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dbt

300
298
+ 1
6
Redsmin

10
26
+ 1
0
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dbt vs Redsmin: What are the differences?

Developers describe dbt as "A command line tool that enables data analysts and engineers to transform data in their warehouse more effectively". dbt - Documentation. On the other hand, Redsmin is detailed as "All-in-one fully featured GUI for Redis". Redsmin is an all-in-one GUI for Redis, a tightly crafted developer oriented, online real-time monitoring and administration service for Redis.

dbt and Redsmin can be categorized as "Database" tools.

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Pros of dbt
Pros of Redsmin
  • 1
    Easy for SQL programmers to learn
  • 1
    Modularity, portability, CI/CD, and documentation
  • 1
    Faster Integrated Testing
  • 1
    Reusable Macro
  • 1
    Schedule Jobs
  • 1
    CI/CD
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    What is dbt?

    dbt is a transformation workflow that lets teams deploy analytics code following software engineering best practices like modularity, portability, CI/CD, and documentation. Now anyone who knows SQL can build production-grade data pipelines.

    What is Redsmin?

    Redsmin is an all-in-one GUI for Redis, a tightly crafted developer oriented, online real-time monitoring and administration service for Redis.

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    Jobs that mention dbt and Redsmin as a desired skillset
    CBRE
    United Kingdom of Great Britain and Northern Ireland England Feltham
    CBRE
    United States of America Nebraska Springfield
    CBRE
    Netherlands Noord-Holland Amsterdam
    CBRE
    United States of America Texas Dallas
    CBRE
    Netherlands Noord-Holland Aalsmeer
    CBRE
    United Kingdom of Great Britain and Northern Ireland England London
    What companies use dbt?
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    What tools integrate with dbt?
    What tools integrate with Redsmin?

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    What are some alternatives to dbt and Redsmin?
    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.
    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.
    Slick
    It is a modern database query and access library for Scala. It allows you to work with stored data almost as if you were using Scala collections while at the same time giving you full control over when a database access happens and which data is transferred.
    See all alternatives