Optimal Big Data solution for aggregating time-series data and storing results to DynamoDB

Question

I am looking into different Big Data solutions and have not been able to find a clear answer or documentation on what might be the best approach and frameworks/services to use to address my Big Data use-case.

My Use-case:

• I have a data producer that will be sending ~1-2 billion events to a Kinesis Data Firehose delivery stream daily.
• This data needs to be stored in some data lake / data warehouse, aggregated, and then loaded into DynamoDB for our service to consume the aggregated data in its business logic.
• The DynamoDB table needs to be updated hourly. (hourly is not a hard requirement but we would like DynamoDB to be updated as soon as possible, at the longest intervals of daily updates if required)
• The event schema is similar to: customerId, deviceId, countryCode, timestamp
• The aggregated schema is similar to: customerId, deviceId, countryCode (the aggregation is on the customerId's/deviceId's MAX(countryCode) for each day over the last 29 days, and then the MAX(countryCode) overall over the last 29 days.
• Only the CustomerIds/deviceIds that had their countryCode change from the last aggregation (from an hour ago) should be written to DynamoDB to keep required write capacity units low.
• The raw data stored in the data lake / data warehouse needs to be deleted after 30 days.

My proposed solution:

• Kinesis Data Firehose delivers the data to a Redshift staging table (by default using S3 as intermediate storage and then using the COPY command to load to Redshift)
• An hourly Glue job that:
  1. Drops the 30 day old time-series table and creates a new time-series table for today in Redshift if this is the first job run of a new day
  2. Loads data from staging table to the appropriate time-series table
  3. Creates a view on top of the last 29 days of time-series tables
  4. Aggregates by customerId, deviceId, date, and MAX(CountryCode)
  5. Then aggregates by customerId, deviceId, MAX(countryCode)
  6. Writes the aggregated results to an S3 bucket
  7. Checks the previous hourly Glue job's run aggregated results vs. the current runs aggregated results to find the customerIds/deviceIds that had their countryCode change
  8. Writes the customerIds/deviceIds rows that had their countryCode change to DynamoDB

My questions:

1. Is Redshift the best storage choice here? I was also considering using S3 as storage and directly querying data from S3 using a Glue job, though I like the idea of a fully-managed data warehouse.
2. Since our data has a fixed retention period of 30 days, AWS documentation: https://docs.aws.amazon.com/redshift/latest/dg/c_best-practices-time-series-tables.html suggests to use time-series tables and running DROP TABLE on older data that needs to be deleted. Are there other approaches (outside of Redshift) that would make the data lifecycle management easier? Having the staging table, creating and loading into new time-series tables, dropping older time-series tables, updating the view to include the new time-series table and not the one that was dropped could be error prone.
3. What would be an optimal way to find the the rows (customerId/deviceId combinations) that had their countryCode change since the last aggregation? I was thinking the Glue job could create a table from the previous runs aggregated results S3 file and another table from the current runs aggregated results S3 file, run some variation of a FULL OUTER JOIN to find the rows that have different countryCodes. Is there a better approach here that I'm not aware of?

I am a newbie when it comes to Big Data and Big Data solutions so any and all input is appreciated!

Answer 1

tldr: Use step functions, not Glue. Use Redshift Spectrum with data in S3. Otherwise you overall structure looks on track.

You are on the right track IMHO but there are a few things that could be better. Redshift is great for sifting through tons of data and performing analytics on it. However I'm not sure you want to COPY the data into Redshift if all you are doing is building aggregates to be loaded into DDB. Do you have other analytic workloads being done that will justify storing the data in Redshift? Are there heavy transforms being done between the staging table and the time series event tables? If not you may want to make the time series tables external - read directly from S3 using Redshift Spectrum. This could be a big win as the initial data grouping and aggregating is done in the Spectrum layer in S3. This way the raw data doesn't have to be moved.

Next I would advise not using Glue unless you have a need (transform) that cannot easily be done elsewhere. I find Glue to require some expertise to get to do what you want and it sounds like you would just be using it for a data movement orchestrator. If this impression is correct you will be better off with a step function or even a data pipeline. (I've wasted way too much time trying to get Glue to do simple things. It's a powerful tool but make sure you'll get value from the time you will spend on it.)

If you are only using Redshift to do these aggregations and you go the Spectrum route above you will want to get as small a cluster as you can get away with. Redshift can be pricy and if you don't use its power, not cost effective. In this case you can run the cluster only as needed but Redshift boot up times are not fast and the smallest clusters are not expensive. So this is a possibility but only in the right circumstances. Depending on how difficult the aggregation is that you are doing you might want to look at Athena. If you are just running a few aggregating queries per hour then this could be the most cost effective approach.

Checking against the last hour's aggregations is just a matter of comparing the new aggregates against the old which are in S3. This is easily done with Redshift Spectrum or Athena as they can makes files (or sets of files) the source for a table. Then it is just running the queries.

In my opinion Glue is an ETL tool that can do high power transforms. It can do a lot of things but is not my first (or second) choice. It is touchy, requires a lot of configuration to do more than the basics, and requires expertise that many data groups don't have. If you are a Glue expert, knock you self out; If not, I would avoid.

As for data management, yes you don't want to be deleting tons of rows from the beginning of tables in Redshift. It creates a lot of data reorganization work. So storing your data in "month" tables and using a view is the right way to go in Redshift. Dropping tables doesn't create this housekeeping. That said if you organize you data in S3 in "month" folders then unneeded removing months of data can just be deleting these folders.

As for finding changing country codes this should be easy to do in SQL. Since you are comparing aggregate data to aggregate data this shouldn't be expensive either. Again Redshift Spectrum or Athena are tools that allow you to do this on S3 data.

As for being a big data newbie, not a worry, we all started there. The biggest difference from other areas is how important it is to move the data the fewest number of times. It sounds like you understand this when you say "Is Redshift the best storage choice here?". You seem to be recognizing the importance of where the data resides wrt the compute elements which is on target. If you need the horsepower of Redshift and will be accessing the data over and over again then the Redshift is the best option - The data is moved once to a place where the analytics need to run. However, Redshift is an expensive storage solution - it's not what it is meant to do. Redshift Spectrum is very interesting in that the initial aggregations of data is done in S3 and much reduced partial results are sent to Redshift for completion. S3 is a much cheaper storage solution and if your workload can be pattern-matched to Spectrum's capabilities this can be a clear winner.

I want to be clear that you have only described on area where you need a solution and I'm assuming that you don't have other needs for a Redshift cluster operating on the same data. This would change the optimization point.