Mara vs Amundsen

Overview

Mara

Stacks5

Followers21

Votes3

Amundsen

Stacks17

Followers42

Votes0

Mara vs Amundsen: What are the differences?

Developers describe Mara as "A lightweight ETL framework". A lightweight ETL framework with a focus on transparency and complexity reduction. On the other hand, Amundsen is detailed as "A metadata driven application for improving the productivity of data analysts, data scientists and engineers". It is a metadata driven application for improving the productivity of data analysts, data scientists and engineers when interacting with data.

Mara and Amundsen belong to "Big Data Tools" category of the tech stack.

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, Amundsen provides the following key features:

Datasets (Tables) schema and usage frequency/popularity
Users bookmark, owner, frequent user
Dashboard popularity, lineage to datasets

Mara and Amundsen are both open source tools. Mara with 1.46K GitHub stars and 70 forks on GitHub appears to be more popular than Amundsen with 889 GitHub stars and 163 GitHub forks.

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Detailed Comparison

Mara	Amundsen
A lightweight ETL framework with a focus on transparency and complexity reduction.	It is a metadata driven application for improving the productivity of data analysts, data scientists and engineers when interacting with data.
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.; GNU make semantics. Nodes depend on the completion of upstream nodes. No data dependencies or data flows.; No in-app data processing: command line tools as the main tool for interacting with databases and data.; Single machine pipeline execution based on Python's multiprocessing. No need for distributed task queues. Easy debugging and and output logging.; Cost based priority queues: nodes with higher cost (based on recorded run times) are run first.	Datasets (Tables) schema and usage frequency/popularity; Users bookmark, owner, frequent user; Dashboard popularity, lineage to datasets
Statistics
Stacks 5	Stacks 17
Followers 21	Followers 42
Votes 3	Votes 0
Pros & Cons
Pros 1 Great developing experience 1 ETL Tool 1 UI focused on ETL development	No community feedback yet
Integrations
No integrations available	Google BigQuery Snowflake AWS Glue Superset Apache Hive

What are some alternatives to Mara, Amundsen?

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.

Presto

Distributed SQL Query Engine for Big Data

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.

Apache Flink

Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.

lakeFS

It is an open-source data version control system for data lakes. It provides a “Git for data” platform enabling you to implement best practices from software engineering on your data lake, including branching and merging, CI/CD, and production-like dev/test environments.

Druid

Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.

Apache Kylin

Apache Kylin™ is an open source Distributed Analytics Engine designed to provide SQL interface and multi-dimensional analysis (OLAP) on Hadoop/Spark supporting extremely large datasets, originally contributed from eBay Inc.

Splunk

It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.

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.