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AWS Glue vs Databricks: What are the differences?
AWS Glue and Databricks are both popular data processing and analytics platforms, but they have some key differences that set them apart from each other. In this comparison, we will explore these differences in detail.
Managed Service vs Collaborative Workspace: AWS Glue is a fully managed ETL (Extract, Transform, Load) service provided by Amazon Web Services. It automates the entire process of discovering, cataloging, and transforming data into a usable format. On the other hand, Databricks is a collaborative workspace that provides a unified analytics platform. It combines data engineering capabilities along with advanced analytics, machine learning, and visualization features.
Scalability and Flexibility: AWS Glue is designed to be highly scalable, allowing you to process large volumes of data efficiently. It automatically scales resources based on the size of the data and the complexity of the transformations. Databricks, on the other hand, provides a flexible and scalable environment for data analytics and processing. It offers the ability to scale compute and storage resources independently, providing more granular control over resource allocation.
Data Lake vs Data Warehouse: AWS Glue is often used as a tool to build data lakes by consolidating data from various sources and making it available for analysis. It is well-integrated with other AWS services like Amazon S3, Redshift, and Athena, enabling seamless data ingestion and transformation. Databricks, on the other hand, focuses more on data warehouse capabilities and provides tight integration with popular data warehousing solutions like Delta Lake and Apache Spark.
Integration with Ecosystem: AWS Glue seamlessly integrates with other AWS services, allowing you to build end-to-end data processing pipelines using services like AWS Lambda, AWS Step Functions, and AWS Glue Spark ETL jobs. Databricks also offers integration with various third-party tools and services, making it easier to connect with different data sources and systems.
Machine Learning Capabilities: Databricks provides extensive support for machine learning and advanced analytics with built-in libraries like MLlib and MLflow. It offers a collaborative environment for data scientists and data engineers to build, deploy, and manage machine learning models. AWS Glue, on the other hand, is primarily focused on data processing and ETL, and does not provide as many built-in machine learning capabilities compared to Databricks.
Pricing Model: AWS Glue pricing is based on the number of data catalog objects, crawler runs, and development endpoints used. It also charges for the amount of data processed during ETL jobs. Databricks follows a consumption-based pricing model, where you pay for the resources you use, such as compute instances and storage.
In summary, AWS Glue is a fully managed ETL service focusing on data integration and processing in the AWS ecosystem, while Databricks is a collaborative workspace that provides a unified analytics platform with powerful machine learning capabilities. The choice between the two depends on your specific use case, whether you need a fully managed service for ETL or a collaborative environment for advanced analytics and machine learning.
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 AWS Glue
- Managed Hive Metastore9
Pros of Databricks
- Best Performances on large datasets1
- True lakehouse architecture1
- Scalability1
- Databricks doesn't get access to your data1
- Usage Based Billing1
- Security1
- Data stays in your cloud account1
- Multicloud1
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