facebooklinkedinrsstwitterBlogAsset 1PRDatasheetDatasheetAsset 1DownloadForumGuideLinkWebinarPRPresentationRoad MapVideofacebooklinkedinrsstwitterBlogAsset 1PRDatasheetDatasheetAsset 1DownloadForumGuideLinkWebinarPRPresentationRoad MapVideo
Actian Blog / The Top 10 Benefits of an Operational Data Warehouse For 2021

The Top 10 Benefits of an Operational Data Warehouse For 2021

Optimization 01 Blogsized

The previous blogs in this series discussed the top 5 pitfalls of traditional operational data warehouses and defined the Operational Data Warehouse (ODW) as a potential solution.  Below is a list of my Top 10 desirable benefits of an effective ODW:

Current. Continuous data updates via “micro-batches” or streamed singleton updates throughout the day provide the most current information for analytics-based decision-making.

Fast. Changes to ODW data need to be made with the lowest performance penalty. Columnar data blocks that maintain their min-max value metadata eliminate the overhead of creating indexes that need to be updated with every change, as traditional row-based databases do. The ability to make better business decisions faster can translate into multiple data warehouse benefits.

Scalable. An effective enterprise data warehouse must be scalable in two dimensions. Vertical scalability enables workloads to take advantage of more CPU and storage capacity on a single system. When you have saturated the hardware capacity of a single system, the ability to scale horizontally to a cluster of systems provides the ability to grow the ODW to handle larger databases and more users. The ability to increase capacity as demand grows is a key advantage of a modern data warehouse.

Secure. The explosive growth of cybercrime and increased regulation of data privacy means that even “internal” systems must be secured. A good ODW must offer built-in support for advanced encryption, auditing, role-based security and data masking.

Flexible. The days when an organization could standardize on a single computing platform are over. The ODW needs to offer the flexibility to be deployed on-premises (on Linux, Windows, or Hadoop Clusters) or in the cloud (on AWS, Microsoft Azure and beyond).

Consistent. Some databases sacrifice query integrity for speed. A good ODW needs to provide row-level locking and full read consistency for running queries even as the underlying data changes.

Robust. A key advantage of a modern data warehouse is the ability to deliver enterprise-level resiliency and manageability. This translates to having an ODW with solid back-up, recovery, failover and replication capabilities.

Economical. Several factors can affect the total cost of ownership (TCO) for a specific database technology being used to support a particular business case. One is the ability to run standard servers to avoid esoteric appliances. Others include offering flexible deployment models to match different business needs, flexibility to scale up and down according to performance requirements, and the option to use different sized components (compute, storage) to optimize operating efficiencies.

Interoperable. A good ODW needs to provide open application programming interfaces (APIs) such as those that support Open Database Connectivity (ODBC) and American National Standards Institute Structured Query Language (ANSI SQL). These are necessary to enable the data warehouse to work with the multitude of query tools an organization might use. Many organizations use more than 20 different visualization and query tools.

Connected. The ability to ingest data at high speed is a critical ODW requirement. If you cannot load your data in a reasonable time, the result is having to work with summary data or worse, using stale data.

I would be very interested to hear which benefits you value the most or others I could have included? Email me at Pradeep.bhanot@actian.com if you would like to share your views.

About Pradeep Bhanot

Product Marketing professional, author, father and photographer. Born in Kenya. Lived in England through disco, punk and new romance eras. Moved to California just in time for grunge. Worked with Oracle databases at Oracle Corporation for 13 years. Database Administration for mainframe IBM DB2 and its predecessor SQL/DS at British Telecom and Watson Wyatt. Worked with IBM VSAM at CA Technologies and Serena Software. Microsoft SQL Server powered solutions from 1E and BDNA.