Amazon Athena vs. Amazon SageMaker

Best suited for analyzing huge amounts of data by just querying on Amazon Athena. Amazon Athena is also best to integrate with Amazon Quickight for visualization and reporting of data. Easy to work with CSV, JSON, and columnar data formats like Parquet, and ORC. Less appropriate to work with AVRO data format and also stored procedures are not supported in Amazon Athena. The size of a single row is also limited to 32 MB.

Incentivized

Amazon Sagemaker suits well in areas of data science and Machine learnings where medium to high-volume data is to be used for analysis. For a lean and platform agnostic deployment, it provides kubernetes integration to containerize the solution and deploy on any platform. It is one of the best solution for technical users for training Machine Learning models.

Incentivized

• Load Balance traffic analysis
• Big data report generation
• Micro services pattern query analysis

Incentivized

• SageMaker is useful as a managed Jupyter notebook server. Using the notebook instances' IAM roles to grant access to private S3 buckets and other AWS resources is great. Using SageMaker's lifecycle scripts and AWS Secrets Manager to inject connection strings and other secrets is great.
• SageMaker is good at serving models. The interface it provides is often clunky, but a managed, auto-scaling model server is powerful.
• SageMaker is opinionated about versioning machine learning models and useful if you agree with its opinions.

Incentivized

• Every dialect of SQL has some missing functions. I wish there was automated GROUP BY options here.
• There are connection problems back to Power BI occasionally.
• If you don't watch certain queries, it's possible that it takes a long time to run and charges you a lot of money.

Incentivized

• Searching and descriptions can be easier to read and interpret.
• Training modules and customer service training representative could make on boarding employees easier.

Incentivized

Easy to use. Scalable. Gets the job of data warehousing setup done. Using the datalake on S3 has become super convenient.

Incentivized

Amazon Athena, a product from Amazon, competes with offerings from Google and Microsoft. Overall, I think your database choice depends on some of the other applications you are running at your company. For example, if you are using Microsoft Power BI for reporting needs, you might want to consider going the Azure route.

Incentivized

We have not invested in another machine learning software at this time and so far this has proved very successful with our machine learning teams. As mentioned, I am training these individuals simply on the fundamentals of the software and using it/customizing it for their needs. It has been very easy to do this and has gotten great reviews across the organization so far.

Incentivized

• It's easy to store and query data on S3. Multiple teams can query the same data to generate their reports. It removes the need for a full-fledged data warehouse for a startup. Saves costs.
• Improved team efficiency on monitoring user activities by easy logging and reporting.
• As the dataset gets heavier on S3, one needs to understand partitioning and that leads to the requirement of expertise.

Incentivized

• Using SageMaker, we can truly implement 'fail early, learn fast,' using an on-demand server for training.
• It also saves your money from investing in a physical server for very rare use.
• However, the pricing is high, but it will cost you only for what you use.

Incentivized