data access technologies, relational and non-relational databases, map-reduce frameworks, and cloud-based data services. This is an umbrella project which contains many subprojects that are specific to a given database.
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