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Apache Impala
Apache Impala

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Mara
Mara

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Mara vs Apache Impala: What are the differences?

What is Mara? A lightweight ETL framework. A lightweight ETL framework with a focus on transparency and complexity reduction.

What is Apache Impala? Real-time Query for Hadoop. Impala is a modern, open source, MPP SQL query engine for Apache Hadoop. Impala is shipped by Cloudera, MapR, and Amazon. With Impala, you can query data, whether stored in HDFS or Apache HBase – including SELECT, JOIN, and aggregate functions – in real time.

Mara and Apache Impala can be categorized as "Big Data" tools.

Some of the features offered by Mara are:

  • Data integration pipelines as code: pipelines, tasks and commands are created using declarative Python code.
  • PostgreSQL as a data processing engine.
  • Extensive web ui. The web browser as the main tool for inspecting, running and debugging pipelines.

On the other hand, Apache Impala provides the following key features:

  • Do BI-style Queries on Hadoop
  • Unify Your Infrastructure
  • Implement Quickly

Mara and Apache Impala are both open source tools. It seems that Apache Impala with 2.19K GitHub stars and 825 forks on GitHub has more adoption than Mara with 1.27K GitHub stars and 54 GitHub forks.

What is Apache Impala?

Impala is a modern, open source, MPP SQL query engine for Apache Hadoop. Impala is shipped by Cloudera, MapR, and Amazon. With Impala, you can query data, whether stored in HDFS or Apache HBase – including SELECT, JOIN, and aggregate functions – in real time.

What is Mara?

A lightweight ETL framework with a focus on transparency and complexity reduction.
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          What are some alternatives to Apache Impala and Mara?
          Presto
          Presto is an open source distributed SQL query engine for running interactive analytic queries against data sources of all sizes ranging from gigabytes to petabytes.
          Apache Drill
          Apache Drill is a distributed MPP query layer that supports SQL and alternative query languages against NoSQL and Hadoop data storage systems. It was inspired in part by Google's Dremel.
          Apache Hive
          Hive facilitates reading, writing, and managing large datasets residing in distributed storage using SQL. Structure can be projected onto data already in storage.
          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.
          Amazon Athena
          Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.
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