AWS Glue vs Apache Atlas

Overview

AWS Glue

Stacks462

Followers819

Votes9

Apache Atlas

Stacks10

Followers12

Votes0

AWS Glue vs Apache Atlas: What are the differences?

Introduction

Here is a comparison between AWS Glue and Apache Atlas, highlighting their key differences.

Deployment and management: AWS Glue is a fully managed ETL (Extract, Transform, Load) service provided by Amazon Web Services. It simplifies the process of setting up, managing, and scaling ETL workflows. On the other hand, Apache Atlas is an open-source metadata management solution that can be deployed on-premises or on a cloud environment. It requires manual configuration and management.
Integration with other AWS services: AWS Glue seamlessly integrates with various AWS services, such as Amazon S3, Amazon Redshift, and Amazon Athena, enabling easy data ingestion, transformation, and analysis. Apache Atlas can also integrate with other services using its APIs, but the level of integration may vary and require additional configuration.
Data catalog capabilities: AWS Glue provides a built-in, searchable data catalog that is used to store metadata about data sources, schemas, and jobs. It enables easy discovery and exploration of data, simplifying the process of creating ETL jobs. Apache Atlas also offers a metadata catalog with similar capabilities, allowing users to manage and search metadata across different data sources.
Governance and data lineage: AWS Glue provides data lineage features that help track data transformation steps, enabling better governance and compliance. It allows users to understand the origin of data and verify the correctness of transformations. Apache Atlas also offers data lineage capabilities, providing a visual representation of data flows and relationships between different entities.
Community and support: AWS Glue is a proprietary service offered by Amazon and comes with dedicated support from AWS. It has a large user base and benefits from regular updates and enhancements from Amazon. Apache Atlas, being an open-source project, relies on community contributions for development and support. It may have a smaller user base and slower release cycles compared to AWS Glue.
Scalability and performance: AWS Glue is highly scalable and can automatically handle large volumes of data processing. It leverages Amazon Elastic MapReduce (EMR) clusters for parallel execution and offers optimized performance for large-scale data transformation. Apache Atlas scalability and performance depend on the infrastructure it is deployed on, requiring manual configuration and optimization.

In summary, AWS Glue is a fully managed service with seamless integration with other AWS services, a built-in data catalog, and dedicated support. Apache Atlas, on the other hand, is an open-source solution that requires manual deployment and management, offers integration capabilities with varying levels of complexity, and relies on community support for development and updates.

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Detailed Comparison

AWS Glue	Apache Atlas
A fully managed extract, transform, and load (ETL) service that makes it easy for customers to prepare and load their data for analytics.	The Apache Software Foundation /əˈpætʃi/ is an American non-profit corporation (classified as a 501 organization in the United States) to support Apache software projects, including the Apache HTTP Server.
Easy - AWS Glue automates much of the effort in building, maintaining, and running ETL jobs. AWS Glue crawls your data sources, identifies data formats, and suggests schemas and transformations. AWS Glue automatically generates the code to execute your data transformations and loading processes.; Integrated - AWS Glue is integrated across a wide range of AWS services.; Serverless - AWS Glue is serverless. There is no infrastructure to provision or manage. AWS Glue handles provisioning, configuration, and scaling of the resources required to run your ETL jobs on a fully managed, scale-out Apache Spark environment. You pay only for the resources used while your jobs are running.; Developer Friendly - AWS Glue generates ETL code that is customizable, reusable, and portable, using familiar technology - Scala, Python, and Apache Spark. You can also import custom readers, writers and transformations into your Glue ETL code. Since the code AWS Glue generates is based on open frameworks, there is no lock-in. You can use it anywhere.	-
Statistics
Stacks 462	Stacks 10
Followers 819	Followers 12
Votes 9	Votes 0
Pros & Cons
Pros 9 Managed Hive Metastore	No community feedback yet
Integrations
Amazon Redshift Amazon S3 Amazon RDS Amazon Athena MySQL Microsoft SQL Server Amazon EMR Amazon Aurora Oracle Amazon RDS for PostgreSQL	No integrations available

What are some alternatives to AWS Glue, Apache Atlas?

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.

Vertica

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

Related Comparisons

AWS Glue vs Apache Atlas: What are the differences?

Introduction

Here is a comparison between AWS Glue and Apache Atlas, highlighting their key differences.

Deployment and management: AWS Glue is a fully managed ETL (Extract, Transform, Load) service provided by Amazon Web Services. It simplifies the process of setting up, managing, and scaling ETL workflows. On the other hand, Apache Atlas is an open-source metadata management solution that can be deployed on-premises or on a cloud environment. It requires manual configuration and management.
Integration with other AWS services: AWS Glue seamlessly integrates with various AWS services, such as Amazon S3, Amazon Redshift, and Amazon Athena, enabling easy data ingestion, transformation, and analysis. Apache Atlas can also integrate with other services using its APIs, but the level of integration may vary and require additional configuration.
Data catalog capabilities: AWS Glue provides a built-in, searchable data catalog that is used to store metadata about data sources, schemas, and jobs. It enables easy discovery and exploration of data, simplifying the process of creating ETL jobs. Apache Atlas also offers a metadata catalog with similar capabilities, allowing users to manage and search metadata across different data sources.
Governance and data lineage: AWS Glue provides data lineage features that help track data transformation steps, enabling better governance and compliance. It allows users to understand the origin of data and verify the correctness of transformations. Apache Atlas also offers data lineage capabilities, providing a visual representation of data flows and relationships between different entities.
Community and support: AWS Glue is a proprietary service offered by Amazon and comes with dedicated support from AWS. It has a large user base and benefits from regular updates and enhancements from Amazon. Apache Atlas, being an open-source project, relies on community contributions for development and support. It may have a smaller user base and slower release cycles compared to AWS Glue.
Scalability and performance: AWS Glue is highly scalable and can automatically handle large volumes of data processing. It leverages Amazon Elastic MapReduce (EMR) clusters for parallel execution and offers optimized performance for large-scale data transformation. Apache Atlas scalability and performance depend on the infrastructure it is deployed on, requiring manual configuration and optimization.

AWS Glue vs Apache Atlas

Overview