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AWS Glue vs Druid: What are the differences?
Introduction
AWS Glue and Druid are both data integration and transformation tools used for big data processing. However, they have significant differences in terms of their capabilities and features. In this comparison, we will highlight the key differences between AWS Glue and Druid.
Data Source and Integration: AWS Glue is primarily designed for integrating and transforming data from various sources into a format suitable for analysis and querying. It supports a wide range of data sources, including RDBMS, NoSQL databases, and file systems. On the other hand, Druid is a distributed data store specifically optimized for time-series and event data. It is built to handle high ingest rates and efficient querying of large datasets.
Data Transformation and Processing: AWS Glue provides a variety of transformations and data processing capabilities, including data cleaning, normalization, deduplication, and schema evolution. It can automatically generate ETL code and run transformations on a fully managed infrastructure. In contrast, Druid offers limited data transformation capabilities. It is primarily focused on storing and querying data efficiently, rather than data transformation and integration.
Querying and Analysis: AWS Glue provides a SQL-like query language called Glue Query Language (GQL) for querying and analyzing data. It supports complex queries, aggregations, and joins on structured and semi-structured data. Druid, on the other hand, uses a custom query language called Druid Query Language (DQL). DQL is optimized for time-series data and provides fast querying and aggregations on large datasets.
Scalability and Performance: Both AWS Glue and Druid are designed to handle large datasets and provide scalable and high-performance data processing. However, Druid is specifically optimized for high ingest rates and efficient querying of time-series data. It can handle streaming data and enable real-time analytics on large volumes of data. AWS Glue, on the other hand, can scale horizontally to handle big data workloads but may not be as optimized for real-time streaming data.
Managed Service vs. Self-Managed: AWS Glue is a fully managed service provided by Amazon Web Services (AWS). It takes care of infrastructure provisioning, scaling, and maintenance, allowing users to focus on data transformation and analysis. In contrast, Druid is an open-source project that requires self-management and infrastructure setup. While it provides flexibility and control, it may require more effort and expertise to manage and maintain.
Integration with other Services: AWS Glue seamlessly integrates with other AWS services, including AWS S3, AWS Redshift, and AWS Athena, providing a unified data processing platform within the AWS ecosystem. It can easily load and transform data from these services for analysis and querying. Druid, being a standalone data store, may require additional integrations and configurations to work with other tools and services in the data analytics stack.
In summary, AWS Glue and Druid have significant differences in terms of their data integration capabilities, data transformation and processing features, querying and analysis tools, scalability and performance optimizations, managed service vs. self-managed aspects, and integration with other services.
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 Druid
- Real Time Aggregations15
- Batch and Real-Time Ingestion6
- OLAP5
- OLAP + OLTP3
- Combining stream and historical analytics2
- OLTP1
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Cons of AWS Glue
Cons of Druid
- Limited sql support3
- Joins are not supported well2
- Complexity1
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