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  1. Stackups
  2. Application & Data
  3. In-Memory Databases
  4. In Memory Databases
  5. MemSQL vs Pachyderm

MemSQL vs Pachyderm

OverviewComparisonAlternatives

Overview

MemSQL
MemSQL
Stacks86
Followers184
Votes44
Pachyderm
Pachyderm
Stacks24
Followers95
Votes5

MemSQL vs Pachyderm: What are the differences?

Developers describe MemSQL as "Database for real-time transactions and analytics". MemSQL converges transactions and analytics for sub-second data processing and reporting. Real-time businesses can build robust applications on a simple and scalable infrastructure that complements and extends existing data pipelines. 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.

MemSQL belongs to "In-Memory Databases" category of the tech stack, while Pachyderm can be primarily classified under "Big Data Tools".

Some of the features offered by MemSQL are:

  • ANSI SQL Support
  • Fully-distributed Joins
  • Compiled Queries

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

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

Pachyderm is an open source tool with 3.81K GitHub stars and 369 GitHub forks. Here's a link to Pachyderm's open source repository on GitHub.

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Detailed Comparison

MemSQL
MemSQL
Pachyderm
Pachyderm

MemSQL converges transactions and analytics for sub-second data processing and reporting. Real-time businesses can build robust applications on a simple and scalable infrastructure that complements and extends existing data pipelines.

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

ANSI SQL Support;Fully-distributed Joins;Compiled Queries; ACID Compliance;In-Memory Tables;On-Disk Tables; Massively Parallel Execution;Lock Free Data Structures;JSON Support; High Availability; Online Backup and Restore;Online Replication
Git-like File System;Dockerized MapReduce;Microservice Architecture;Deployed with CoreOS
Statistics
Stacks
86
Stacks
24
Followers
184
Followers
95
Votes
44
Votes
5
Pros & Cons
Pros
  • 9
    Distributed
  • 5
    Realtime
  • 4
    JSON
  • 4
    Concurrent
  • 4
    Sql
Pros
  • 3
    Containers
  • 1
    Versioning
  • 1
    Can run on GCP or AWS
Cons
  • 1
    Recently acquired by HPE, uncertain future.
Integrations
Google Compute Engine
Google Compute Engine
MySQL
MySQL
QlikView
QlikView
Docker
Docker
Amazon EC2
Amazon EC2
Google Compute Engine
Google Compute Engine
Vagrant
Vagrant

What are some alternatives to MemSQL, Pachyderm?

Redis

Redis

Redis is an open source (BSD licensed), in-memory data structure store, used as a database, cache, and message broker. Redis provides data structures such as strings, hashes, lists, sets, sorted sets with range queries, bitmaps, hyperloglogs, geospatial indexes, and streams.

Apache Spark

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

Presto

Distributed SQL Query Engine for Big Data

Hazelcast

Hazelcast

With its various distributed data structures, distributed caching capabilities, elastic nature, memcache support, integration with Spring and Hibernate and more importantly with so many happy users, Hazelcast is feature-rich, enterprise-ready and developer-friendly in-memory data grid solution.

Amazon Athena

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.

Aerospike

Aerospike

Aerospike is an open-source, modern database built from the ground up to push the limits of flash storage, processors and networks. It was designed to operate with predictable low latency at high throughput with uncompromising reliability – both high availability and ACID guarantees.

Apache Ignite

Apache Ignite

It is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads delivering in-memory speeds at petabyte scale

Apache Flink

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

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

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.

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