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Apache Flink
Apache Flink

139
146
+ 1
11
Pachyderm
Pachyderm

7
12
+ 1
2
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Apache Flink vs Pachyderm: 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, 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.

Apache Flink and Pachyderm can be categorized as "Big Data" tools.

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, Pachyderm provides the following key features:

  • Git-like File System
  • Dockerized MapReduce
  • Microservice Architecture

Apache Flink and Pachyderm are both open source tools. Apache Flink with 9.35K GitHub stars and 5K forks on GitHub appears to be more popular than Pachyderm with 3.81K GitHub stars and 369 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 Pachyderm?

Pachyderm is an open source MapReduce engine that uses Docker containers for distributed computations.
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      What are some alternatives to Apache Flink and Pachyderm?
      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 Pachyderm
      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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