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AWS Glue vs Azure Data Factory: What are the differences?
AWS Glue and Azure Data Factory are both cloud-based data integration services offered by AWS and Microsoft respectively. These services enable organizations to orchestrate and automate the process of data extraction, transformation, and loading (ETL) in a scalable and efficient manner.
Pricing Model: AWS Glue follows a pay-as-you-go pricing model, where users are billed based on the amount of data processed and the number of compute resources used. Azure Data Factory also adopts a similar pricing structure, charging based on data integration activity and the number of pipeline runs.
Connectivity: AWS Glue supports a wide range of data sources, including popular AWS services such as Amazon S3, Amazon RDS, and Amazon Redshift, as well as various on-premises data stores. Azure Data Factory, on the other hand, integrates well with Microsoft services like Azure Blob Storage, Azure SQL Database, and Azure Data Lake Store, along with other external data sources.
Data Transformation Capabilities: Both AWS Glue and Azure Data Factory provide robust data transformation capabilities. AWS Glue supports the use of Python and Spark-based ETL scripts for complex transformations, and it offers a visual interface for creating and managing workflows. Azure Data Factory offers a drag-and-drop interface for building data transformation pipelines and supports built-in data integration and transformation activities.
Data Movement and Orchestration: AWS Glue leverages scalable serverless technology to automatically generate and execute ETL code, simplifying the process of moving and transforming data. Azure Data Factory also provides a serverless option for data movement and orchestration, enabling users to efficiently transfer data between various sources and destinations.
Monitoring and Governance: Both AWS Glue and Azure Data Factory offer built-in monitoring and logging capabilities. AWS Glue integrates with Amazon CloudWatch to provide detailed monitoring and performance metrics. Azure Data Factory integrates with Azure Monitor and Azure Log Analytics for monitoring and logging purposes.
Ecosystem and Integration: AWS Glue seamlessly integrates with other AWS services, allowing users to leverage additional capabilities such as data cataloging, data quality checks, and metadata management. Azure Data Factory integrates well with the Microsoft Azure ecosystem, enabling users to leverage services like Azure Machine Learning, Azure Databricks, and Azure Functions for advanced data processing and analytics.
In summary, both AWS Glue and Azure Data Factory provide powerful data integration and ETL capabilities in a cloud environment, with differences primarily lying in their pricing models, connectivity options, data transformation capabilities, data movement and orchestration methods, monitoring and governance features, and ecosystem integrations.
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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