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AWS Glue vs Amazon Athena: What are the differences?
AWS Glue and Amazon Athena are two powerful data analysis and processing services provided by Amazon Web Services (AWS). Let's explore the key differences between them.
Data Processing: AWS Glue is primarily used for Extract, Transform, Load (ETL) processes, where it provides a fully managed and serverless environment for data transformation and integration. It can handle large volumes of data and is highly scalable. On the other hand, Amazon Athena is a query service that allows you to run SQL queries directly on data stored in Amazon S3, without the need for any setup or infrastructure management. Athena is best suited for ad-hoc querying and analysis of data.
Data Catalog: AWS Glue provides a centralized metadata repository called the AWS Glue Data Catalog. This catalog can store and manage metadata information about various data sources, tables, and their schemas. It also supports automatic schema discovery and data cataloging. In contrast, Amazon Athena does not have its own data catalog. It relies on the AWS Glue Data Catalog or an external Hive Metastore for storing and managing metadata.
Data Formats and Compression: AWS Glue supports a wide range of data formats for ingestion, transformation, and processing, including CSV, JSON, Avro, etc. It also allows you to perform compression and decompression of data using codecs like Gzip, Snappy, etc. On the other hand, Amazon Athena supports a limited set of data formats, primarily CSV, JSON, Parquet, and ORC. It also supports columnar compression using Snappy and Zlib.
Data Partitioning: AWS Glue provides built-in support for data partitioning, which allows you to organize your data in multiple directories based on certain columns. This can significantly improve query performance, especially when dealing with large datasets. Amazon Athena, on the other hand, does not have native support for data partitioning. However, you can still use the underlying directory structure in Amazon S3 to mimic the partitioning behavior.
Pricing Model: AWS Glue has a pay-as-you-go pricing model, where you are charged based on the number of Data Processing Units (DPUs) consumed during ETL jobs. DPUs represent the processing power and memory allocated to a job. On the other hand, Amazon Athena follows a pay-per-query pricing model, where you are charged based on the amount of data scanned by each query. This can be cost-effective for sporadic or ad-hoc queries.
Integration with Other AWS Services: AWS Glue seamlessly integrates with other AWS services like Amazon S3, Amazon Redshift, Amazon RDS, etc., allowing you to easily move and process data between these services. It also provides built-in connections to popular data sources like Oracle, MySQL, etc. Amazon Athena, on the other hand, is tightly integrated with Amazon S3 and is primarily used for querying and analyzing data stored in S3. It does not have direct integrations with other AWS services.
In summary, AWS Glue is a comprehensive ETL service that offers managed data transformation, integration, and cataloging capabilities. It is designed for large-scale data processing and provides extensive integration options. On the other hand, Amazon Athena is a powerful querying and analysis service that allows you to run SQL queries directly on data stored in Amazon S3. It is ideal for ad-hoc analysis and does not require any setup or infrastructure management.
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 Amazon Athena
- Use SQL to analyze CSV files16
- Glue crawlers gives easy Data catalogue8
- Cheap7
- Query all my data without running servers 24x76
- No data base servers yay4
- Easy integration with QuickSight3
- Query and analyse CSV,parquet,json files in sql2
- Also glue and athena use same data catalog2
- No configuration required1
- Ad hoc checks on data made easy0
Pros of AWS Glue
- Managed Hive Metastore9