Pachyderm vs Vespa | What are the differences? | StackShare

Pachyderm vs Vespa

Overview

Pachyderm

Stacks24

Followers95

Votes5

Vespa

Stacks12

Followers29

Votes0

GitHub Stars6.5K

Forks675

Pachyderm vs Vespa: 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; Vespa: Store, search, rank and organize big data. Vespa is an engine for low-latency computation over large data sets. It stores and indexes your data such that queries, selection and processing over the data can be performed at serving time.

Pachyderm and Vespa belong to "Big Data Tools" category of the tech stack.

Pachyderm and Vespa are both open source tools. Pachyderm with 3.81K GitHub stars and 369 forks on GitHub appears to be more popular than Vespa with 2.85K GitHub stars and 339 GitHub forks.

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Detailed Comparison

Pachyderm	Vespa
Pachyderm is an open source MapReduce engine that uses Docker containers for distributed computations.	Vespa is an engine for low-latency computation over large data sets. It stores and indexes your data such that queries, selection and processing over the data can be performed at serving time.
Git-like File System;Dockerized MapReduce;Microservice Architecture;Deployed with CoreOS	-
Statistics
GitHub Stars -	GitHub Stars 6.5K
GitHub Forks -	GitHub Forks 675
Stacks 24	Stacks 12
Followers 95	Followers 29
Votes 5	Votes 0
Pros & Cons
Pros 3 Containers 1 Can run on GCP or AWS 1 Versioning Cons 1 Recently acquired by HPE, uncertain future.	No community feedback yet
Integrations
Docker Amazon EC2 Google Compute Engine Vagrant	Hadoop Pig

What are some alternatives to Pachyderm, Vespa?

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

Distributed SQL Query Engine for Big Data

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.

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

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 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.

Apache Kylin

Apache Kylin™ is an open source Distributed Analytics Engine designed to provide SQL interface and multi-dimensional analysis (OLAP) on Hadoop/Spark supporting extremely large datasets, originally contributed from eBay Inc.

Splunk

It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.

Apache Impala

Impala is a modern, open source, MPP SQL query engine for Apache Hadoop. Impala is shipped by Cloudera, MapR, and Amazon. With Impala, you can query data, whether stored in HDFS or Apache HBase – including SELECT, JOIN, and aggregate functions – in real time.