Exceeding corporate goals and outperforming your competitors through better analytics requires access to the most current and best information.
Finding that information can mean searching through millions or billions of rows of data, though. Whether your users are trying to detect threats and fraud or identify the best customers as targets for the next campaign, reacting faster than the competition will determine the winner in a given market or vertical.
Storing those millions or billions of data points required to answer your questions is not trivial, especially if the data resides in a traditional RDBMS.
That requires a complex infrastructure. Your chosen database software must live on a specific manufacturer’s server and be connected to storage from another specific vendor. All those components require a lot of work to deploy, configure, and manage. Technical support of the environment can also be a nightmare. Have you ever been told by a software vendor that an issue is hardware-based, and then had the server vendor tell you that your hurdle is actually a storage problem?
Another drawback to traditional data warehouses is the amount of tuning that is required. Query performance is never fast enough for your analytics users, so your DBAs spend much of their time adding indexes and managing buffer pools so that the business does not lose confidence in the data warehouse or IT.
Fortunately, there is a solution to the complexity and cost of ownership of legacy data repositories: the data warehouse appliance. Data warehousing appliances deliver game-changing performance without the constant need for attention demanded by RDBMS-based solutions.
Built on massively parallel architectures, data warehouse appliances are engineered from the ground up deliver extreme performance for analytics. Since everything was designed to work together, the configuration is pre-balanced and pre-optimized.
Gone are the days where you are left wondering if the database servers, storage, and hosts can handle the volume and I/O demands of your users’ queries. In addition, you will only have one vendor and one number to call if support is needed.
And how would you feel about getting more business value out of analytics with less administration? An appliance does not require data models (although data modeling is still a best practice), and storage administration and database tuning are all but eliminated. I thought you’d like that!
The growing popularity and clear benefits of appliances makes IBM’s recent announcement extremely exciting for analytics customers. The IBM Integrated Analytics System, or IIAS, went GA earlier this month. Their latest version of a data warehouse appliance is much more than just a place to store your data: it’s a hybrid data management solution in a box.
Besides delivering all the benefits I’ve described, the IIAS also offers:
- Simplified system that is elastic and reduces management requirements
- A path to Cloud
- A Common SQL Engine for workloads in the cloud, on premise, or both
- Federation of queries that require access to structured and unstructured data
- Machine learning and the IBM Data Science Experience
- Support of analytical and transactional workloads
The LRS Big Data and Analytics team has 20 years of experience in analytics and data warehousing, and have sold and implemented appliances such as IIAS for over 10 years. We can help you understand how an appliance can accelerate your time to business value, reduce complexity, and improve the economics of analytics.
About the author
Steve Cavolick is a Senior Solution Architect with LRS IT Solutions. With over 20 years of experience in enterprise business analytics and information management, Steve is 100% focused on helping customers find value in their data to drive better business outcomes. Using technologies from best-of-breed vendors, he has created solutions for the retail, telco, manufacturing, distribution, financial services, gaming, and insurance industries.