A huge fraction of analytics is about monitoring. People rarely want to frame things in those terms; evidently they think “monitoring” sounds boring or uncool. One cost of that silence is that it’s hard to get good discussions going about how monitoring should be done. But I’m going to try anyway, yet again.
Business intelligence is largely about monitoring, and the same was true of predecessor technologies such as green paper reports or even pre-computer techniques. Two of the top uses of reporting technology can be squarely described as monitoring, namely:
- Watching whether trends are continuing or not.
- Seeing if there are any events — actual or impending as the case may be — that call for response, in areas such as:
- Machine breakages (computer or general metal alike).
- Resource shortfalls (e.g. various senses of “inventory”).
Yes, monitoring-oriented BI needs investigative drilldown, or else it can be rather lame. Yes, purely investigative BI is very important too. But monitoring is still the heart of most BI desktop installations.
Predictive modeling is often about monitoring too. It is common to use statistics or machine learning to help you detect and diagnose problems, and many such applications have a strong monitoring element.
I.e., you’re predicting trouble before it happens, when there’s still time to head it off.
As for incident response, in areas such as security — any incident you respond to has to be noticed first Often, it’s noticed through analytic monitoring.
Hopefully, that’s enough of a reminder to establish the great importance of analytics-based monitoring. So how can the practice be improved? At least three ways come to mind, and only one of those three is getting enough current attention.
0. Matt Brandwein of Cloudera briefed me on the new Cloudera Data Science Workbench. The problem it purports to solve is:
- One way to do data science is to repeatedly jump through the hoops of working with a properly-secured Hadoop cluster. This is difficult.
- Another way is to extract data from a Hadoop cluster onto your personal machine. This is insecure (once the data arrives) and not very parallelized.
- A third way is needed.
Cloudera’s idea for a third way is:
- You don’t run anything on your desktop/laptop machine except a browser.
- The browser connects you to a Docker container that holds (and isolates) a kind of virtual desktop for you.
- The Docker container runs on your Cloudera cluster, so connectivity-to-Hadoop and security are handled rather automagically.
In theory, that’s pure goodness … assuming that the automagic works sufficiently well. I gather that Cloudera Data Science Workbench has been beta tested by 5 large organizations and many 10s of users. We’ll see what is or isn’t missing as more customers take it for a spin.
|Categories: Cloudera, Hadoop, Market share and customer counts, Predictive modeling and advanced analytics||2 Comments|
For starters, let me say:
- SequoiaDB, the company, is my client.
- SequoiaDB, the product, is the main product of SequoiaDB, the company.
- SequoiaDB, the company, has another product line SequoiaCM, which subsumes SequoiaDB in content management use cases.
- SequoiaDB, the product, is fundamentally a JSON data store. But it has a relational front end …
- … and is usually sold for RDBMS-like use cases …
- … except when it is sold as part of SequoiaCM, which adds in a large object/block store and a content-management-oriented library.
- SequoiaDB’s products are open source.
- SequoiaDB’s largest installation seems to be 2 PB across 100 nodes; that includes block storage.
- Figures for DBMS-only database sizes aren’t as clear, but the sweet spot of the cluster-size range for such use cases seems to be 6-30 nodes.
- SequoiaDB, the company, was founded in Toronto, by former IBM DB2 folks.
- Even so, it’s fairly accurate to view SequoiaDB as a Chinese company. Specifically:
- SequoiaDB’s founders were Chinese nationals.
- Most of them went back to China.
- Other employees to date have been entirely Chinese.
- Sales to date have been entirely in China, but SequoiaDB has international aspirations
- SequoiaDB has >100 employees, a large majority of which are split fairly evenly between “engineering” and “implementation and technical support”.
- SequoiaDB’s marketing (as opposed to sales) department is astonishingly tiny.
- SequoiaDB cites >100 subscription customers, including 10 in the global Fortune 500, a large fraction of which are in the banking sector. (Other sectors mentioned repeatedly are government and telecom.)
Unfortunately, SequoiaDB has not captured a lot of detailed information about unpaid open source production usage.
|Categories: Application areas, Business intelligence, Data models and architecture, Data warehousing, Databricks, Spark and BDAS, Market share and customer counts, NoSQL, OLTP, Open source, PostgreSQL, SequoiaDB, Structured documents||4 Comments|
That said, while Steve Bannon is firmly established as Trump’s puppet master, they don’t agree on quite everything, and one of the documented disagreements had been in their view of skilled, entrepreneurial founder-type immigrants: Bannon opposes them, but Trump has disagreed with his view. And as per the speech, Trump seems to be maintaining his disagreement.
At least, that seems implied by his call for “a merit-based immigration system.”
And by the way — Trump managed to give a whole speech without saying anything overtly racist. Indeed, he specifically decried the murder of an Indian-immigrant engineer. By Trump standards, that counts as a kind of progress.
Edit (March 5): But now there is negative-seeming news about H1-B visas.
I’d like to argue that a single frame can be used to view a lot of the issues that we think about. Specifically, I’m referring to coordination, which I think is a clearer way of characterizing much of what we commonly call communication or collaboration.
It’s easy to argue that computing, to an overwhelming extent, is really about communication. Most obviously:
- Data is constantly moving around — across wide area networks, across local networks, within individual boxes, or even within particular chips.
- Many major developments are almost purely about communication. The most important computing device today may be a telephone. The World Wide Web is essentially a publishing platform. Social media are huge. Etc.
Indeed, it’s reasonable to claim:
- When technology creates new information, it’s either analytics or just raw measurement.
- Everything else is just moving information around, and that’s communication.
A little less obvious is the much of this communication could be alternatively described as coordination. Some communication has pure consumer value, such as when we talk/email/Facebook/Snapchat/FaceTime with loved ones. But much of the rest is for the purpose of coordinating business or technical processes.
Among the technical categories that boil down to coordination are:
- Operating systems.
- Anything to do with distributed computing.
- Anything to do with system or cluster management.
- Anything that’s called “collaboration”.
That’s a lot of the value in “platform” IT right there. Read more
|Categories: Business intelligence, Predictive modeling and advanced analytics, Public policy||2 Comments|
The United States and consequently much of the world are in political uproar. Much of that is about very general and vital issues such as war, peace or the treatment of women. But quite a lot of it is to some extent tech-industry-specific. The purpose of this post is outline how and why that is.
- There’s a worldwide backlash against “elites” — and tech industry folks are perceived as members of those elites.
- That perception contains a lot of truth, and not just in terms of culture/education/geography. Indeed, it may even be a bit understated, because trends commonly blamed on “trade” or “globalization” often have their roots in technological advances.
- There’s a worldwide trend towards authoritarianism. Surveillance/ privacy and censorship issues are strongly relevant to that trend.
- Social media companies are up to their neck in political considerations.
Because they involve grave threats to liberty, I see surveillance/privacy as the biggest technology-specific policy issues in the United States. (In other countries, technology-driven censorship might loom larger yet.) My views on privacy and surveillance have long been:
- Fixing the legal frameworks around information use is a difficult and necessary job. The tech community should be helping more than it is.
- Until those legal frameworks are indeed cleaned up, the only responsible alternative is to foot-drag on data collection, on data retention, and on the provision of data to governmental agencies.
Given the recent election of a US president with strong authoritarian tendencies, that foot-dragging is much more important than it was before.
Other important areas of technology/policy overlap include: Read more
The United States presidency was recently assumed by an Orwellian lunatic.* Sadly, this is not an exaggeration. The dangers — both of authoritarianism and of general mis-governance — are massive. Everybody needs in some way to respond.
*”Orwellian lunatic” is by no means an oxymoron. Indeed, many of the most successful tyrants in modern history have been delusional; notable examples include Hitler, Stalin, Mao and, more recently, Erdogan. (By way of contrast, I view most other Soviet/Russian leaders and most jumped-up-colonel coup leaders as having been basically sane.)
There are many candidates for what to focus on, including:
- Technology-specific issues — e.g. privacy/surveillance, network neutrality, etc.
- Issues in which technology plays a large role — e.g. economic changes that affect many people’s employment possibilities.
- Subjects that may not be tech-specific, but are certainly of great importance. The list of candidates here is almost endless, such as health care, denigration of women, maltreatment of immigrants, or the possible breakdown of the whole international order.
But please don’t just go on with your life and leave the politics to others. Those “others” you’d like to rely on haven’t been doing a very good job.
What I’ve chosen to do personally includes: Read more
Crate.io and CrateDB basics include:
- Crate.io makes CrateDB.
- CrateDB is a quasi-RDBMS designed to receive sensor data and similar IoT (Internet of Things) inputs.
- CrateDB’s creators were perhaps a little slow to realize that the “R” part was needed, but are playing catch-up in that regard.
- Crate.io is an outfit founded by Austrian guys, headquartered in Berlin, that is turning into a San Francisco company.
- Crate.io says it has 22 employees and 5 paying customers.
- Crate.io cites bigger numbers than that for confirmed production users, clearly active clusters, and overall product downloads.
In essence, CrateDB is an open source and less mature alternative to MemSQL. The opportunity for MemSQL and CrateDB alike exists in part because analytic RDBMS vendors didn’t close it off.
CrateDB’s not-just-relational story starts:
- A column can contain ordinary values (of usual-suspect datatypes) or “objects”, …
- … where “objects” presumably are the kind of nested/hierarchical structures that are common in the NoSQL/internet-backend world, …
- … except when they’re just BLOBs (Binary Large OBjects).
- There’s a way to manually define “strict schemas” on the structured objects, and a syntax for navigating their structure in WHERE clauses.
- There’s also a way to automagically infer “dynamic schemas”, but it’s simplistic enough to be more suitable for development/prototyping than for serious production.
|Categories: Columnar database management, Data models and architecture, Databricks, Spark and BDAS, GIS and geospatial, MemSQL, NoSQL, Open source, Structured documents||3 Comments|
After a July visit to DataStax, I wrote
The idea that NoSQL does away with DBAs (DataBase Administrators) is common. It also turns out to be wrong. DBAs basically do two things.
- Handle the database design part of application development. In NoSQL environments, this part of the job is indeed largely refactored away. More precisely, it is integrated into the general app developer/architect role.
- Manage production databases. This part of the DBA job is, if anything, a bigger deal in the NoSQL world than in more mature and automated relational environments. It’s likely to be called part of “devops” rather than “DBA”, but by whatever name it’s very much a thing.
That turns out to understate the core point, which is that DBAs still matter in non-RDBMS environments. Specifically, it’s too narrow in two ways.
- First, it’s generally too narrow as to what DBAs do; people with DBA-like skills are also involved in other areas such as “data governance”, “information lifecycle management”, storage, or what I like to call data mustering.
- Second — and more narrowly — the first bullet point of the quote is actually incorrect. In fact, the database design part of application development can be done by a specialized person up front in the NoSQL world, just as it commonly is for RDBMS apps.
My wake-up call for that latter bit was a recent MongoDB 3.4 briefing. MongoDB certainly has various efforts in administrative tools, which I won’t recapitulate here. But to my surprise, MongoDB also found a role for something resembling relational database design. The idea is simple: A database administrator defines a view against a MongoDB database, where views: Read more
|Categories: Databricks, Spark and BDAS, Hadoop, MongoDB, NoSQL, Streaming and complex event processing (CEP)||Leave a Comment|
“Multimodel” database management is a hot new concept these days, notwithstanding that it’s been around since at least the 1990s. My clients at MongoDB of course had to join the train as well, but they’ve taken a clear and interesting stance:
- A query layer with multiple ways to query and analyze data.
- A separate data storage layer in which you have a choice of data storage engines …
- … each of which has the same logical (JSON-based) data structure.
When I pointed out that it would make sense to call this “multimodel query” — because the storage isn’t “multimodel” at all — they quickly agreed.
To be clear: While there are multiple ways to read data in MongoDB, there’s still only one way to write it. Letting that sink in helps clear up confusion as to what about MongoDB is or isn’t “multimodel”. To spell that out a bit further: Read more
|Categories: Database diversity, Emulation, transparency, portability, MongoDB, MySQL, NoSQL, Open source, RDF and graphs, Structured documents, Text||3 Comments|