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Mara vs Pachyderm: 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, Pachyderm is detailed as "MapReduce without Hadoop. Analyze massive datasets with Docker". Pachyderm is an open source MapReduce engine that uses Docker containers for distributed computations.
Mara and Pachyderm can be primarily classified 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, Pachyderm provides the following key features:
- Git-like File System
- Dockerized MapReduce
- Microservice Architecture
Mara and Pachyderm are both open source tools. Pachyderm with 3.81K GitHub stars and 369 forks on GitHub appears to be more popular than Mara with 1.24K GitHub stars and 51 GitHub forks.
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Learn MorePros of Mara
Pros of Pachyderm
Pros of Mara
- Great developing experience1
- ETL Tool1
- UI focused on ETL development1
Pros of Pachyderm
- Containers3
- Versioning1
- Can run on GCP or AWS1
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Cons of Mara
Cons of Pachyderm
Cons of Mara
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Cons of Pachyderm
- Recently acquired by HPE, uncertain future.1
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What is Mara?
A lightweight ETL framework with a focus on transparency and complexity reduction.
What is Pachyderm?
Pachyderm is an open source MapReduce engine that uses Docker containers for distributed computations.
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What companies use Mara?
What companies use Pachyderm?
What companies use Mara?
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What tools integrate with Mara?
What tools integrate with Pachyderm?
What tools integrate with Mara?
No integrations found
What tools integrate with Pachyderm?
What are some alternatives to Mara and Pachyderm?
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.
Riot
Riot brings custom tags to all browsers. Think React + Polymer but with enjoyable syntax and a small learning curve.
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.
Splunk
It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.
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.