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Pros of dbt
Pros of Apache Spark
Pros of dbt
- Easy for SQL programmers to learn1
Pros of Apache Spark
- Open-source56
- Fast and Flexible45
- One platform for every big data problem7
- Easy to install and to use6
- Great for distributed SQL like applications6
- Works well for most Datascience usecases3
- Machine learning libratimery, Streaming in real2
- In memory Computation2
- Interactive Query0
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Cons of dbt
Cons of Apache Spark
Cons of dbt
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Cons of Apache Spark
- Speed0
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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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Jobs that mention dbt and Apache Spark as a desired skillset
What companies use dbt?
What companies use Apache Spark?
What companies use Apache Spark?
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What tools integrate with dbt?
What tools integrate with Apache Spark?
What tools integrate with dbt?
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.
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.
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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