August 22, 2017

                                                    Imanis Data

                                                    I talked recently with the folks at Imanis Data. For starters:

                                                    Read more

                                                    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

                                                    April 17, 2017


                                                    Interana has an interesting story, in technology and business model alike. For starters:

                                                    And to be clear — if we leave aside any questions of marketing-name sizzle, this really is business intelligence. The closest Interana comes to helping with predictive modeling is giving its ad-hoc users inspiration as to where they should focus their modeling attention.

                                                    Interana also has an interesting twist in its business model, which I hope can be used successfully by other enterprise software startups as well. 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.

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                                                    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

                                                    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 — 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 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

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