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Improving our use of Redshift: Then and now

Historically, and common among enterprise IT processes, the 2nd Watch optimization team was pulling in cost usage reports from Amazon and storing them in S3 buckets. The data was then loaded into Redshift, Amazon’s cloud data warehouse, where it could be manipulated and analyzed for client optimization. Unfortunately, the Redshift cluster filled up quickly and regularly, forcing us to spend unnecessary time and resources on maintenance and clean up. Additionally, Redshift requires a large cluster to work with, so the process for accessing and using data became slow and inefficient.

Of course, to solve for this we could have doubled the size, and therefore the cost, of our Redshift usage, but that went against our commitment to provide cost-effective options for our clients. We also could have considered moving to a different type of node that is storage optimized, instead of compute optimized.

Lakehouse Architecture for speed improvements and cost savings

The better solution we uncovered, however, was to follow the Lakehouse Architecture pattern to improve our use of Redshift to move faster and with more visibility, without additional storage fees. The Lakehouse Architecture is a way to strike a balance between cost and agility by selectively moving data in and out of Redshift depending on the processing speed needed for the data. Now, after a data dump to S3, we use AWS Glue crawlers and tables to create external tables in the Glue Data Catalogues. The external tables or schemas are linked to the Redshift cluster, allowing our optimization team to read from S3 to Redshift using Redshift Spectrum.

Our cloud data warehouse remains tidy without dedicated clean-up resources, and we can query the data in S3 via Redshift without having to move anything. Even though we’re using the same warehouse, we’ve optimized its use for the benefit of both our clients and 2nd Watch best practices. In fact, our estimated savings are $15,000 per month, or 100% of our previous Redshift cost.

How we’re using Redshift today

With our new model and the benefits afforded to clients, 2nd Watch is applying Redshift for a variety of optimization opportunities.

Discover new opportunities for optimization. By storing and organizing data related to our clients’ AWS, Azure, and/or Google Cloud usage versus spend data, the 2nd Watch optimization team can see where further optimization is possible. Improved data access and visibility enables a deeper examination of cost history, resource usage, and any known RIs or savings plans.

Increase automation and reduce human error. The new model allows us to use DBT (data build tool) to complete SQL transforms on all data models used to feed reporting. These reports go into our dashboards and are then presented to clients for optimization. DBT empowers analysts to transform warehouse data more efficiently, and with less risk, by relying on automation instead of spreadsheets.

Improve efficiency from raw data to client reporting. Raw data that lives in a data lake in s3 is transformed and organized into a structured data lake that is prepared to be defined in AWS Glue Catalog tables. This gives the analysts access to query the data from Redshift and use DBT to format the data into useful tables. From there, the optimization team can make data-based recommendations and generate complete reports for clients.

In the future, we plan on feeding a power business intelligence dashboard directly from Redshift, further increasing efficiency for both our optimization team and our clients.

Client benefits with Redshift optimization

  • Cost savings: Only pay for the S3 storage you use, without any storage fees from Redshift.
  • Unlimited data access: Large amounts of old data are available in the data lake, which can be joined across tables and brought into Redshift as needed.
  • Increased data visibility: Greater insight into data enables us to provide more optimization opportunities and supports decision making.
  • Improved flexibility and productivity: Analysts can get historical data within one hour, rather than waiting 1-2 weeks for requests to be fulfilled.
  • Reduced compute cost: By shifting the compute cost of loading data into to Amazon EKS.

-Spencer Dorway, Data Engineer

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