Apache Spark vs STUMPY

Overview

Apache Spark

Stacks3.1K

Followers3.5K

Votes140

GitHub Stars42.2K

Forks28.9K

STUMPY

Stacks1

Followers9

Votes0

Apache Spark vs STUMPY: What are the differences?

Introduction:

Apache Spark and STUMPY are two popular tools in the field of data analysis and processing. While both serve similar purposes, there are some key differences that set them apart.

1. Scalability: One significant difference between Apache Spark and STUMPY lies in their scalability. Apache Spark is known for its ability to distribute computation across a cluster of machines, making it well-suited for handling large-scale data processing tasks. On the other hand, STUMPY is more focused on time series data analysis and may not offer the same level of scalability as Spark for general-purpose data processing.

2. Use case specialization: Another critical difference is in their use case specialization. Apache Spark is a general-purpose data processing framework that can be used for a wide range of applications, including ETL, machine learning, and streaming analytics. In contrast, STUMPY is specifically designed for time series data analysis and offers specialized algorithms and functionalities tailored for this particular domain.

3. Programming interface: Apache Spark provides a rich set of APIs in languages like Java, Scala, Python, and R, making it accessible to a broad range of developers. On the other hand, STUMPY primarily focuses on Python for its programming interface, which may limit the adoption for users who work with other programming languages.

4. Maturity and ecosystem: Apache Spark has been around for a longer time and has built a robust ecosystem around it, including various libraries, tools, and community support. This maturity gives Spark an edge in terms of stability, documentation, and resources available for users. STUMPY, being a relatively newer tool, may not have the same level of maturity and ecosystem as Spark.

5. Performance optimization: Apache Spark comes with built-in optimizations for improving performance, such as lazy evaluation, in-memory caching, and query optimizations. These features help Spark achieve efficient execution of data processing tasks. While STUMPY may have its own performance optimizations, they may not be as comprehensive or well-established as those in Spark.

6. Learning curve: Given its broad range of functionalities and capabilities, Apache Spark may have a steeper learning curve compared to STUMPY, which is more specialized and focused on time series data analysis. Users looking for a tool that is easier to pick up and start working with may find STUMPY to be more straightforward, while those needing a more powerful and versatile framework may opt for Apache Spark.

In Summary, Apache Spark and STUMPY differ in terms of scalability, use case specialization, programming interface, maturity and ecosystem, performance optimization, and learning curve, making them suitable for different types of data processing tasks and user requirements.

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Advice on Apache Spark, STUMPY

Nilesh

Technical Architect at Self Employed

Jul 8, 2020

Needs adviceon

Elasticsearch

Kafka

We have a Kafka topic having events of type A and type B. We need to perform an inner join on both type of events using some common field (primary-key). The joined events to be inserted in Elasticsearch.

In usual cases, type A and type B events (with same key) observed to be close upto 15 minutes. But in some cases they may be far from each other, lets say 6 hours. Sometimes event of either of the types never come.

In all cases, we should be able to find joined events instantly after they are joined and not-joined events within 15 minutes.

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Detailed Comparison

Apache Spark	STUMPY
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.	Efficiently computes something called the matrix profile, which can be used for a variety of time series data mining tasks.
Run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk;Write applications quickly in Java, Scala or Python;Combine SQL, streaming, and complex analytics;Spark runs on Hadoop, Mesos, standalone, or in the cloud. It can access diverse data sources including HDFS, Cassandra, HBase, S3	-
Statistics
GitHub Stars 42.2K	GitHub Stars -
GitHub Forks 28.9K	GitHub Forks -
Stacks 3.1K	Stacks 1
Followers 3.5K	Followers 9
Votes 140	Votes 0
Pros & Cons
Pros 61 Open-source 48 Fast and Flexible 8 One platform for every big data problem 8 Great for distributed SQL like applications 6 Easy to install and to use Cons 4 Speed	No community feedback yet
Integrations
No integrations available	Python

What are some alternatives to Apache Spark, STUMPY?

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.

Vertica

It provides a best-in-class, unified analytics platform that will forever be independent from underlying infrastructure.

Azure Synapse

It is an analytics service that brings together enterprise data warehousing and Big Data analytics. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources—at scale. It brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate BI and machine learning needs.

Related Comparisons

Apache Spark vs STUMPY: What are the differences?

Introduction:

Apache Spark and STUMPY are two popular tools in the field of data analysis and processing. While both serve similar purposes, there are some key differences that set them apart.

Apache Spark vs STUMPY

Overview