Amazon Web Services’ (AWS) are the global market leaders in the cloud and related services. Its product AWS Glue is one of the best solutions in the serverless cloud computing category.
It allows the users to Extract, Transform, and Load (ETL) from the cloud data sources. An ETL tool is a vital part of the big data processing and analytics process. It also allows integrations with other tools such as AWS Lambda.
But, there are a few limitations that you may face in implementing AWS Glue. We will be looking at some of the AWS Glue limitations through this blog.
7 Limitations that come with AWS Glue
AWS Glue is a managed ETL service for Apache Spark. And it is not a full-fledged ETL service like Talend, Xplexty, etc.
Hence in order to customize the services as per your requirement, you need expertise. And it involves a huge amount of work as well.
But, once you make these customizations, you can seamlessly operate AWS Glue.
AWS Glue is specifically made for the AWS console and its products. And hence it isn’t easy to use for other technologies.
Also, it supports limited data sources like S3 and JDBC. Hence, you need to move your data to these cloud applications (if it is not there already) for the AWS Glue functioning.
This is one of the biggest limitations of the AWS Glue. To overcome this limitation, you need to have the above-mentioned data sources.
As AWS Glue only supports a handful of data sources like S3, there is no room to include an incremental synchronization with the data source.
Due to the lack of incremental sync, you cannot see the real-time data for complex operations.
You can overcome this challenge by portioning your data source sequences into a simplified process and seeing the real-time data.
AWS Glue is a serverless application, and it is still a novel technology.
Hence, the skillset required to implement and operate the AWS Glue is on the higher side.
You need to have a team with adequate knowledge expertise in the serverless architecture.
AWS Glue cannot support the conventional relational database systems. It can only support structured databases.
Hence, you need to have a SQL system for database storage to implement the AWS Glue successfully.
But, as most of the companies are using the SQL, NoSQL, or NewSQL anyways, this limitation is overcome in many cases.
AWS Glue requires you to test the changes in the live environment. It does not provide the test environment to analyze the repercussions of a change.
This slows down the deployment speed of the procedure.
But, you can test the changes in the smallest components of the real data and extrapolate those results on a big scale. This process can help you overcome this particular limitation of AWS Glue.
AWS Glue is still quite a new concept, and with serverless architecture, there is a lack of information readily available. Also, there are not many use cases and ready documentation that can solve your problems.
But this challenge in AWS Glue can easily be overcome. You simply need to raise tickets to solve the queries, and AWS has an excellent support team.
We can see from the above-mentioned examples that there are few limitations to the AWS glue.
But, we can also see that most of these limitations can be overcome without much hassle. Essentially, AWS Glue is still a new concept, and with time, it will only get better.
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