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dbt vs TablePlus: 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, TablePlus is detailed as "Easily edit database data and structure". TablePlus is a native app which helps you easily edit database data and structure. TablePlus includes many security features to protect your database, including native libssh and TLS to encrypt your connection.
dbt and TablePlus belong to "Database Tools" category of the tech stack.
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Learn MorePros of dbt
Pros of TablePlus
Pros of dbt
- Easy for SQL programmers to learn5
- CI/CD2
- Schedule Jobs2
- Reusable Macro2
- Faster Integrated Testing2
- Modularity, portability, CI/CD, and documentation2
Pros of TablePlus
- Great tool, sleek UI, run fast and secure connections5
- Free3
- Perfect for develop use2
- Security1
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Cons of dbt
Cons of TablePlus
Cons of dbt
- Only limited to SQL1
- Cant do complex iterations , list comprehensions etc .1
- People will have have only sql skill set at the end1
- Very bad for people from learning perspective1
Cons of TablePlus
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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 TablePlus?
TablePlus is a native app which helps you easily edit database data and structure. TablePlus includes many security features to protect your database, including native libssh and TLS to encrypt your connection.
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What companies use dbt?
What companies use TablePlus?
What companies use TablePlus?
See which teams inside your own company are using dbt or TablePlus.
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What tools integrate with dbt?
What tools integrate with TablePlus?
What tools integrate with dbt?
What tools integrate with TablePlus?
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What are some alternatives to dbt and TablePlus?
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.