A third party is producing a complete daily snapshot of their database table (Authors) and is storing it as a Parquet file in S3. Currently the number of records are around 55 million+. This will increase daily. There are 12 columns.
Initially I want to take this whole dataset and do some processing on the records, normalise them and then block them into groups of authors based on some specific criterias. I will then need to repeat this process daily, and filter it to only include authors that have been added or updated since the previous day.
I am using AWS EMR on EKS (Kubernetes) as my Spark cluster. My current thoughts are that I can save my blocks of authors on HDFS.
The main use for the blocks of data will be a separate Spark Streaming job that will then be deployed unto the same EMR cluster, and will read events from a Kafka topic and do a quick search to see which blocks of data are related to that event, and then it will do some matching (pairwise) against each item of that block.
I have two main questions:
- Is using HDFS a performant and viable option for this use case?
- The third party database table dump is going to be an initial goal. Later on, there will be quite possibly 10s or even 100s of other sources that I would need to do matching against. Which means trillions of data that are blocked and those blocks need to be stored somewhere. Would this option still be viable at that stage?