Pig vs STUMPY Comparison | StackShare | StackShare

Pig vs STUMPY

Overview

Pig

Stacks59

Followers111

Votes5

GitHub Stars686

Forks447

STUMPY

Stacks1

Followers9

Votes0

Pig vs STUMPY: What are the differences?

Developers describe Pig as "Platform for analyzing large data sets". Pig is a dataflow programming environment for processing very large files. Pig's language is called Pig Latin. A Pig Latin program consists of a directed acyclic graph where each node represents an operation that transforms data Operations are of two flavors: (1) relational-algebra style operations such as join, filter, project; (2) functional-programming style operators such as map, reduce. . On the other hand, STUMPY is detailed as "A Python library for time series data mining tasks". Efficiently computes something called the matrix profile, which can be used for a variety of time series data mining tasks.

Pig and STUMPY can be categorized as "Big Data" tools.

Pig and STUMPY are both open source tools. STUMPY with 661 GitHub stars and 51 forks on GitHub appears to be more popular than Pig with 583 GitHub stars and 449 GitHub forks.

Detailed Comparison

Pig	STUMPY
Pig is a dataflow programming environment for processing very large files. Pig's language is called Pig Latin. A Pig Latin program consists of a directed acyclic graph where each node represents an operation that transforms data. Operations are of two flavors: (1) relational-algebra style operations such as join, filter, project; (2) functional-programming style operators such as map, reduce.	Efficiently computes something called the matrix profile, which can be used for a variety of time series data mining tasks.
Statistics
GitHub Stars 686	GitHub Stars -
GitHub Forks 447	GitHub Forks -
Stacks 59	Stacks 1
Followers 111	Followers 9
Votes 5	Votes 0
Pros & Cons
Pros 2 Finer-grained control on parallelization 1 Open-source 1 Join optimizations for highly skewed data 1 Proven at Petabyte scale	No community feedback yet
Integrations
No integrations available	Python

What are some alternatives to Pig, STUMPY?

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.