Discussion of Hadoop. Related subjects include:

Open source database management systems

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

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

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:

Read more

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 25, 2015

Splunk engages in stupid lawyer tricks

Using legal threats as an extension of your marketing is a bad idea. At least, it’s a bad idea in the United States, where such tactics are unlikely to succeed, and are apt to backfire instead. Splunk seems to actually have had some limited success intimidating Sumo Logic. But it tried something similar against Rocana, and I was set up to potentially be collateral damage. I don’t think that’s working out very well for Splunk.

Specifically, Splunk sent a lawyer letter to Rocana, complaining about a couple of pieces of Rocana marketing collateral. Rocana responded publicly, and posted both the Splunk letter and Rocana’s lawyer response. The Rocana letter eviscerated Splunk’s lawyers on matters of law, clobbered them on the facts as well, exposed Splunk’s similar behavior in the past, and threw in a bit of snark at the end.

Now I’ll pile on too. In particular, I’ll note that, while Splunk wants to impose a duty of strict accuracy upon those it disagrees with, it has fewer compunctions about knowingly communicating falsehoods itself.

1. Splunk’s letter insinuates that Rocana might have paid me to say what I blogged about them. Those insinuations are of course false.

Splunk was my client for a lot longer, and at a higher level of annual retainer, than Rocana so far has been. Splunk never made similar claims about my posts about them. Indeed, Splunk complained that I did not write about them often or favorably enough, and on at least one occasion seemed to delay renewing my services for that reason.

2. Similarly, Splunk’s letter makes insinuations about quotes I gave Rocana. But I also gave at least one quote to Splunk when they were my client. As part of the process — and as is often needed — I had a frank and open discussion with them about my quote policies. So Splunk should know that their insinuations are incorrect.

3. Splunk’s letter actually included the sentences  Read more

November 19, 2015

The questionably named Cloudera Navigator Optimizer

I only have mixed success at getting my clients to reach out to me for messaging advice when they’re introducing something new. Cloudera Navigator Optimizer, which is being announced along with Cloudera 5.5, is one of my failures in that respect; I heard about it for the first time Tuesday afternoon. I hate the name. I hate some of the slides I saw. But I do like one part of the messaging, namely the statement that this is about “refactoring” queries.

All messaging quibbles aside, I think the Cloudera Navigator Optimizer story is actually pretty interesting, and perhaps not just to users of SQL-on-Hadoop technologies such as Hive (which I guess I’d put in that category for simplicity) or Impala. As I understand Cloudera Navigator Optimizer:

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.

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