Alation vs Apache Spark

Overview

Apache Spark

Stacks3.1K

Followers3.5K

Votes140

GitHub Stars42.2K

Forks28.9K

Alation

Stacks14

Followers26

Votes0

Alation vs Apache Spark: What are the differences?

Introduction: Comparison between Alation and Apache Spark.

Data Catalog: Alation is primarily a data catalog tool that focuses on data governance, collaboration, and data stewardship, providing a centralized location for metadata management. On the other hand, Apache Spark is a distributed computing framework that is used for big data processing and analytics, enabling high-speed processing of large datasets.
Usage: While Alation is used for data governance, collaboration, and stewardship, Apache Spark is utilized for processing, analyzing, and transforming large volumes of data in a distributed computing environment. Alation serves as a data catalog and knowledge repository, whereas Apache Spark facilitates data processing tasks.
Technology Stack: Alation is built on modern web technologies and utilizes machine learning algorithms and natural language processing to enhance data discovery and understanding capabilities. In contrast, Apache Spark is written in Scala and offers APIs in various programming languages such as Java, Python, and R for data processing tasks.
Scalability: Alation is designed to scale with the data governance and collaboration needs of organizations, providing support for various data sources and integrations. Apache Spark excels in scalability for data processing tasks by enabling parallel processing and distributed computing across clusters of machines, allowing for horizontal scaling as data volumes increase.
Deployment: Alation is typically deployed on-premises or in the cloud, offering flexibility in deployment options for organizations. Apache Spark is often deployed in cloud environments such as AWS, Azure, or Google Cloud Platform, leveraging the scalability and elasticity of cloud infrastructure for processing large datasets.
Community Support: Alation has a strong focus on enterprise support and customer success, providing tailored solutions and services to meet the specific needs of organizations. Apache Spark, being an open-source project, benefits from a large and active community of developers contributing to its development, documentation, and support resources.

In Summary, Alation is a data catalog tool focused on data governance and collaboration, while Apache Spark is a distributed computing framework for processing large datasets with scalability and flexibility in deployment options.

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Advice on Apache Spark, Alation

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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Comments

Detailed Comparison

Apache Spark	Alation
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.	The leader in collaborative data cataloging, it empowers analysts & information stewards to search, query & collaborate for fast and accurate insights.
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	Data Catalog; Automatically indexes your data by source; Automatically gathers knowledge about your data
Statistics
GitHub Stars 42.2K	GitHub Stars -
GitHub Forks 28.9K	GitHub Forks -
Stacks 3.1K	Stacks 14
Followers 3.5K	Followers 26
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

What are some alternatives to Apache Spark, Alation?

Segment

Segment is a single hub for customer data. Collect your data in one place, then send it to more than 100 third-party tools, internal systems, or Amazon Redshift with the flip of a switch.

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.

Related Comparisons

Alation vs Apache Spark: What are the differences?

Introduction: Comparison between Alation and Apache Spark.

Data Catalog: Alation is primarily a data catalog tool that focuses on data governance, collaboration, and data stewardship, providing a centralized location for metadata management. On the other hand, Apache Spark is a distributed computing framework that is used for big data processing and analytics, enabling high-speed processing of large datasets.
Usage: While Alation is used for data governance, collaboration, and stewardship, Apache Spark is utilized for processing, analyzing, and transforming large volumes of data in a distributed computing environment. Alation serves as a data catalog and knowledge repository, whereas Apache Spark facilitates data processing tasks.
Technology Stack: Alation is built on modern web technologies and utilizes machine learning algorithms and natural language processing to enhance data discovery and understanding capabilities. In contrast, Apache Spark is written in Scala and offers APIs in various programming languages such as Java, Python, and R for data processing tasks.
Scalability: Alation is designed to scale with the data governance and collaboration needs of organizations, providing support for various data sources and integrations. Apache Spark excels in scalability for data processing tasks by enabling parallel processing and distributed computing across clusters of machines, allowing for horizontal scaling as data volumes increase.
Deployment: Alation is typically deployed on-premises or in the cloud, offering flexibility in deployment options for organizations. Apache Spark is often deployed in cloud environments such as AWS, Azure, or Google Cloud Platform, leveraging the scalability and elasticity of cloud infrastructure for processing large datasets.
Community Support: Alation has a strong focus on enterprise support and customer success, providing tailored solutions and services to meet the specific needs of organizations. Apache Spark, being an open-source project, benefits from a large and active community of developers contributing to its development, documentation, and support resources.

Alation vs Apache Spark

Overview