October 30, 2013
Splunk strengthens its stack
I’m a little shaky on embargo details — but I do know what was in my own quote in a Splunk press release that went out yesterday. 🙂
Splunk has been rolling out a lot of news. In particular:
- Hunk follows through on the Hadoop/Splunk (get it?) co-opetition I foreshadowed last year, including access to Hadoop via the same tools that run over the Splunk data store, plus …
- … some Datameer-like capabilities to view partial Hadoop-job results as they flow in.
- Splunk 6 has lots of new features, including a bunch of better please-don’t-call-it-BI capabilities, and …
- … a high(er)-performance data store into which you can selectively copy columns of data.
I imagine there are some operationally-oriented use cases for which Splunk instantly offers the best Hadoop business intelligence choice available. But what I really think is cool is Splunk’s schema-on-need story, wherein:
- Data comes in wholly schema-less, in a time series of text snippets.
- Some of the fields in the text snippets are indexed for faster analysis, automagically or upon user decree.
- All this can now happen over the Splunk data store or (new option) over Hadoop.
- Fields can (in another new option) also be copied to a separate data store, claimed to be of much higher performance.
That highlights a pretty serious and flexible vertical analytic stack. I like it.
Categories: Business intelligence, Data models and architecture, Data warehousing, Hadoop, Schema on need, Splunk
Subscribe to our complete feed!
Comments
2 Responses to “Splunk strengthens its stack”
Leave a Reply
[…] Splunk is focused on machine-generated data, which can arrive in a steady stream (e.g. into Splunk’s own data store) or perhaps in batches (e.g. into Hadoop). Splunk’s BI tools are focused on questions like “What’s actually going on in our machines?” […]
[…] Splunk, of course, has a complete stack. At the data acquisition and parsing layers, it’s second to none, and it has a considerable set of log-appropriate BI capabilities as well. And for data management it in effect is stitching together two different inverted-list data stores, plus Hadoop. […]