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

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 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

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

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

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

Cloudera Kudu deep dive

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

Let’s talk in more detail about how Kudu stores data.

Read more

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:

Read more

July 7, 2015

Zoomdata and the Vs

Let’s start with some terminology biases:

So when my clients at Zoomdata told me that they’re in the business of providing “the fastest visual analytics for big data”, I understood their choice, but rolled my eyes anyway. And then I immediately started to check how their strategy actually plays against the “big data” Vs.

It turns out that:

*The HDFS/S3 aspect seems to be a major part of Zoomdata’s current story.

Core aspects of Zoomdata’s technical strategy include:  Read more

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