Need advice about which tool to choose?Ask the StackShare community!
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.
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.
You could also use AWS Lambda and use Cloudwatch event schedule if you know when the function should be triggered. The benefit is that you could use any language and use the respective database client.
But if you orchestrate ETLs then it makes sense to use Apache Airflow. This requires Python knowledge.
Though we have always built something custom, Apache airflow (https://airflow.apache.org/) stood out as a key contender/alternative when it comes to open sources. On the commercial offering, Amazon Redshift combined with Amazon Kinesis (for complex manipulations) is great for BI, though Redshift as such is expensive.
You may want to look into a Data Virtualization product called Conduit. It connects to disparate data sources in AWS, on prem, Azure, GCP, and exposes them as a single unified Spark SQL view to PowerBI (direct query) or Tableau. Allows auto query and caching policies to enhance query speeds and experience. Has a GPU query engine and optimized Spark for fallback. Can be deployed on your AWS VM or on prem, scales up and out. Sounds like the ideal solution to your needs.
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?
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?
you can use aws glue service to convert you pipe format data to parquet format , and thus you can achieve data compression . Now you should choose Redshift to copy your data as it is very huge. To manage your data, you should partition your data in S3 bucket and also divide your data across the redshift cluster
First of all you should make your choice upon Redshift or Athena based on your use case since they are two very diferent services - Redshift is an enterprise-grade MPP Data Warehouse while Athena is a SQL layer on top of S3 with limited performance. If performance is a key factor, users are going to execute unpredictable queries and direct and managing costs are not a problem I'd definitely go for Redshift. If performance is not so critical and queries will be predictable somewhat I'd go for Athena.
Once you select the technology you'll need to optimize your data in order to get the queries executed as fast as possible. In both cases you may need to adapt the data model to fit your queries better. In the case you go for Athena you'd also proabably need to change your file format to Parquet or Avro and review your partition strategy depending on your most frequent type of query. If you choose Redshift you'll need to ingest the data from your files into it and maybe carry out some tuning tasks for performance gain.
I'll recommend Redshift for now since it can address a wider range of use cases, but we could give you better advice if you described your use case in depth.
It depend of the nature of your data (structured or not?) and of course your queries (ad-hoc or predictible?). For example you can look at partitioning and columnar format to maximize MPP capabilities for both Athena and Redshift
you can change your PSV fomat data to parquet file format with AWS GLUE and then your query performance will be improved
Pros of Apache Beam
- Open-source5
- Cross-platform5
- Portable2
- Unified batch and stream processing2
Pros of AWS Glue
- Managed Hive Metastore9