Need advice about which tool to choose?Ask the StackShare community!

Pachyderm

20
65
+ 1
5
Pilosa

1
11
+ 1
0
Add tool

Pachyderm vs Pilosa: What are the differences?

Pachyderm: MapReduce without Hadoop. Analyze massive datasets with Docker. Pachyderm is an open source MapReduce engine that uses Docker containers for distributed computations; Pilosa: Open source, distributed bitmap index in Go. Pilosa is an open source, distributed bitmap index that dramatically accelerates queries across multiple, massive data sets.

Pachyderm and Pilosa can be primarily classified as "Big Data" tools.

Pachyderm and Pilosa are both open source tools. Pachyderm with 3.81K GitHub stars and 369 forks on GitHub appears to be more popular than Pilosa with 1.83K GitHub stars and 149 GitHub forks.

Get Advice from developers at your company using Private StackShare. Sign up for Private StackShare.
Learn More
Pros of Pachyderm
Pros of Pilosa
  • 3
    Containers
  • 1
    Versioning
  • 1
    Can run on GCP or AWS
    Be the first to leave a pro

    Sign up to add or upvote prosMake informed product decisions

    - No public GitHub repository available -

    What is Pachyderm?

    Pachyderm is an open source MapReduce engine that uses Docker containers for distributed computations.

    What is Pilosa?

    Pilosa is an open source, distributed bitmap index that dramatically accelerates queries across multiple, massive data sets.

    Need advice about which tool to choose?Ask the StackShare community!

    What companies use Pachyderm?
    What companies use Pilosa?
      No companies found
      See which teams inside your own company are using Pachyderm or Pilosa.
      Sign up for Private StackShareLearn More

      Sign up to get full access to all the companiesMake informed product decisions

      What tools integrate with Pachyderm?
      What tools integrate with Pilosa?
      What are some alternatives to Pachyderm and Pilosa?
      Hadoop
      The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and 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.
      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.
      Kafka
      Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.
      DVC
      It is an open-source Version Control System for data science and machine learning projects. It is designed to handle large files, data sets, machine learning models, and metrics as well as code.
      See all alternatives