The Top 9 Data Warehousing Trends of the Future
It’s the most sophisticated form of data management in the IT house, and it’s about to get even bigger.
In fact, data warehousing is now labeled “mission-critical” by Gartner analysts. The data warehouse is
expected to remain the key component of the IT infrastructure, “one of the largest—if not the largest—information
repository in the enterprise.” Here are the top 9 data warehousing trends of the future.
1. Optimization and Performance
Data warehousing will use optimization and performance as a differentiator in addition to
focusing on the issue of optimizing storage for warehouses via compression and usage-based
data placement strategies.
2. Data Warehouse Appliances
Appliances are the next big thing – mostly because of their simplicity. The vendor builds and
certifies the configuration, balancing hardware, software and services for a predictable performance.
Appliances also install rapidly. They also can speed delivery by avoiding time-consuming hardware balancing.
3. The Intensive POC
Gartner recommends that POCs (proof of concept) use as much real source-system extracted data (SSED)
from the operational systems as possible, while performing the POC with as many users as possible,
creating a data warehouse workload that approaches that of the environment to be used in production.
4. Data Warehouse Mixed Workloads
There are six workloads that are delivered by the data warehouse platform: bulk/batch load, basic reporting,
basic online analytical processing (OLAP), real-time/continuous load, data mining and operational BI. Warehouses
delivering all six workloads need to be assessed for predictability of mixed workload performance as failing to
plan for mixed workloads will lead to increased administration costs over time, as volume and additional workloads
are added, potentially leading to major sustainability issues.
5. The Resurgence of Data Marts
Data mart usage will increase throughout 2011 and 2012 due to their effectiveness in optimizing the
warehouse environment by offloading part of the workload to the data mart.
6. Column-Store DBMSs (Database Management System)
Column-store DBMSs generally exhibit faster query response than traditional, row-based systems and can serve as
excellent data mart platforms, and even as a main data warehouse platform.
7. In-Memory DBMSs (Database Management System)
Not only do in-memory DBMSs deliver fast query responses, they introduce a higher probability that
analytics and transactional systems can share the same database. Analytic data models, master data
approaches and data services within a middle tier will begin to emerge as the dominant approach,
forcing more traditional row-based vendors to adapt to column approaches and in-memory simultaneously.
8. Data Warehouse as a Service and Cloud
According to Gartner, in 2011, data warehouse as a service comes in two "flavors" — software as a
service (SaaS) and outsourced data warehouses. Data warehouse in the cloud is primarily an infrastructure
design option as a data model must still be developed, an integration strategy must be deployed and BI user
access must be enabled and managed. Private clouds are an emerging infrastructure design choice for some
organizations in supporting their data warehouse and analytics.
9. Using an Open-Source DBMS to Deploy the Data Warehouse
Gartner states that this particular trend still remains in the experimental stage. At this point, open-source
warehouses are rare and usually smaller than traditional ones and also generally require a more manual level
of support. However, some solutions are optimized specifically for data warehousing.
Gartner analysts also predicted that data warehouse DBMS vendors will combine their offerings to become more of
an information management platform. By 2013, Gartner sees DBMS supporting and performing data management and