Databricks, Spark and BDAS

Discussion of BDAS (Berkeley Data Analytics Systems), especially Spark and related projects, and also of Databricks, the company commercializing Spark.

April 13, 2017

Analyzing the right data

0. A huge fraction of what’s important in analytics amounts to making sure that you are analyzing the right data. To a large extent, “the right data” means “the right subset of your data”.

1. In line with that theme:

2. Business intelligence interfaces today don’t look that different from what we had in the 1980s or 1990s. The biggest visible* changes, in my opinion, have been in the realm of better drilldown, ala QlikView and then Tableau. Drilldown, of course, is the main UI for business analysts and end users to subset data themselves.

*I used the word “visible” on purpose. The advances at the back end have been enormous, and much of that redounds to the benefit of BI.

3. I wrote 2 1/2 years ago that sophisticated predictive modeling commonly fit the template:

That continues to be tough work. Attempts to productize shortcuts have not caught fire.

Read more

March 12, 2017

Introduction to SequoiaDB and SequoiaCM

For starters, let me say:


Unfortunately, SequoiaDB has not captured a lot of detailed information about unpaid open source production usage.

Read more

December 18, 2016

Introduction to and CrateDB and CrateDB basics include:

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:

Read more

November 23, 2016

DBAs of the future

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.

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

October 21, 2016

Rapid analytics

“Real-time” technology excites people, and has for decades. Yet the actual, useful technology to meet “real-time” requirements remains immature, especially in cases which call for rapid human decision-making. Here are some notes on that conundrum.

1. I recently posted that “real-time” is getting real. But there are multiple technology challenges involved, including:

2. In early 2011, I coined the phrase investigative analytics, about which I said three main things: Read more

August 28, 2016

Are analytic RDBMS and data warehouse appliances obsolete?

I used to spend most of my time — blogging and consulting alike — on data warehouse appliances and analytic DBMS. Now I’m barely involved with them. The most obvious reason is that there have been drastic changes in industry structure:

Simply reciting all that, however, begs the question of whether one should still care about analytic RDBMS at all.

My answer, in a nutshell, is:

Analytic RDBMS — whether on premises in software, in the form of data warehouse appliances, or in the cloud – are still great for hard-core business intelligence, where “hard-core” can refer to ad-hoc query complexity, reporting/dashboard concurrency, or both. But they aren’t good for much else.

Read more

August 21, 2016

Introduction to data Artisans and Flink

data Artisans and Flink basics start:

Like many open source projects, Flink seems to have been partly inspired by a Google paper.

To this point, data Artisans and Flink have less maturity and traction than Databricks and Spark. For example:  Read more

August 21, 2016

More about Databricks and Spark

Databricks CEO Ali Ghodsi checked in because he disagreed with part of my recent post about Databricks. Ali’s take on Databricks’ position in the Spark world includes:

Ali also walked me through customer use cases and adoption in wonderful detail. In general:

The story on those sectors, per Ali, is:  Read more

July 31, 2016

Notes on Spark and Databricks — technology

During my recent visit to Databricks, I of course talked a lot about technology — largely with Reynold Xin, but a bit with Ion Stoica as well. Spark 2.0 is just coming out now, and of course has a lot of enhancements. At a high level:

The majority of Databricks’ development efforts, however, are specific to its cloud service, rather than being donated to Apache for the Spark project. Some of the details are NDA, but it seems fair to mention at least:

Two of the technical initiatives Reynold told me about seemed particularly cool. Read more

July 31, 2016

Notes on Spark and Databricks — generalities

I visited Databricks in early July to chat with Ion Stoica and Reynold Xin. Spark also comes up in a large fraction of the conversations I have. So let’s do some catch-up on Databricks and Spark. In a nutshell:

I shall explain below. I also am posting separately about Spark evolution, especially Spark 2.0. I’ll also talk a bit in that post about Databricks’ proprietary/closed-source technology.

Spark is the replacement for Hadoop MapReduce.

This point is so obvious that I don’t know what to say in its support. The trend is happening, as originally decreed by Cloudera (and me), among others. People are rightly fed up with the limitations of MapReduce, and — niches perhaps aside — there are no serious alternatives other than Spark.

The greatest use for Spark seems to be the same as the canonical first use for MapReduce: data transformation. Also in line with the Spark/MapReduce analogy:  Read more

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