AWS Glue vs. Azure Data Factory

When the data which requires ETL has different formats, schema, and volume, this service suits them best. So, when the volume is not consistent (typical use-case of healthcare and online shopping), AWS Glue can be the prime choice. When the data is available in both batch and streaming mode, the developer needs to generate a separate codebase. This increases the source code management efforts. So, prefer to go with Glue when the nature of the data is the same (either batched or streamed).

Incentivized

In a data pipeline, you will be able to add different kinds of activities for example connect from your on-premise SFTP and move CSV files to storage accounts. As well data factory has its own data flow if you are an ETL developer who experimented with maybe you have worked with SSIS, thus, you will start quickly with this new feature of the data factory.

Incentivized

• After data cleansing, the team also implemented the best practices for using AWS platform services as a Data Lake, such as job bookmarking for AWS Glue jobs, proper delimiter for the AWS Glue crawlers, partitioning in AWS S3, and transformation to parquet file for compression and faster querying time in Amazon Athena.
• Data modernization through combining data from multiple sources into a functioning datasets, rebuilding DW, and resctructuring data sources.
• Aims to lessen customer complaints, eliminate manual data extraction requests via SR from different data sources, and Increase accuracy, consistency and speed up reconciliation process.

Incentivized

• Creating ETL and ELT workflows as well as orchestrating and monitoring pipelines without writing any code.
• Hybrid data integration is easily and agilely possible through this software.
• It has lot of various useful components

Incentivized

• It’s integration with other cloud vendors is bit difficult
• If it can support non SQL based databases as well, it would be powerful.
• Real time data synchronisation in data source is missing

Incentivized

• Learning curve for pipeline creation interface.
• Alerting isn't necessarily built in. Had to work around this to meet team needs.
• With GIT enabled, some features can only be done via git, while some need to be done via the portal.

Incentivized

I personally found it very usable for a data engineer's day job, particularly for performing ETL and managing the data pipelines.

Incentivized

So far product has performed as expected. We were noticing some performance issues, but they were largely Synapse related. This has led to a shift from Synapse to Databricks. Overall this has delayed our analytic platform. Once databricks becomes fully operational, Azure Data Factory will be critical to our environment and future success.

Incentivized

Amazon responds in good time once the ticket has been generated but needs to generate tickets frequent because very few sample codes are available, and it's not cover all the scenarios.

Incentivized

We have not had need to engage with Microsoft much on Azure Data Factory, but they have been responsive and helpful when needed. This being said, we have not had a major emergency or outage requiring their intervention. The score of seven is a representation that they have done well for now, but have not proved out their support for a significant issue

Incentivized

The cataloging of data objects is the best in the case of AWS Glue. We use AWS Glue in all of our data pipelines to sync external and internal data sources and to automatically produce SQL-based ETL based on AWS Glue catalog objects. Integration with Amazon products is the other advantage.

Incentivized

Azure Data Factory fits well into our overall systems architecture where we already utilize largely Azure services and also Microsoft based products in the on-premises environment. I think cost structure is also very competitive with Azure Data Factory. Most services provide a visual interface for designing ETL workflows, but our team found Azure Data Factory's interface more intuitive.

Incentivized

• Positive Impact :- after ETL we can able to do some kind of automation
• Negative :- At some point of time it can hamper the cost but not really

Incentivized

• Limiting the amount of data moving up and down from the cloud for cloud-native applications.
• Overall simple to use interface which is actually easier for a first time ETL developer than SSIS.

Incentivized