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AWS Glue vs Apache Parquet: What are the differences?
Introduction
AWS Glue and Apache Parquet are both technologies used in the field of big data processing. While AWS Glue is a fully managed extract, transform, and load (ETL) service provided by Amazon Web Services, Apache Parquet is an open-source columnar storage file format. Although both technologies have their own unique features and benefits, there are several key differences that set them apart from each other.
Data Processing: AWS Glue is primarily used for ETL operations, allowing users to extract data from various sources, transform it according to their needs, and load it into a target destination. On the other hand, Apache Parquet is a file format optimized for columnar storage, making it suitable for analytical processing and fast query performance.
Data Storage: AWS Glue does not provide storage capabilities of its own. Instead, it allows users to work with data stored in various formats such as Amazon S3, Amazon Redshift, and more. Apache Parquet, on the other hand, is a file format that efficiently stores data in a columnar layout, providing high compression ratios and enabling efficient querying by reading only the necessary columns.
Schema Evolution: AWS Glue offers built-in schema evolution capabilities, allowing users to handle changes in data structures over time. This means that if a data source's schema changes, AWS Glue can adjust the transformation logic accordingly. In contrast, Apache Parquet has limited support for schema evolution and may require manual intervention to handle changes in schema.
Compression: AWS Glue offers multiple compression options for transforming and loading data, providing flexibility and reducing storage costs. Apache Parquet, on the other hand, natively supports compression algorithms such as Snappy, Gzip, and LZO, enabling efficient storage and retrieval of data.
Data Partitioning: AWS Glue supports data partitioning, allowing users to store data in a partitioned manner based on specific columns. This helps improve query performance by reducing the amount of data that needs to be scanned. Apache Parquet also supports data partitioning, but it is implemented at the file level rather than the column level.
Metadata Management: AWS Glue automatically generates and manages metadata for the data it processes, providing a comprehensive data catalog and enabling easy discovery and exploration of data. Apache Parquet, on the other hand, does not have built-in metadata management capabilities and relies on external tools or custom implementations for managing metadata.
In summary, AWS Glue is a fully managed ETL service focused on data extraction, transformation, and loading, while Apache Parquet is an open-source columnar storage file format optimized for analytical processing. AWS Glue provides built-in schema evolution, compression options, data partitioning, and metadata management capabilities, whereas Apache Parquet offers efficient columnar storage, limited schema evolution support, native compression options, and file-level data partitioning.
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
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