Analytic technologies
Discussion of technologies related to information query and analysis. Related subjects include:
- Business intelligence
- Data warehousing
- (in Text Technologies) Text mining
- (in The Monash Report) Data mining
- (in The Monash Report) General issues in analytic technology
Hoping for true columnar storage in Oracle12c
I was asked to clarify one of my July comments on Oracle12c,
I wonder whether Oracle will finally introduce a true columnar storage option, a year behind Teradata. That would be the obvious enhancement on the data warehousing side, if they can pull it off. If they can’t, it’s a damning commentary on the core Oracle codebase.
by somebody smart who however seemed to have half-forgotten my post comparing (hybrid) columnar compression to (hybrid) columnar storage.
In simplest terms:
- Columnar storage and columnar compression are two different things. The main connections are:
- Columnar storage can make columnar compression more effective.
- In different ways, both technologies reduce I/O.
- EMC Greenplum, Teradata Aster, and Teradata Classic are all originally row-based systems that have gone hybrid columnar.
- Vertica is an originally column-based system that has gone hybrid columnar.
| Categories: Aster Data, Columnar database management, Data warehousing, Database compression, Greenplum, Oracle, Teradata, Vertica Systems | 4 Comments |
When should analytics be in-memory?
I was asked today for rules or guidance regarding “analytical problems, situations, or techniques better suited for in-database versus in-memory processing”. There are actually two kinds of distinction to be drawn:
- Some workloads, in principle, should run on data to which there’s very fast and unfettered access — so fast and unfettered that you’d love the whole data set to be in RAM. For others, there is little such need.
- Some products, in practice, are coupled to specific in-memory data stores or to specific DBMS, even though other similar products don’t make the same storage assumptions.
Let’s focus on the first part of that — what work, in principle, should be done in memory? Read more
| Categories: Business intelligence, Data warehousing, Memory-centric data management, Parallelization, Predictive modeling and advanced analytics | 2 Comments |
Notes on Hadoop adoption
I successfully resisted telephone consulting while on vacation, but I did do some by email. One was on the oft-recurring subject of Hadoop adoption. I think it’s OK to adapt some of that into a post.
Notes on past and current Hadoop adoption include:
- Enterprise Hadoop adoption is for experimental uses or departmental production (as opposed to serious enterprise-level production). Indeed, it’s rather tough to disambiguate those two. If an enterprise uses Hadoop to search for new insights and gets a few, is that an experiment that went well, or is it production?
- One of the core internet-business use cases for Hadoop is a many-step ETL, ELT, and data refinement pipeline, with Hadoop executing some or many of the steps. But I don’t think that’s in production at many enterprises yet, except in the usual forward-leaning sectors of financial services and (we’re all guessing) national intelligence.
- In terms of industry adoption:
- Financial services on the investment/trading side are all over Hadoop, just as they’re all over any technology. Ditto national intelligence, one thinks.
- Consumer financial services, especially credit card, are giving Hadoop a try too, for marketing and/or anti-fraud.
- I’m sure there’s some telecom usage, but I’m hearing of less than I thought I would. Perhaps this is because telcos have spent so long optimizing their data into short, structured records.
- Whatever consumer financial services firms do, retailers do too, albeit with smaller budgets.
Thoughts on how Hadoop adoption will look going forward include: Read more
| Categories: Cloud computing, Data warehouse appliances, Data warehousing, EAI, EII, ETL, ELT, ETLT, Hadoop, Investment research and trading, Telecommunications | 3 Comments |
Integrated internet system design
What are the central challenges in internet system design? We probably all have similar lists, comprising issues such as scale, scale-out, throughput, availability, security, programming ease, UI, or general cost-effectiveness. Screw those up, and you don’t have an internet business.
Much new technology addresses those challenges, with considerable success. But the success is usually one silo at a time — a short-request application here, an analytic database there. When it comes to integration, unsolved problems abound.
The top integration and integration-like challenges for me, from a practical standpoint, are:
- Integrating silos — a decades-old problem still with us in a big way.
- Dynamic schemas with joins.
- Low-latency business intelligence.
- Human real-time personalization.
Other concerns that get mentioned include:
- Geographical distribution due to privacy laws, which for some users is a major requirement for compliance.
- Logical data warehouse, a term that doesn’t actually mean anything real.
- In-memory data grids, which some day may no longer always be hand-coupled to the application and data stacks they accelerate.
Let’s skip those latter issues for now, focusing instead on the first four.
In-database analytics — analytic glossary draft entry
This is a draft entry for the DBMS2 analytic glossary. Please comment with any ideas you have for its improvement!
Note: Words and phrases in italics will be linked to other entries when the glossary is complete.
“In-database analytics” is a catch-all term for analytic capabilities, beyond standard SQL, running on the same machine as and under the management of an analytic DBMS. These can run in one or both of two modes:
- In-process or unfenced, i.e. in the same process as the DBMS itself. This option gives maximum performance, but any defects in the analytic code may crash the whole DBMS. Also, it generally requires that the code be in the same language as the DBMS, i.e. C++.
- Out-of-process or fenced, i.e. in a separate process. This option sacrifices performance, in favor of reliability and language flexibility.
In-database analytics may offer great performance and scalability advantages versus the alternative of extracting data and having it be processed on a separate server. This is particularly likely to be the case in MPP (Massively Parallel Processing) analytic DBMS environments.
Examples of in-database analytics include:
- Creating temporary data structures that persist past the life of a query.
- Creating temporary data structures that are non-tabular.
- Predictive modeling that uses all the same nodes in an MPP cluster where the data resides.
- Predictive analytics (scoring only).
Other common domains for in-database analytics include sessionization, time series analysis, and relationship analytics.
Notable products offering in-database analytics include:
- Teradata Aster SQL/MR.
- Multiple other analytic platforms, such as Sybase IQ, Vertica, or IBM Netezza. Indeed, in-database analytics are a defining feature of analytic platforms.
- Fuzzy Logix (for predictive analytics).
| Categories: Analytic glossary, Aster Data, Data warehousing, IBM and DB2, MapReduce, Netezza, Parallelization, Predictive modeling and advanced analytics, Sybase, Teradata, Vertica Systems | 8 Comments |
Analytic platform — analytic glossary draft entry
This is a draft entry for the DBMS2 analytic glossary. Please comment with any ideas you have for its improvement!
Note: Words and phrases in italics will be linked to other entries when the glossary is complete.
In our usage, an “analytic platform” is an analytic DBMS with well-integrated in-database analytics, or a data warehouse appliance that includes one. The term is also sometimes used to refer to:
- Any analytic DBMS or data warehouse appliance.
- Other kinds of software, or software/hardware combination, that support broad analytic capabilities.
To varying extents, most major vendors of analytic DBMS or data warehouse appliances have extended their products into analytic platforms; see, for example, our original coverage of analytic platform versions of as Aster, Netezza, or Vertica.
Related posts
- Our original definition of “analytic platform” (February, 2011)
- Our original feature list for analytic platforms (January, 2011)
| Categories: Analytic glossary, Aster Data, Data warehouse appliances, Data warehousing, Netezza, Vertica Systems | 3 Comments |
Data warehouse appliance — analytic glossary draft entry
This is a draft entry for the DBMS2 analytic glossary. Please comment with any ideas you have for its improvement!
Note: Words and phrases in italics will be linked to other entries when the glossary is complete.
A data warehouse appliance is a combination of hardware and software that includes an analytic DBMS (DataBase Management System). However, some observers incorrectly apply the term “data warehouse appliance” to any analytic DBMS.
The paradigmatic vendors of data warehouse appliances are:
- Teradata, which embraced the term “data warehouse appliance” in 2008.
- Netezza — now an IBM company — which popularized the term “data warehouse appliance” in the 2000s.
Further, vendors of analytic DBMS commonly offer — directly or through partnerships — optional data warehouse appliance configurations; examples include:
- Greenplum, now part of EMC.
- Vertica, now an HP company.
- IBM DB2, under the brand “Smart Analytic System”.
- Microsoft (Parallel Data Warehouse).
Oracle Exadata is sometimes regarded as a data warehouse appliance as well, despite not being solely focused on analytic use cases.
Data warehouse appliances inherit marketing claims from the category of analytic DBMS, such as: Read more
| Categories: Analytic glossary, Data warehouse appliances, Data warehousing, EMC, Exadata, Greenplum, HP and Neoview, IBM and DB2, Microsoft and SQL*Server, Netezza, Oracle, Teradata | 4 Comments |
People’s facility with statistics — extremely difficult to predict
My recent post on broadening the usefulness of statistics presupposed two things about the statistical sophistication of business intelligence tool users:
- It varies a lot.
- In many cases, it isn’t be very high.
Let me now say a little more on the subject. My basic message is — people’s facility with statistics is extremely difficult to predict.
If you DO have to make a point estimate, however, you could do worse than just putting quotation marks around the last four words of that sentence …
Suppose we measure people’s statistical understanding on a 5-point scale:
- People who haven’t clue what a p-value is.
- People who think a p-value of .05 signifies a 95% chance of truth.
- People who know better than that, but who still think that “statistically significant” is pretty close to the same as “true”.
- People who know better yet, but aren’t fluent in using statistical techniques correctly.
- People who are fluent in statistics.
Just knowing somebody’s job description, can you confidently predict their ranking to within, say, +/- 1 point? I suggest you can’t. People differ wildly in general numeracy and in specific statistical knowledge.
Even our guesses about average knowledge may be off, not least because education is changing things. Read more
Integrating statistical analysis into business intelligence
Business intelligence tools have been around for two decades.* In that time, many people have had the idea of integrating statistical analysis into classical BI. Yet I can’t think of a single example that was more than a small, niche success.
*Or four decades, if you count predecessor technologies.
The first challenge, I think, lies in the paradigm. Three choices that come to mind are:
- “I think I may have discovered a new trend or pattern. Tell me, Mr. BI — is the pattern real?”
- “I think I see a trend. Help me, Mr. BI, to launch a statistical analysis.”
- “If you want to do some statistical analysis, here’s a link to fire it up.”
But the first of those approaches requires too much intelligence from the software, while the third requires too much numeracy from the users. So only the second option has a reasonable chance to work, and even that one may be hard to pull off unless vendors focus on one vertical market at a time.
The challenges in full automation start: Read more
Some Vertica 6 features
Vertica 6 was recently announced, and so it seemed like a good time to catch up on Vertica features. The main topics I want to address are:
- External tables and the associated new Hadoop connector.
- Online schema evolution.
- Workload management.
Also:
- I have some tidbits to add to my June, 2011 coverage of Vertica’s analytic functionality.
- I’ll stand for now on my previous coverage of Vertica’s database organization.
In general, the main themes of Vertica 6 appear to be:
- Enterprise/SaaS-friendliness, high uptime, and so on.
- Improved analytic usefulness.
Let’s do the analytic functionality first. Notes on that include:
- Vertica has extended its user-defined function/analytic procedure/whatever functionality to include user-defined load. (Same SDK, different specific classes.)
- One of the languages Vertica supports is R. But for now, parallel R is limited to “Of course, you can run the same functions and procedures on many nodes at once.”
- Based on community activity around bugs and so on, it seems there are users for Vertica’s JSON-based Twitter sentiment analysis plug-in.
I’ll also take this opportunity to expand on something I wrote about a few vendors — including Vertica — at the end of my post on approximate query results. When I probed how customers of Vertica and other RDBMS-based analytic platform vendors used vendor-proprietary advanced analytic SQL and other analytic capabilities, answers included: Read more