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Amazon Athena vs Apache Parquet: What are the differences?
Introduction
Amazon Athena and Apache Parquet are both widely used technologies in the field of big data analytics. While Amazon Athena is a serverless interactive query service that enables users to analyze data directly from Amazon S3 using SQL, Apache Parquet is a columnar storage file format that is optimized for analytical processing.
Data Storage: One key difference between Amazon Athena and Apache Parquet is their approach to data storage. Amazon Athena uses Amazon S3 as its data source, where data is stored in its original format. On the other hand, Apache Parquet is a file format that stores data in a columnar format, which is more efficient for analytical processing. This columnar storage allows for faster query performance, especially when only a subset of columns needs to be accessed.
Data Schema: Another difference lies in how the data schema is managed. Amazon Athena does not require predefined schema definitions and can infer the schema dynamically at runtime, making it more flexible for ad-hoc queries. In contrast, Apache Parquet requires a predefined schema to be defined and enforced, providing stronger data validation and consistency.
Data Partitioning: Amazon Athena supports automatic data partitioning based on the data stored in Amazon S3. This means that queries can be optimized by targeting specific partitions, resulting in faster execution times. On the other hand, Apache Parquet does not have built-in support for automatic data partitioning and requires manual configuration.
Query Performance: When it comes to query performance, Amazon Athena provides a serverless, highly scalable and performant solution. It can handle large-scale parallel query execution, resulting in fast query response times. Apache Parquet, being a columnar storage format, offers faster query performance due to its ability to selectively read only the required columns for a query, minimizing disk I/O.
Data Compression: Both Amazon Athena and Apache Parquet support data compression techniques, but they have different approaches. Amazon Athena utilizes ORC (Optimized Row Columnar) or Parquet columnar storage formats and uses compression techniques like Snappy and Zlib. Apache Parquet, being a columnar storage format itself, employs a wide range of compression options such as Snappy, Gzip, and LZO, allowing users to choose the most suitable compression algorithm for their data.
Data Accessibility: Amazon Athena is a managed service provided by Amazon Web Services (AWS) and can be accessed directly through the AWS Management Console or API, making it easily accessible to AWS users. Apache Parquet, being an open-source file format, can be accessed through various tools and frameworks including Apache Hive, Apache Spark, and Apache Arrow, providing flexibility for users to choose the best toolset for their specific needs.
In Summary, Amazon Athena and Apache Parquet differ in their storage approach, data schema management, data partitioning capabilities, query performance, data compression options, and accessibility, making them suitable for different use cases and preferences.
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 Apache Parquet
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