Databricks, Spark and BDAS

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

July 19, 2016

Notes from a long trip, July 19, 2016

For starters:

A running list of recent posts is:

Subjects I’d like to add to that list include:

Read more

January 25, 2016

Kafka and more

In a companion introduction to Kafka post, I observed that Kafka at its core is remarkably simple. Confluent offers a marchitecture diagram that illustrates what else is on offer, about which I’ll note:

Kafka offers little in the way of analytic data transformation and the like. Hence, it’s commonly used with companion products.  Read more

January 22, 2016

Cloudera in the cloud(s)

Cloudera released Version 2 of Cloudera Director, which is a companion product to Cloudera Manager focused specifically on the cloud. This led to a discussion about — you guessed it! — Cloudera and the cloud.

Making Cloudera run in the cloud has three major aspects:

Features new in this week’s release of Cloudera Director include:

I.e., we’re talking about some pretty basic/checklist kinds of things. Cloudera Director is evidently working for Amazon AWS and Google GCP, and planned for Windows Azure, VMware and OpenStack.

As for porting, let me start by noting: Read more

January 14, 2016

BI and quasi-DBMS

I’m on two overlapping posting kicks, namely “lessons from the past” and “stuff I keep saying so might as well also write down”. My recent piece on Oracle as the new IBM is an example of both themes. In this post, another example, I’d like to memorialize some points I keep making about business intelligence and other analytics. In particular:

Similarly, BI has often been tied to data integration/ETL (Extract/Transform/Load) functionality.* But I won’t address that subject further at this time.

*In the Hadoop/Spark era, that’s even truer of other analytics than it is of BI.

My top historical examples include:

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December 10, 2015

Readings in Database Systems

Mike Stonebraker and Larry Ellison have numerous things in common. If nothing else:

I mention the latter because there’s a new edition of Readings in Database Systems, aka the Red Book, available online, courtesy of Mike, Joe Hellerstein and Peter Bailis. Besides the recommended-reading academic papers themselves, there are 12 survey articles by the editors, and an occasional response where, for example, editors disagree. Whether or not one chooses to tackle the papers themselves — and I in fact have not dived into them — the commentary is of great interest.

But I would not take every word as the gospel truth, especially when academics describe what they see as commercial market realities. In particular, as per my quip in the first paragraph, the data warehouse market has not yet gone to the extremes that Mike suggests,* if indeed it ever will. And while Joe is close to correct when he says that the company Essbase was acquired by Oracle, what actually happened is that Arbor Software, which made Essbase, merged with Hyperion Software, and the latter was eventually indeed bought by the giant of Redwood Shores.**

*When it comes to data warehouse market assessment, Mike seems to often be ahead of the trend.

**Let me interrupt my tweaking of very smart people to confess that my own commentary on the Oracle/Hyperion deal was not, in retrospect, especially prescient.

Mike pretty much opened the discussion with a blistering attack against hierarchical data models such as JSON or XML. To a first approximation, his views might be summarized as:  Read more

November 19, 2015

CDH 5.5

I talked with Cloudera shortly ahead of today’s announcement of Cloudera 5.5. Much of what we talked about had something or other to do with SQL data management. Highlights include:

While I had Cloudera on the phone, I asked a few questions about Impala adoption, specifically focused on concurrency. There was mention of: Read more

October 15, 2015

Basho and Riak

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.

For starters:

Basho’s product line has gotten a bit confusing, but as best I understand things the story is:

Technical notes on some of that include:  Read more

October 15, 2015

Couchbase 4.0 and related subjects

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.

Technical notes on Couchbase 4.0 — and related riffs :) — start: Read more

September 28, 2015

The potential significance of Cloudera Kudu

This is part of a three-post series on Kudu, a new data storage system from Cloudera.

Combined with Impala, Kudu is (among other things) an attempt to build a no-apologies analytic DBMS (DataBase Management System) into Hadoop. My reactions to that start:

I’ll expand on that last point. Analytics is no longer just about fast queries on raw or simply-aggregated data. Data transformation is getting ever more complex — that’s true in general, and it’s specifically true in the case of transformations that need to happen in human real time. Predictive models now often get rescored on every click. Sometimes, they even get retrained at short intervals. And while data reduction in the sense of “event extraction from high-volume streams” isn’t that a big deal yet in commercial apps featuring machine-generated data — if growth trends continue as much of us expect, it’s only a matter of time before that changes.

Of course, this is all a bullish argument for Spark (or Flink, if I’m wrong to dismiss its chances as a Spark competitor). But it also all requires strong low-latency analytic data underpinnings, and I suspect that several kinds of data subsystem will prosper. I expect Kudu-supported Hadoop/Spark to be a strong contender for that role, along with the best of the old-school analytic RDBMS, Tachyon-supported Spark, one or more contenders from the Hana/MemSQL crowd (i.e., memory-centric RDBMS that purport to be good at analytics and transactions alike), and of course also whatever Cloudera’s strongest competitor(s) choose to back.

September 28, 2015

Introduction to Cloudera Kudu

This is part of a three-post series on Kudu, a new data storage system from Cloudera.

Cloudera is introducing a new open source project, Kudu,* which from Cloudera’s standpoint is meant to eventually become the single best underpinning for analytics on the Hadoop stack. I’ve spent multiple hours discussing Kudu with Cloudera, mainly with Todd Lipcon. Any errors are of course entirely mine.

*Like the impala, the kudu is a kind of antelope. I knew that, because I enjoy word games. What I didn’t know — and which is germane to the naming choice — is that the kudu has stripes. :)

For starters:

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