SQL/Hadoop integration

Discussion of SQL-on-Hadoop and other forms of SQL/Hadoop integration.

July 23, 2014

Teradata bought Hadapt and Revelytix

My client Teradata bought my (former) clients Revelytix and Hadapt.* Obviously, I’m in confidentiality up to my eyeballs. That said — Teradata truly doesn’t know what it’s going to do with those acquisitions yet. Indeed, the acquisitions are too new for Teradata to have fully reviewed the code and so on, let alone made strategic decisions informed by that review. So while this is just a guess, I conjecture Teradata won’t say anything concrete until at least September, although I do expect some kind of stated direction in time for its October user conference.

*I love my business, but it does have one distressing aspect, namely the combination of subscription pricing and customer churn. When your customers transform really quickly, or even go out of existence, so sometimes does their reliance on you.

I’ve written extensively about Hadapt, but to review:

As for what Teradata should do with Hadapt: Read more

July 15, 2014

The point of predicate pushdown

Oracle is announcing today what it’s calling “Oracle Big Data SQL”. As usual, I haven’t been briefed, but highlights seem to include:

And by the way – Oracle Big Data SQL is NOT “SQL-on-Hadoop” as that term is commonly construed, unless the complete Oracle DBMS is running on every node of a Hadoop cluster.

Predicate pushdown is actually a simple concept:

“Predicate pushdown” gets its name from the fact that portions of SQL statements, specifically ones that filter data, are properly referred to as predicates. They earn that name because predicates in mathematical logic and clauses in SQL are the same kind of thing — statements that, upon evaluation, can be TRUE or FALSE for different values of variables or data.

The most famous example of predicate pushdown is Oracle Exadata, with the story there being:

Oracle evidently calls this “SmartScan”, and says Oracle Big Data SQL does something similar with predicate pushdown into Hadoop.

Oracle also hints at using predicate pushdown to do non-tabular operations on the non-relational systems, rather than shoehorning operations on multi-structured data into the Oracle DBMS, but my details on that are sparse.

Related link

June 18, 2014

Using multiple data stores

I’m commonly asked to assess vendor claims of the kind:

So I thought it might be useful to quickly review some of the many ways organizations put multiple data stores to work. As usual, my bottom line is:

Horses for courses

It’s now widely accepted that different data managers are better for different use cases, based on distinctions such as:

Vendors are part of this consensus; already in 2005 I observed

For all practical purposes, there are no DBMS vendors left advocating single-server strategies.

Vendor agreement has become even stronger in the interim, as evidenced by Oracle/MySQL, IBM/Netezza, Oracle’s NoSQL dabblings, and various companies’ Hadoop offerings.

Multiple data stores for a single application

We commonly think of one data manager managing one or more databases, each in support of one or more applications. But the other way around works too; it’s normal for a single application to invoke multiple data stores. Indeed, all but the strictest relational bigots would likely agree:  Read more

May 2, 2014

Introduction to CitusDB

One of my lesser-known clients is Citus Data, a largely Turkish company that is however headquartered in San Francisco. They make CitusDB, which puts a scale-out layer over a collection of fully-functional PostgreSQL nodes, much like Greenplum and Aster Data before it. However, in contrast to those and other Postgres-based analytic MPP (Massively Parallel Processing) DBMS:

*One benefit to this strategy, besides the usual elasticity and recovery stuff, is that while PostgreSQL may be single-core for any given query, a CitusDB query can use multiple cores by virtue of hitting multiple PostgreSQL tables on each node.

Citus has thrown a few things against the wall; for example, there are two versions of its product, one which involves HDFS (Hadoop Distributed File System) and one of which doesn’t. But I think Citus’ focus will be scale-out PostgreSQL for at least the medium-term future. Citus does have actual customers, and they weren’t all PostgreSQL users previously. Still, the main hope — at least until the product is more built-out — is that existing PostgreSQL users will find CitusDB easy to adopt, in technology and price alike.

Read more

April 30, 2014

Cloudera, Impala, data warehousing and Hive

There’s much confusion about Cloudera’s SQL plans and beliefs, and the company has mainly itself to blame. That said, here’s what I think is going on.

And of course, as vendors so often do, Cloudera generally overrates both the relative maturity of Impala and the relative importance of the use cases in which its offerings – Impala or otherwise – shine.

Related links

April 30, 2014

Spark on fire

Spark is on the rise, to an even greater degree than I thought last month.

*Yes, my fingerprints are showing again.

The most official description of what Spark now contains is probably the “Spark ecosystem” diagram from Databricks. However, at the time of this writing it is slightly out of date, as per some email from Databricks CEO Ion Stoica (quoted with permission):

… but if I were to redraw it, SparkSQL will replace Shark, and Shark will eventually become a thin layer above SparkSQL and below BlinkDB.

With this change, all the modules on top of Spark (i.e., SparkStreaming, SparkSQL, GraphX, and MLlib) are part of the Spark distribution. You can think of these modules as libraries that come with Spark.

Read more

March 17, 2014

Notes and comments, March 17, 2014

I have ever more business-advice posts up on Strategic Messaging. Recent subjects include pricing and stealth-mode marketing. Other stuff I’ve been up to includes:

The Spark buzz keeps increasing; almost everybody I talk with expects Spark to win big, probably across several use cases.

Disclosure: I’ll soon be in a substantial client relationship with Databricks, hoping to improve their stealth-mode marketing. :D

The “real-time analytics” gold rush I called out last year continues. A large fraction of the vendors I talk with have some variant of “real-time analytics” as a central message.

Basho had a major change in leadership. A Twitter exchange ensued. :) Joab Jackson offered a more sober — figuratively and literally — take.

Hadapt laid off its sales and marketing folks, and perhaps some engineers as well. In a nutshell, Hadapt’s approach to SQL-on-Hadoop wasn’t selling vs. the many alternatives, and Hadapt is doubling down on poly-structured data*/schema-on-need.

*While Hadapt doesn’t to my knowledge use the term “poly-structured data”, some other vendors do. And so I may start using it more myself, at least when the poly-structured/multi-structured distinction actually seems significant.

WibiData is partnering with DataStax, WibiData is of course pleased to get access to Cassandra’s user base, which gave me the opportunity to ask why they thought Cassandra had beaten HBase in those accounts. The answer was performance and availability, while Cassandra’s traditional lead in geo-distribution wasn’t mentioned at all.

Disclosure: My fingerprints are all over that deal.

In other news, WibiData has had some executive departures as well, but seems to be staying the course on its strategy. I continue to think that WibiData has a really interesting vision about how to do large-data-volume interactive computing, and anybody in that space would do well to talk with them or at least look into the open source projects WibiData sponsors.

I encountered another apparently-popular machine-learning term — bandit model. It seems to be glorified A/B testing, and it seems to be popular. I think the point is that it tries to optimize for just how much you invest in testing unproven (for good or bad) alternatives.

I had an awkward set of interactions with Gooddata, including my longest conversations with them since 2009. Gooddata is in the early days of trying to offer an all-things-to-all-people analytic stack via SaaS (Software as a Service). I gather that Hadoop, Vertica, PostgreSQL (a cheaper Vertica alternative), Spark, Shark (as a faster version of Hive) and Cassandra (under the covers) are all in the mix — but please don’t hold me to those details.

I continue to think that computing is moving to a combination of appliances, clusters, and clouds. That said, I recently bought a new gaming-class computer, and spent many hours gaming on it just yesterday.* I.e., there’s room for general-purpose workstations as well. But otherwise, I’m not hearing anything that contradicts my core point.

*The last beta weekend for The Elder Scrolls Online; I loved Morrowind.

February 9, 2014

Distinctions in SQL/Hadoop integration

Ever more products try to integrate SQL with Hadoop, and discussions of them seem confused, in line with Monash’s First Law of Commercial Semantics. So let’s draw some distinctions, starting with (and these overlap):

In particular:

Let’s go to some examples. Read more

December 5, 2013

Vertica 7

It took me a bit of time, and an extra call with Vertica’s long-time R&D chief Shilpa Lawande, but I think I have a decent handle now on Vertica 7, code-named Crane. The two aspects of Vertica 7 I find most interesting are:

Other Vertica 7 enhancements include:

Overall, two recurring themes in our discussion were:

Read more

September 8, 2013

Layering of database technology & DBMS with multiple DMLs

Two subjects in one post, because they were too hard to separate from each other

Any sufficiently complex software is developed in modules and subsystems. DBMS are no exception; the core trinity of parser, optimizer/planner, and execution engine merely starts the discussion. But increasingly, database technology is layered in a more fundamental way as well, to the extent that different parts of what would seem to be an integrated DBMS can sometimes be developed by separate vendors.

Major examples of this trend — where by “major” I mean “spanning a lot of different vendors or projects” — include:

Other examples on my mind include:

And there are several others I hope to blog about soon, e.g. current-day PostgreSQL.

In an overlapping trend, DBMS increasingly have multiple data manipulation APIs. Examples include:  Read more

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