AWS Glue vs Apache Beam

Overview

Apache Beam

Stacks183

Followers361

Votes14

AWS Glue

Stacks462

Followers819

Votes9

AWS Glue vs Apache Beam: What are the differences?

Introduction

In this article, we will discuss the key differences between AWS Glue and Apache Beam. Both AWS Glue and Apache Beam are frameworks used for data processing, but they have some distinct characteristics that set them apart.

Execution Environment: AWS Glue is a fully managed extract, transform, and load (ETL) service provided by Amazon Web Services. It provides a serverless environment for running ETL jobs on a scalable infrastructure. On the other hand, Apache Beam is an open-source unified model for data processing. It provides a portable execution framework that can run on various processing backends such as Apache Flink, Apache Spark, and Google Cloud Dataflow.
Language Support: AWS Glue supports Python and Scala as its programming languages. It provides pre-built transformations that can be used for data cleansing, enriching, and transforming operations. Apache Beam, on the other hand, supports multiple programming languages including Java, Python, and Go. It provides a rich set of transformations and connectors, allowing developers to write highly expressive pipelines.
Deployment Flexibility: AWS Glue is tightly integrated with Amazon Web Services ecosystem and can be easily deployed within the AWS infrastructure. It provides seamless integration with other AWS services such as Amazon S3, Amazon Redshift, and Amazon Athena. Apache Beam, being an open-source framework, can be deployed on various cloud providers as well as on-premises infrastructure. It offers a high level of flexibility in terms of the deployment environment.
Data Processing Model: AWS Glue uses a directed acyclic graph (DAG) model for defining and executing ETL jobs. It provides a visual interface for designing and monitoring workflows. Apache Beam, on the other hand, uses a unified batch and streaming model. It provides a consistent API for processing both bounded (batch) and unbounded (streaming) data, making it suitable for building real-time streaming pipelines.
Community and Ecosystem: AWS Glue is a managed service provided by Amazon Web Services, which has a large user base and a wide range of support resources. It is tightly integrated with other AWS services, providing a comprehensive ecosystem for data processing. Apache Beam is an open-source framework backed by the Apache Software Foundation. It has an active community that contributes to its development and provides support through forums, mailing lists, and documentation.
Pricing Model: AWS Glue pricing is based on the number of data processing units (DPUs) used, which determines the processing capacity of the jobs. It also includes additional costs for data catalog storage and data transfer. Apache Beam, being an open-source framework, does not have any licensing or usage costs. However, the costs may vary depending on the chosen processing backend and the infrastructure used for deployment.

In summary, the key differences between AWS Glue and Apache Beam lie in their execution environment, language support, deployment flexibility, data processing model, community and ecosystem, and pricing model. While AWS Glue provides a fully managed serverless environment with tight integration within the AWS ecosystem, Apache Beam offers a portable execution framework with support for multiple programming languages and a unified batch and streaming processing model.

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Advice on Apache Beam, AWS Glue

Vamshi

Data Engineer at Tata Consultancy Services

May 29, 2020

Needs adviceon

PySpark

Azure Data Factory

Databricks

I have to collect different data from multiple sources and store them in a single cloud location. Then perform cleaning and transforming using PySpark, and push the end results to other applications like reporting tools, etc. What would be the best solution? I can only think of Azure Data Factory + Databricks. Are there any alternatives to #AWS services + Databricks?

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Comments

datocrats-org

Jul 29, 2020

Needs adviceon

Amazon EC2

Tableau

PowerBI

We need to perform ETL from several databases into a data warehouse or data lake. We want to

keep raw and transformed data available to users to draft their own queries efficiently
give users the ability to give custom permissions and SSO
move between open-source on-premises development and cloud-based production environments

We want to use inexpensive Amazon EC2 instances only on medium-sized data set 16GB to 32GB feeding into Tableau Server or PowerBI for reporting and data analysis purposes.

319k views319k

Comments

Pavithra

Mar 12, 2020

Needs adviceon

Amazon S3

Amazon Athena

Amazon Redshift

Hi all,

Currently, we need to ingest the data from Amazon S3 to DB either Amazon Athena or Amazon Redshift. But the problem with the data is, it is in .PSV (pipe separated values) format and the size is also above 200 GB. The query performance of the timeout in Athena/Redshift is not up to the mark, too slow while compared to Google BigQuery. How would I optimize the performance and query result time? Can anyone please help me out?

522k views522k

Comments

Detailed Comparison

Apache Beam	AWS Glue
It implements batch and streaming data processing jobs that run on any execution engine. It executes pipelines on multiple execution environments.	A fully managed extract, transform, and load (ETL) service that makes it easy for customers to prepare and load their data for analytics.
-	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 183	Stacks 462
Followers 361	Followers 819
Votes 14	Votes 9
Pros & Cons
Pros 5 Cross-platform 5 Open-source 2 Unified batch and stream processing 2 Portable	Pros 9 Managed Hive Metastore
Integrations
No integrations available	Amazon Redshift Amazon S3 Amazon RDS Amazon Athena MySQL Microsoft SQL Server Amazon EMR Amazon Aurora Oracle Amazon RDS for PostgreSQL

What are some alternatives to Apache Beam, AWS Glue?

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.

Airflow

Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command lines utilities makes performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress and troubleshoot issues when needed.

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.

GitHub Actions

It makes it easy to automate all your software workflows, now with world-class CI/CD. Build, test, and deploy your code right from GitHub. Make code reviews, branch management, and issue triaging work the way you want.

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.

Related Comparisons

AWS Glue vs Apache Beam: What are the differences?

Introduction

Execution Environment: AWS Glue is a fully managed extract, transform, and load (ETL) service provided by Amazon Web Services. It provides a serverless environment for running ETL jobs on a scalable infrastructure. On the other hand, Apache Beam is an open-source unified model for data processing. It provides a portable execution framework that can run on various processing backends such as Apache Flink, Apache Spark, and Google Cloud Dataflow.
Language Support: AWS Glue supports Python and Scala as its programming languages. It provides pre-built transformations that can be used for data cleansing, enriching, and transforming operations. Apache Beam, on the other hand, supports multiple programming languages including Java, Python, and Go. It provides a rich set of transformations and connectors, allowing developers to write highly expressive pipelines.
Deployment Flexibility: AWS Glue is tightly integrated with Amazon Web Services ecosystem and can be easily deployed within the AWS infrastructure. It provides seamless integration with other AWS services such as Amazon S3, Amazon Redshift, and Amazon Athena. Apache Beam, being an open-source framework, can be deployed on various cloud providers as well as on-premises infrastructure. It offers a high level of flexibility in terms of the deployment environment.
Data Processing Model: AWS Glue uses a directed acyclic graph (DAG) model for defining and executing ETL jobs. It provides a visual interface for designing and monitoring workflows. Apache Beam, on the other hand, uses a unified batch and streaming model. It provides a consistent API for processing both bounded (batch) and unbounded (streaming) data, making it suitable for building real-time streaming pipelines.
Community and Ecosystem: AWS Glue is a managed service provided by Amazon Web Services, which has a large user base and a wide range of support resources. It is tightly integrated with other AWS services, providing a comprehensive ecosystem for data processing. Apache Beam is an open-source framework backed by the Apache Software Foundation. It has an active community that contributes to its development and provides support through forums, mailing lists, and documentation.
Pricing Model: AWS Glue pricing is based on the number of data processing units (DPUs) used, which determines the processing capacity of the jobs. It also includes additional costs for data catalog storage and data transfer. Apache Beam, being an open-source framework, does not have any licensing or usage costs. However, the costs may vary depending on the chosen processing backend and the infrastructure used for deployment.

AWS Glue vs Apache Beam

Overview

AWS Glue vs Apache Beam: What are the differences?