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Apache Flink
Apache Flink

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149
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Mara
Mara

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+ 1
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Apache Flink vs Mara: What are the differences?

Developers describe Apache Flink as "Fast and reliable large-scale data processing engine". 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. On the other hand, Mara is detailed as "A lightweight ETL framework". A lightweight ETL framework with a focus on transparency and complexity reduction.

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

Some of the features offered by Apache Flink are:

  • Hybrid batch/streaming runtime that supports batch processing and data streaming programs.
  • Custom memory management to guarantee efficient, adaptive, and highly robust switching between in-memory and data processing out-of-core algorithms.
  • Flexible and expressive windowing semantics for data stream programs

On the other hand, Mara provides the following key features:

  • 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.

Apache Flink and Mara are both open source tools. Apache Flink with 9.35K GitHub stars and 5K forks on GitHub appears to be more popular than Mara with 1.24K GitHub stars and 51 GitHub forks.

What is 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.

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 Flink and Mara?
          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.
          Apache Storm
          Apache Storm is a free and open source distributed realtime computation system. Storm makes it easy to reliably process unbounded streams of data, doing for realtime processing what Hadoop did for batch processing. Storm has many use cases: realtime analytics, online machine learning, continuous computation, distributed RPC, ETL, and more. Storm is fast: a benchmark clocked it at over a million tuples processed per second per node. It is scalable, fault-tolerant, guarantees your data will be processed, and is easy to set up and operate.
          Beam
          A distributed knowledge graph store. Knowledge graphs are suitable for modeling data that is highly interconnected by many types of relationships, like encyclopedic information about the world.
          Apache Flume
          It is a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of log data. It has a simple and flexible architecture based on streaming data flows. It is robust and fault tolerant with tunable reliability mechanisms and many failover and recovery mechanisms. It uses a simple extensible data model that allows for online analytic application.
          Kafka
          Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.
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          How developers use Apache Flink and Mara
          Avatar of Coolfront Technologies
          Coolfront Technologies uses Apache FlinkApache Flink

          Used for analytics on streaming data.

          Avatar of rmetzger
          rmetzger uses Apache FlinkApache Flink

          Flink for stream data analytics

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