Better than querying off your OLTP
Anonymous | TrustRadius Reviewer
July 15, 2016

Better than querying off your OLTP

Score 9 out of 10
Vetted Review
Verified User
Review Source

Overall Satisfaction with SQL Data Warehouse

We use it to store large amounts of SQL data for our predictive analytics and big data modeling. We use it across several team but I cannot say it is used for the entire organization as my department operates relatively independently of the rest of the organization. We have an extremely large data sets and need to store it in a way that makes it accessible and fast.
  • Quick to return data. Queries in a SQL data warehouse architecture tend to return data much more quickly than a OLTP setup. Especially with columnar indexes.
  • Ability to manage extremely large SQL tables. Our databases contain billions of records. This would be unwieldy without a proper SQL datawarehouse
  • Backup and replication. Because we're already using SQL, moving the data to a datawarehouse makes it easier to manage as our users are already familiar with SQL.
  • It takes some time to setup a proper SQL Datawarehouse architecture. Without proper SSIS/automation scripts, this can be a very daunting task.
  • It takes a lot of foresight when designing a Data Warehouse. If not properly designed, it can be very troublesome to use and/or modify later on.
  • It takes a lot of effort to maintain. Businesses are continually changing. With that, a full time staff member or more will be required to maintain the SQL Data Warehouse.
  • It definitely has a positive impact on ROI. We are able to use it to generate MORE revenue through predictive analytics and pricing optimization.
  • Because of the SQL Data Warehouse design, we're able to set up some self service reporting tools which allow our users to generate reports ad hoc instead of having a full time employee creating these by hand.
  • Having visibility into the data is very useful for management to make good business decisions.
SQL Data Warehousing is much easier to manage if you already have SQL Server experience and analysts who are familiar with its interface. We are currently piloting using NoSQL and Hadoop type databases but it is difficult to get set up properly. Additionally, we have to re-train our users to learn how to create python scripts and use spark to query the data.
It is very well suited for big data analytics. Predictive modeling, optimization, and other large scale analysis benefit from using a properly defined SQL Data Warehouse. It is also suited for simple business intelligence such as building historical and active dashboards.