4 Keys to Succeeding with Agile Data Warehousing in 2016
I have been out giving talks again on using agile methods for data warehouse and business intelligence projects, so I thought it was time for me to share my thoughts about the 4 key elements you need to be successful with an Agile DW project in 2016.
Adopt an Agile Methodology
By this I am talking about SCRUM, Kanban, ScrumBan, or DAD (Disciplined Agile Development), among others.
Go read the blogs, read the books, study these methods. Attend a conference (like Agile Tech in April). Figure out what will work for your organization’s culture and leverage the skills of your staff. One size does not fit all.
In past engagements I have used approaches primarily based on SCRUM and Kanban. Both have been very effective once we got our processes down.
If you need/want help, find a good agile coach.
Use an Agile Data Engineering Approach
If you want to develop your data warehouse in an agile, iterative manner, then you need a way to design your EDW repository that lends itself to this approach without causing huge re-engineering pains (known as refactoring) in future iterations.
The best way I have found is using the Data Vault modeling approach. It was designed specifically for building data warehouses in this manner. I have written much about this approach and give many talks showing examples of successful agile projects using Data Vault. And there is plenty of material available to help you learn how to do it (see the books on the sidebar of this blog).
Use Data Warehouse Automation Software
No better way to get agile and deliver results fast, than to automate as much of your development work as possible. If you use repeatable patterns (like Data Vault) in your design methodology, then it is even easier to automate and greatly reduce your time to market.
There are two vendors in the market that I like a lot and have had some experience with. They are WhereScape and AnalytixDS. And both support not only “traditional” approaches to data warehousing (like automating the ETL for a Type 2 Slowly Changing Dimension) but they both also support Data Vault (and both will be at WWDVC 2016).
Which of these tools you might use depends on your approach, your current tools, and your skills.
If you are coming from a more traditional DW paradigm and use ETL tools like Informatica, Talend, or DataStage, then I would recommend you look at AnalytixDS Mapping Manager which allows you to generate your ETL code from source to target mappings.
If you are just getting started or are committed to more of a database-centric approach and want your ETL or ELT code to run in the database, then look at WhereScape’s products.
Both are great companies with knowledgable people and happy customers.
Your third option is to write your own automation routines. There are many shops doing that as well. Just be sure you have the appropriate skills in house and can allocate the upfront time to get going (a month or so at least).
Deploy on an Agile Data Warehouse Platform
So now that I have learned about Elastic Data Warehousing in the cloud, I can’t imagine trying to do an agile DW project any other way.
Of course I am referring to Snowflake Computing’s DWaaS (data warehouse as a service) offering. Yes, I might be a bit biased since I do work for them now, but…this tech is really good!
From a features perspective, what I am talking about is having a high powered, easily scalable database that supports BI and analytic workloads and does not require a ton of time to configure and tweak.
Why do I think that is a success criteria? Because I have spent way too many months on way too many “agile” projects waiting to get access to the hardware! Or I get access and we either run out of space (e.g., “we had no idea you need THAT much storage”) or we can’t properly test production level loads and queries because the development box does not have enough horsepower.
Taking advantage of the elasticity of the cloud solves both of these problems and the folks at Snowflake have successfully built an RDBMS in the cloud that specifically harnesses these features and leverages them for data warehouse and analytic workloads by providing the ability to scale up and scale down both storage and compute resources on demand.
So what do you think? Are you ready to accelerate your team’s performance and adopt an agile approach to data warehousing?
I hope this post gives you a few ideas on how to make that happen.
The Data Warrior