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  • June 16, 2017

    Generally available Kudu

    I talked with Cloudera about Kudu in early May. Besides giving me a lot of information about Kudu, Cloudera also helped confirm some trends I’m seeing elsewhere, including:

    Now let’s talk about Kudu itself. As I discussed at length in September 2015, Kudu is:

    Kudu’s adoption and roll-out story starts: Read more

    June 14, 2017

    Light-touch managed services

    Cloudera recently introduced Cloudera Altus, a Hadoop-in-the-cloud offering with an interesting processing model:

    Thus, you avoid a potential security risk (shipping your data to Cloudera’s service). I’ve tentatively named this strategy light-touch managed services, and am interested in exploring how broadly applicable it might or might not be.

    For light-touch to be a good approach, there should be (sufficiently) little downside in performance, reliability and so on from having your service not actually control the data. That assumption is trivially satisfied in the case of Cloudera Altus, because it’s not an ordinary kind of app; rather, its whole function is to improve the job-running part of your stack. Most kinds of apps, however, want to operate on your data directly. For those, it is more challenging to meet acceptable SLAs (Service-Level Agreements) on a light-touch basis.

    Let’s back up and consider what “light-touch” for data-interacting apps (i.e., almost all apps) would actually mean. The basics are:? Read more

    June 14, 2017

    Cloudera Altus

    I talked with Cloudera before the recent release of Altus. In simplest terms, Cloudera’s cloud strategy aspires to:

    In other words, Cloudera is porting its software to an important new platform.* And this port isn’t complete yet, in that Altus is geared only for certain workloads. Specifically, Altus is focused on “data pipelines”, aka data transformation, aka “data processing”, aka new-age ETL (Extract/Transform/Load). (Other kinds of workload are on the roadmap, including several different styles of Impala use.) So what about that is particularly interesting? Well, let’s drill down.

    *Or, if you prefer, improving on early versions of the port.

    Read more

    March 19, 2017

    Cloudera’s Data Science Workbench

    0. Matt Brandwein of Cloudera briefed me on the new Cloudera Data Science Workbench. The problem it purports to solve is:

    Cloudera’s idea for a third way is:

    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.

    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

    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

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