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dbt

473
440
+ 1
15
Redsmin

10
28
+ 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
  • 5
    Easy for SQL programmers to learn
  • 2
    CI/CD
  • 2
    Schedule Jobs
  • 2
    Reusable Macro
  • 2
    Faster Integrated Testing
  • 2
    Modularity, portability, CI/CD, and documentation
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    Cons of dbt
    Cons of Redsmin
    • 1
      Only limited to SQL
    • 1
      Cant do complex iterations , list comprehensions etc .
    • 1
      People will have have only sql skill set at the end
    • 1
      Very bad for people from learning perspective
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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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      What companies use dbt?
      What companies use Redsmin?
      See which teams inside your own company are using dbt or Redsmin.
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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.
      JavaScript
      JavaScript is most known as the scripting language for Web pages, but used in many non-browser environments as well such as node.js or Apache CouchDB. It is a prototype-based, multi-paradigm scripting language that is dynamic,and supports object-oriented, imperative, and functional programming styles.
      See all alternatives