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AWS Glue vs Delta Lake: What are the differences?
Introduction
This Markdown code highlights the key differences between AWS Glue and Delta Lake.
Data Storage: AWS Glue is a fully managed extract, transform, and load (ETL) service that allows you to prepare and transform massive amounts of data for analytics. It does not provide storage capabilities but integrates with various data storage services like Amazon S3 and Amazon Redshift. On the other hand, Delta Lake is an open-source data lake storage layer that adds reliability, scalability, and performance optimizations to cloud storage systems such as S3 and Azure Data Lake Storage Gen1/Gen2.
ACID Transactions: Delta Lake provides support for ACID (Atomicity, Consistency, Isolation, Durability) transactions, ensuring that data modifications follow these properties. It allows for concurrent reads and writes, enables accurate rollbacks, and provides transactional consistency. AWS Glue, on the other hand, does not provide built-in ACID transactions, and data modifications may not be atomic or consistent.
Schema Evolution: Delta Lake supports schema evolution, which allows changes to the structure of the data over time. It handles schema changes seamlessly and provides flexibility in evolving data models. AWS Glue, however, does not have direct support for schema evolution and may require manual intervention or additional processing to handle schema changes.
Data Validation: Delta Lake includes a mechanism for data validation using schema enforcement. It ensures that data adheres to a predefined schema and rejects writes that violate the schema. This helps maintain data integrity and prevents data corruption. AWS Glue, on the other hand, does not have built-in data validation mechanisms and may require additional data quality checks to ensure data integrity.
Metadata Management: AWS Glue provides a metadata catalog that allows you to define, manage, and discover metadata for various data sources, including tables, databases, and jobs. It provides a centralized view of metadata and fosters data governance. Delta Lake, while not specifically offering a metadata management system, allows storing metadata in its transactional log to track changes in tables and data.
Data Lake Optimization: Delta Lake provides various optimizations for data lake workloads, including indexing, data skipping, and Z-ordering. These optimizations improve query performance by reducing data IO and promoting query pushdown to the storage layer. AWS Glue, being an ETL service, focuses more on data transformation and preparation rather than optimizing data lake workloads.
In summary, AWS Glue is a fully managed ETL service that integrates with storage services, while Delta Lake is an open-source data lake storage layer that adds reliability, scalability, and transactional capabilities to cloud storage systems. Delta Lake provides support for ACID transactions, schema evolution, data validation, and data lake optimizations, whereas AWS Glue focuses on ETL operations and metadata management without ACID transactions or built-in schema evolution capabilities.
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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