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High-Performance Data Warehousing: An Overview in 34 Tweets

Blog by Philip Russom
Research Director for Data Management, TDWI

To help you better understand High-Performance Data Warehousing (HiPerDW) and why you should care about it, I’d like to share with you the series of 34 tweets I recently issued. I think you’ll find the tweets interesting, because they provide an overview of HiPerDW in a form that’s compact, yet amazingly comprehensive.

Every tweet I wrote was a short sound bite or stat bite drawn from my recent TDWI report on HiPerDW. Many of the tweets focus on a statistic cited in the report, while other tweets are definitions stated in the report.

I left in the arcane acronyms, abbreviations, and incomplete sentences typical of tweets, because I think that all of you already know them or can figure them out. Even so, I deleted a few tiny URLs, hashtags, and repetitive phrases. I issued the tweets in groups, on related topics; so I’ve added some headings to this blog to show that organization. Otherwise, these are raw tweets.

Defining High-Performance Data Warehousing (#HiPerDW)
1. The 4 dimensions of High-Performance Data Warehousing (#HiPerDW): speed, scale, complexity, concurrency.
2. High-performance data warehousing (#HiPerDW) achieves speed & scale, despite complexity & concurrency.
3. #HiPerDW 4 dimensions relate. Scaling requires speed. Complexity & concurrency inhibit speed & scale.
4. High-performance data warehousing (#HiPerDW) isn't just DW. #BizIntel, #DataIntegration & #Analytics must also perform.
5. Common example of speed via high-performance data warehousing (#HiPerDW) = #RealTime for #OperationalBI.
6. A big challenge to high-performance data warehousing (#HiPerDW) = Scaling up or out to #BigData volumes.
7. Growing complexity & diversity of sources, platforms, data types, & architectures challenge #HiPerDW.
8. Increasing concurrency of users, reports, apps, #Analytics, & multiple workloads also challenge #HiPerDW.

HiPerDW Makes Many Applications Possible
9. High-performance data warehousing (#HiPerDW) enables fast-paced, nimble, competitive biz practices.
10. Extreme speed/scale of #BigData #Analytics requires extreme high-performance warehousing (#HiPerDW).
11. #HiPerDW enables #OperationalBI, just-in-time inv, biz monitor, price optimiz, fraud detect, mobile mgt.

HiPerDW is An Opportunity
12. #TDWI SURVEY SEZ: High-performance data warehousing (#HiPerDW) is mostly opportunity (64%); sometimes problem (36%).
13. #HiPerDW is an opportunity because it enables new, broader and faster data-driven business practices.
14. #TDWI SURVEY SEZ: 66% say High Perf #DataWarehousing (#HiPerDW) is extremely important. 6% find it a non-issue.
15. #TDWI SURVEY SEZ: Most performance improvements are responses to biz demands, growth, or slow tools.

New Options for HiPerDW
16. Many architectures support High-Perf #DataWarehousing (#HiPerDW): MPP, grids, clusters, virtual, clouds.
17. #HiPerDW depends on #RealTime functions for: streaming data, buses, SOA, event processing, in-memory DBs.
18. Many hardware options support #HiPerDW: big memory, multi-core CPUs, Flash memory, solid-state drives.
19. Innovations for Hi-Perf #DataWarehousing (#HiPerDW) = appliance, columnar, #Hadoop, #MapReduce, InDB #Analytics.
20. Vendor tools are indispensible, but #HiPerDW still requires optimization, tweaks & tuning by tech users.

Benefits and Barriers for HiPerDW
21. #TDWI SURVEY SEZ: Any biz process or tech that’s #Analytics, #RealTime or data-driven benefits from #HiPerDW.
22. #TDWI SURVEY SEZ: Biggest barriers to #HiPerDW are cost, tool deficiencies, inadequate skills, & #RealTime.

Replacing DW to Achieve HiPerDW
23. #TDWI SURVEY SEZ: 1/3 of users will replace DW platform within 3 yrs to boost performance. #HiPerDW
24. #TDWI SURVEY SEZ: Top reason to replace #EDW is scalability. Second reason is speed. #HiPerDW
25. #TDWI SURVEY SEZ: The number of analytic datasets in 100-500+ terabyte ranges will triple. #HiPerDW

HiPerDW Best Practices
26. #TDWI SURVEY SEZ: 61% their top High-Performance DW method (#HiPerDW) is ad hoc tweaking & tuning.
27. #TDWI SURVEY SEZ: Bad news: Tweaking & tuning for #HiPerDW keeps developers from developing.
28. #TDWI SURVEY SEZ: Good news: Only 9% spend half or more of time tweaking & tuning for #HiPerDW.
29. #TDWI SURVEY SEZ: #HiPerDW methods: remodeling data, indexing, revising SQL, hardware upgrade.
30. BI/DW team is responsible for high-performance data warehousing (#HiPerDW), then IT & architects.

HiPerDW Options that will See Most Growth
31. #HiPerDW priorities for hardware = server memory, computing architecture, CPUs, storage.
32. #TDWI SURVEY SEZ: In-database #Analytics will see greatest 3-yr adoption among #HiPerDW functions.
33. #TDWI SURVEY SEZ: Among High-Perf #DataWarehouse functions (#HiPerDW), #RealTime ones see most adoption.
34. #TDWI SURVEY SEZ: In-memory databases will also see strong 3-yr growth among #HiPerDW functions.

FOR FURTHER STUDY:
For a more detailed discussion of High-Performance Data Warehousing (HiPerDW) – in a traditional publication! – see the TDWI Best Practices Report, titled “High-Performance Data Warehousing,” which is available in a PDF file via download.

You can also register for and replay my TDWI Webinar, where I present the findings of the TDWI report on High-Performance Data Warehousing (HiPerDW).

If you're not already, please follow me as @prussom on Twitter.

Posted by Philip Russom, Ph.D. on October 26, 2012


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