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Basho was on my (very short) blacklist of companies with whom I refuse to speak, because they have lied about the contents of previous conversations. But Tony Falco et al. are long gone from the company. So when Basho’s new management team reached out, I took the meeting.
- Basho management turned over significantly 1-2 years ago. The main survivors from the old team are 1 each in engineering, sales, and services.
- Basho moved its headquarters to Bellevue, WA. (You get one guess as to where the new CEO lives.) Engineering operations are very distributed geographically.
- Basho claims that it is much better at timely product shipments than it used to be. Its newest product has a planned (or at least hoped-for) 8-week cadence for point releases.
- Basho’s revenue is ~90% subscription.
- Basho claims >200 enterprise clients, vs. 100-120 when new management came in. Unfortunately, I forgot to ask the usual questions about divisions vs. whole organizations, OEM sell-through vs. direct, etc.
- Basho claims an average contract value of >$100K, typically over 2-3 years. $9 million of that (which would be close to half the total, actually), comes from 2 particular deals of >$4 million each.
Basho’s product line has gotten a bit confusing, but as best I understand things the story is:
- There’s something called Riak Core, which isn’t even a revenue-generating product. However, it’s an open source project with some big users (e.g. Goldman Sachs, Visa), and included in pretty much everything else Basho promotes.
- Riak KV is the key-value store previously known as Riak. It generates the lion’s share of Basho’s revenue.
- Riak S2 is an emulation of Amazon S3. Basho thinks that Riak KV loses efficiency when objects get bigger than 1 MB or so, and that’s when you might want to use Riak S2 in addition or instead.
- Riak TS is for time series, and just coming out now.
- Also in the mix are some (extra charge) connectors for Redis and Spark. Presumably, there are more of these to come.
- There’s an umbrella marketing term of “Basho Data Platform”.
Technical notes on some of that include: Read more
|Categories: Aerospike, Basho and Riak, Cassandra, Clustering, Couchbase, Databricks, Spark and BDAS, DataStax, HBase, Health care, Log analysis, MapR, Market share and customer counts, MongoDB, NoSQL, Pricing, Specific users, Splunk||Leave a Comment|
I last wrote about Couchbase in November, 2012, around the time of Couchbase 2.0. One of the many new features I mentioned then was secondary indexing. Ravi Mayuram just checked in to tell me about Couchbase 4.0. One of the important new features he mentioned was what I think he said was Couchbase’s “first version” of secondary indexing. Obviously, I’m confused.
Now that you’re duly warned, let me remind you of aspects of Couchbase timeline.
- 2 corporate name changes ago, Couchbase was organized to commercialize memcached. memcached, of course, was internet companies’ default way to scale out short-request processing before the rise of NoSQL, typically backed by manually sharded MySQL.
- Couchbase’s original value proposition, under the name Membase, was to provide persistence and of course support for memcached. This later grew into a caching-oriented pitch even to customers who weren’t already memcached users.
- A merger with the makers of CouchDB ensued, with the intention of replacing Membase’s SQLite back end with CouchDB at the same time as JSON support was introduced. This went badly.
- By now, however, Couchbase sells for more than distributed cache use cases. Ravi rattled off a variety of big-name customer examples for system-of-record kinds of use cases, especially in session logging (duh) and also in travel reservations.
- Couchbase 4.0 has been in beta for a few months.
Technical notes on Couchbase 4.0 — and related riffs — start: Read more
|Categories: Cache, Clustering, Couchbase, Data models and architecture, Databricks, Spark and BDAS, Exadata, Hadoop, MarkLogic, MongoDB, MySQL, NoSQL, Open source, Schema on need, Structured documents, Web analytics||1 Comment|
Don’t plan to fish in your personal data lake.
Perhaps the biggest mess in all of IT is the management of individual consumers’ data. Our electronic data is thoroughly scattered. Most individual portions are poorly managed. There’s no integration. The data that’s on paper is even worse. For example:
- Do you have access to your medical records? Do you even know when you were last vaccinated for what?
- Several enterprises have comprehensive records of all your credit card purchases, in easy-to-analyze form. Do you have such records too?
- How easily can you find old emails? How about old paper correspondence?
For the most part, the technology community is barely trying to solve those problems. But even when it does try, success is mixed at best. For example:
- People generally hate iCloud and iTunes. Even usability icon Apple can’t get its data management apps right.
- Intuit is divesting flagship product Quicken.
- Evernote seems to be in trouble, although there are evidently execution issues involved.
- How is Dropbox’s consumer business doing, especially from a revenue standpoint?
And those are some of the most successful names.
There are numerous reasons for this dismal state of affairs. Read more
- My client Rocana is the renamed ScalingData, where Rocana is meant to signify ROot Cause ANAlysis.
- Rocana was founded by Omer Trajman, who I’ve referenced numerous times in the past, and who I gather is a former boss of …
- … cofounder Eric Sammer.
- Rocana recently told me it had 35 people.
- Rocana has a very small number of quite large customers.
Rocana portrays itself as offering next-generation IT operations monitoring software. As you might expect, this has two main use cases:
- Actual operations — figuring out exactly what isn’t working, ASAP.
Rocana’s differentiation claims boil down to fast and accurate anomaly detection on large amounts of log data, including but not limited to:
- The sort of network data you’d generally think of — “everything” except packet-inspection stuff.
- Firewall output.
- Database server logs.
- Point-of-sale data (at a retailer).
- “Application data”, whatever that means. (Edit: See Tom Yates’ clarifying comment below.)
|Categories: Business intelligence, Hadoop, Log analysis, Market share and customer counts, Petabyte-scale data management, Predictive modeling and advanced analytics, Pricing, Rocana, Splunk, Web analytics||1 Comment|
A lot of what I hear and talk about boils down to “data is a mess”. Below is a very partial list of examples.
To a first approximation, one would expect operational data to be rather clean. After all, it drives and/or records business transactions. So if something goes awry, the result can be lost money, disappointed customers, or worse, and those are outcomes to be strenuously avoided. Up to a point, that’s indeed true, at least at businesses large enough to be properly automated. (Unlike, for example — — mine.)
Even so, operational data has some canonical problems. First, it could be inaccurate; somebody can just misspell or otherwise botch an entry. Further, there are multiple ways data can be unreachable, typically because it’s:
- Inconsistent, in which case humans might not know how to look it up and database JOINs might fail.
- Unintegrated, in which case one application might not be able to use data that another happily maintains. (This is the classic data silo problem.)
Inconsistency can take multiple forms, including: Read more
Occasionally I talk with an astute reporter — there are still a few left — and get led toward angles I hadn’t considered before, or at least hadn’t written up. A blog post may then ensue. This is one such post.
There is a group of questions going around that includes:
- Is Hadoop overhyped?
- Has Hadoop adoption stalled?
- Is Hadoop adoption being delayed by skills shortages?
- What is Hadoop really good for anyway?
- Which adoption curves for previous technologies are the best analogies for Hadoop?
To a first approximation, my responses are: Read more
|Categories: Application areas, Data warehousing, Databricks, Spark and BDAS, EAI, EII, ETL, ELT, ETLT, Hadoop, Hortonworks, MapR, MapReduce, Market share and customer counts, Open source, Pricing||6 Comments|
It’s difficult to project the rate of IT change in health care, because:
- Health care is suffused with technology — IT, medical device and biotech alike — and hence has the potential for rapid change. However, it is also the case that …
- … health care is heavily bureaucratic, political and regulated.
Timing aside, it is clear that health care change will be drastic. The IT part of that starts with vastly comprehensive electronic health records, which will be accessible (in part or whole as the case may be) by patients, care givers, care payers and researchers alike. I expect elements of such records to include:
- The human-generated part of what’s in ordinary paper health records today, but across a patient’s entire lifetime. This of course includes notes created by doctors and other care-givers.
- Large amounts of machine-generated data, including:
- The results of clinical tests. Continued innovation can be expected in testing, for reasons that include:
- Most tests exploit electronic technology. Progress in electronics is intense.
- Biomedical research is itself intense.
- In particular, most research technologies (for example gene sequencing) can be made cheap enough over time to be affordable clinically.
- The output of consumer health-monitoring devices — e.g. Fitbit and its successors. The buzzword here is “quantified self”, but what it boils down to is that every moment of our lives will be measured and recorded.
- The results of clinical tests. Continued innovation can be expected in testing, for reasons that include:
These vastly greater amounts of data cited above will allow for greatly changed analytics.
I talked with my clients at MemSQL about the release of MemSQL 4.0. Let’s start with the reminders:
- MemSQL started out as in-memory OTLP (OnLine Transaction Processing) DBMS …
- … but quickly positioned with “We also do ‘real-time’ analytic processing” …
- … and backed that up by adding a flash-based column store option …
- … before Gartner ever got around to popularizing the term HTAP (Hybrid Transaction and Analytic Processing).
- There’s also a JSON option.
The main new aspects of MemSQL 4.0 are:
- Geospatial indexing. This is for me the most interesting part.
- A new optimizer and, I suppose, query planner …
- … which in particular allow for serious distributed joins.
- Some rather parallel-sounding connectors to Spark. Hadoop and Amazon S3.
- Usual-suspect stuff including:
- More SQL coverage (I forgot to ask for details).
- Some added or enhanced administrative/tuning/whatever tools (again, I forgot to ask for details).
- Surely some general Bottleneck Whack-A-Mole.
There’s also a new free MemSQL “Community Edition”. MemSQL hopes you’ll experiment with this but not use it in production. And MemSQL pricing is now wholly based on RAM usage, so the column store is quasi-free from a licensing standpoint is as well.
|Categories: Amazon and its cloud, Columnar database management, Databricks, Spark and BDAS, GIS and geospatial, Hadoop, Investment research and trading, Market share and customer counts, MemSQL, NewSQL, Pricing, Structured documents||8 Comments|
1. There are multiple ways in which analytics is inherently modular. For example:
- Business intelligence tools can reasonably be viewed as application development tools. But the “applications” may be developed one report at a time.
- The point of a predictive modeling exercise may be to develop a single scoring function that is then integrated into a pre-existing operational application.
- Conversely, a recommendation-driven website may be developed a few pages — and hence also a few recommendations — at a time.
Also, analytics is inherently iterative.
- Everything I just called “modular” can reasonably be called “iterative” as well.
- So can any work process of the nature “OK, we got an insight. Let’s pursue it and get more accuracy.”
If I’m right that analytics is or at least should be modular and iterative, it’s easy to see why people hate multi-year data warehouse creation projects. Perhaps it’s also easy to see why I like the idea of schema-on-need.
2. In 2011, I wrote, in the context of agile predictive analytics, that
… the “business analyst” role should be expanded beyond BI and planning to include lightweight predictive analytics as well.
I gather that a similar point is at the heart of Gartner’s new term citizen data scientist. I am told that the term resonates with at least some enterprises. Read more
|Categories: Business intelligence, Data warehousing, Datameer, Hadoop, Log analysis, Oracle, Platfora, Predictive modeling and advanced analytics, SAS Institute, Software as a Service (SaaS), Tableau Software, Web analytics||2 Comments|
I’m going to be out-of-sorts this week, due to a colonoscopy. (Between the prep, the procedure, and the recovery, that’s a multi-day disablement.) In the interim, here’s a collection of links, quick comments and the like.
1. Are you an engineer considering a start-up? This post is for you. It’s based on my long experience in and around such scenarios, and includes a section on “Deadly yet common mistakes”.
2. There seems to be a lot of confusion regarding the business model at my clients Databricks. Indeed, my own understanding of Databricks’ on-premises business has changed recently. There are no changes in my beliefs that:
- Databricks does not directly license or support on-premises Spark users. Rather …
- … it helps partner companies to do so, where:
- Examples of partner companies include usual-suspect Hadoop distribution vendors, and DataStax.
- “Help” commonly includes higher-level support.
However, I now get the impression that revenue from such relationships is a bigger deal to Databricks than I previously thought.
Databricks, by the way, has grown to >50 people.
3. DJ Patil and Ruslan Belkin apparently had a great session on lessons learned, covering a lot of ground. Many of the points are worth reading, but one in particular echoed something I’m hearing lots of places — “Data is super messy, and data cleanup will always be literally 80% of the work.” Actually, I’d replace the “always” by something like “very often”, and even that mainly for newish warehouses, data marts or datasets. But directionally the comment makes a whole lot of sense.
|Categories: Data integration and middleware, Databricks, Spark and BDAS, DataStax, Hadoop, Health care, Investment research and trading, Text||Leave a Comment|