The enterprise data warehouse of the future
Though the enterprise data warehouse (EDW) has traditionally been the repository for historical data such as sales and financials, it is quickly evolving to meet the demands of new technologies.
These include artificial intelligence (AI), Internet of Things (IoT), mobile and social, which continue to drive greater data volume, velocity and variety. In addition, there is a growing need to drive near-real-time decision-making to advance predictive analytics, machine learning and data science.
Another way EDWs are meeting these needs is through disaster recovery solutions previously used to protect data from planned and unplanned outages and maintain regulatory compliance. These are being augmented with data replication, which helps meet analytical demands with continuous availability of data.
What has changed in the world of data
For most organizations, online transaction processing (OLTP), characterized by the high volume/concurrency and the low latency of shipping, billing, and customer relationship management (CRM), will not slow down.
What is changing is the need for online analytic processing (OLAP) that provides powerful technology for data discovery, facilitating business intelligence (BI), complex analytic calculations and predictive analytics. One of the main benefits of OLAP is the consistency of information and calculations it uses to drive data from machine learning to improve product quality, customer interactions and process improvements. Most organizations will eventually require support both OLTP and OLAP.
HTAP brings together OLTP + OLAP
For that reason, many are turning to hybrid transaction/analytic processing (HTAP) 1, a term coined by Gartner to describe an emerging application architecture that breaks the wall between OLTP and OLAP. This new architecture enables more informed and near-real-time decisions by incorporating the two in a single database.
Moreover, HTAP architectures go beyond the passive data copies used for OLTP failovers by enabling continuous availability. In this way, HTAP can satisfy not only disaster recovery, but high availability and workload balancing to support active applications as well.
Benefits of data replication and continuous availability for HTAP
IBM sees the value in an HTAP architecture with continuous availability and is using it in the EDW to help clients in industries including finance, healthcare and retail adopt leaner business processes, accelerate analytic insights and curtail disruptions to operations. Businesses could also see benefits from data replication and continuous availability such as the ability to:
- Reduce concurrency by offloading operational workloads to replica servers. Deliver warehouse augmentation, shift workloads to one or more replicate servers, increase data accessibility, improve operations and lower overall costs.
- Satisfy operational needs associated with BI, reporting and data science activities. Continuously replacing data on replica servers allows near-real-time data to be used for up-to-date-insights from line-of-business (LOB) users and data scientists using deep learning and predictive analytics.
- Address industry and organizational regulatory and compliance requirements. Replication also helps create a secure, accurate, and accessible, near real-time archive for systems of record to help comply with GDPR, HIPPA, PCI DSS, FINRA and BCS guidelines.
- Ensure availability of critical data in the event of a catastrophe. In the unfortunate event of a major data center outage or catastrophe, fast data recovery with minimal data loss is possible. This is achieved by replicating schemas and tables for critical data associated with Tier 1 applications to disaster recovery sites.
- Reduce outage windows for planned and unplanned events. With continuous availability, data is available even during times when installations, upgrades and planned or unplanned maintenance is taking place.
IAS brings together data replication, continuous availability and HTAP
To make these benefits more accessible, IBM Data Replication for Continuous Availability is embedded in the IBM Integrated Analytics System (IAS) and available for both current and new users. It supports active-RW standby and both row and columnar based tables (such as data loads).
The world-class Q Replication technology also provides new streaming replication with low latency. This software-based replication supports active and stand-by replicas for workload balancing, shifting workloads during planned outages while also dramatically reducing the time to recovery for unplanned outages.
The IBM Integrated Analytic System is designed to help you get started quickly with a 90-day “try it now” license. Discover for yourself how data replication and continuous availability can help you get the most out of your analytics.
1 Gartner, “Hybrid Transaction/Analytic Process Will Foster Opportunities for Dramatic Business Innovation”, Massimo Pezzini, Donald Feinberg, Nigel Rayner and Roxane Edjlali, April 28th, 2015