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AWS Glue vs Amazon Redshift Spectrum vs Mara: What are the differences?
Introduction
When choosing between AWS Glue, Amazon Redshift Spectrum, and Mara for data processing in the cloud, it's essential to understand the key differences between these services.
Integration with data sources: AWS Glue is a fully managed ETL service that can extract, transform, and load data from various sources seamlessly. In contrast, Amazon Redshift Spectrum extends the functionality of Amazon Redshift to query data directly from S3 without the need to load it into Redshift. On the other hand, Mara is a data orchestration tool that provides workflow automation and integration with multiple data sources, making it easier to manage complex data pipelines.
Cost structure: AWS Glue pricing is based on the number of Data Processing Units (DPU) used during job execution, as well as the number of crawlers, classifiers, and connections. Amazon Redshift Spectrum, on the other hand, charges based on the amount of data scanned during queries. Mara offers a flexible pricing model based on the number of active workflows and users, making it a cost-effective option for organizations with varying data processing needs.
Performance and scalability: AWS Glue provides elastic scalability to handle varying workloads efficiently, making it suitable for dynamic data processing requirements. Amazon Redshift Spectrum leverages the power of Amazon Redshift's massively parallel processing (MPP) architecture for high-performance querying of large datasets. Mara, with its distributed data processing capabilities, can scale horizontally to accommodate growing data volumes and processing demands without compromising performance.
Data storage and retention: While AWS Glue offers data cataloging capabilities to organize and manage metadata for various data sources, Amazon Redshift Spectrum relies on the existing data structures in S3. Mara allows users to define data storage policies and retention rules to manage data lifecycle effectively, ensuring compliance with data governance policies and regulations.
Query optimization and data processing: Amazon Redshift Spectrum optimizes queries by pushing down predicates to S3 and caching query results for faster retrieval, improving query performance and reducing costs. AWS Glue uses Apache Spark to process and transform data at scale, offering built-in optimizations for parallel processing and distributed computing. Mara streamlines data processing workflows using custom workflows and task dependencies, ensuring efficient data processing and timely execution of tasks in complex data pipelines.
Ease of use and management: AWS Glue provides a visual interface for building and monitoring ETL workflows, simplifying the development and management of data pipelines. Amazon Redshift Spectrum seamlessly integrates with Redshift's SQL-based querying language, making it easy for users to access and analyze data stored in S3. Mara offers a user-friendly interface for designing and managing data workflows, with drag-and-drop features and scheduling options for automating data processing tasks effectively.
In Summary, understanding the key differences between AWS Glue, Amazon Redshift Spectrum, and Mara is crucial for selecting the right data processing solution based on cost, performance, scalability, and management requirements.
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 Amazon Redshift Spectrum
- Good Performance1
- Great Documentation1
- Economical1
Pros of AWS Glue
- Managed Hive Metastore9
Pros of Mara
- Great developing experience1
- ETL Tool1
- UI focused on ETL development1
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