February 25, 2013

Greenplum HAWQ

My former friends at Greenplum no longer talk to me, so in particular I wasn’t briefed on Pivotal HD and Greenplum HAWQ. Pivotal HD seems to be yet another Hadoop distribution, with the idea that you use Greenplum’s management tools. Greenplum HAWQ seems to be Greenplum tied to HDFS.

The basic idea seems to be much like what I mentioned a few days ago  — the low-level file store for Greenplum can now be something else one has heard of before, namely HDFS (Hadoop Distributed File System, which is also an option for, say, NuoDB). Beyond that, two interesting quotes in a Greenplum blog post are:

When a query starts up, the data is loaded out of HDFS and into the HAWQ execution engine.

and

In addition, it has native support for HBase, supporting HBase predicate pushdown, hive[sic] connectivity, and offering a ton of intelligent features to retrieve HBase data.

The first sounds like the invisible loading that Daniel Abadi wrote about last September on Hadapt’s blog. (Edit: Actually, see Daniel’s comment below.) The second sounds like a good idea that, again, would also be a natural direction for vendors such as Hadapt.

February 22, 2013

Should you offer “complete” analytic applications?

WibiData is essentially on the trajectory:

The same, it turns out, is true of Causata.* Talking with them both the same day led me to write this post. Read more

November 1, 2012

More on Cloudera Impala

What I wrote before about Cloudera Impala was quite incomplete. After a followup call, I now feel I have a better handle on the whole thing.

First, some basics:

The general technical idea of Impala is:

Read more

October 31, 2012

Notes and comments — October 31, 2012

Time for another catch-all post. First and saddest — one of the earliest great commenters on this blog, and a beloved figure in the Boston-area database community, was Dan Weinreb, whom I had known since some Symbolics briefings in the early 1980s. He passed away recently, much much much too young. Looking back for a couple of examples — even if you’ve never heard of him before, I see that Dan ‘s 2009 comment on Tokutek is still interesting today, and so is a post on his own blog disagreeing with some of my choices in terminology.

Otherwise, in no particular order:

1. Chris Bird is learning MongoDB. As is common for Chris, his comments are both amusing and enlightening.

2. When I relayed Cloudera’s comments on Hadoop adoption, I left out a couple of categories. One Cloudera called “mobile”; when I probed, that was about HBase, with an example being messaging apps.

The other was “phone home” — i.e., the ingest of machine-generated data from a lot of different devices. This is something that’s obviously been coming for several years — but I’m increasingly getting the sense that it’s actually arrived.

Read more

October 29, 2012

Introduction to Continuuity

I chatted with Todd Papaioannou about his new company Continuuity. Todd is as handy at combining buzzwords as he is at concatenating vowels, and so Continuuity — with two “U”s —  is making a big data fabric platform as a service with REST APIs that runs over Hadoop and HBase in the private or public clouds. I found the whole thing confusing, in that:

But all confusion aside, there are some interesting aspects to Continuuity. Read more

October 18, 2012

Notes on Hadoop adoption and trends

With Strata/Hadoop World being next week, there is much Hadoop discussion. One theme of the season is BI over Hadoop. I have at least 5 clients claiming they’re uniquely positioned to support that (most of whom partner with a 6th client, Tableau); the first 2 whose offerings I’ve actually written about are Teradata Aster and Hadapt. More generally, I’m hearing “Using Hadoop is hard; we’re here to make it easier for you.”

If enterprises aren’t yet happily running business intelligence against Hadoop, what are they doing with it instead? I took the opportunity to ask Cloudera, whose answers didn’t contradict anything I’m hearing elsewhere. As Cloudera tells it (approximately — this part of the conversation* was rushed):   Read more

August 26, 2012

How immediate consistency works

This post started as a minor paragraph in another one I’m drafting. But it grew. Please also see the comment thread below.

Increasingly many data management systems store data in a cluster, putting several copies of data — i.e. “replicas” — onto different nodes, for safety and reliable accessibility. (The number of copies is called the “replication factor”.) But how do they know that the different copies of the data really have the same values? It seems there are three main approaches to immediate consistency, which may be called:

I shall explain.

Two-phase commit has been around for decades. Its core idea is:

Unless a piece of the system malfunctions at exactly the wrong time, you’ll get your consistent write. And if there indeed is an unfortunate glitch — well, that’s what recovery is for.

But 2PC has a flaw: If a node is inaccessible or down, then the write is blocked, even if other parts of the system were able to accept the data safely. So the NoSQL world sometimes chooses RYW consistency, which in essence is a loose form of 2PC: Read more

August 8, 2012

HCatalog — yes, it matters

To a first approximation, HCatalog is the thing that will make Hadoop play nicely with all kinds of ETLT (Extract/Transform/Load/Transform). However, HCatalog is both less and more than that:

The base use case for HCatalog is:

Major variants on that include: Read more

June 25, 2012

Why I’m so forward-leaning about Hadoop features

In my recent series of Hadoop posts, there were several cases where I had to choose between recommending that enterprises:

I favored the more advanced features each time. Here’s why.

To a first approximation, I divide Hadoop use cases into two major buckets, only one of which I was addressing with my comments:

1. Analytic data management.* Here I favored features over reliability because they are more important, for Hadoop as for analytic RDBMS before it. When somebody complains about an analytic data store not being ready for prime time, never really working, or causing them to tear their hair out, what they usually mean is that:

Those complaints are much, much, more frequent than “It crashed”. So it was for Netezza, DATAllegro, Greenplum, Aster Data, Vertica, Infobright, et al. So it also is for Hadoop. And how does one address those complaints? By performance and feature enhancements, of the kind that the Hadoop community is introducing at high speed. Read more

June 19, 2012

Notes on HBase 0.92

This is part of a four-post series, covering:

As part of my recent round of Hadoop research, I talked with Cloudera’s Todd Lipcon. Naturally, one of the subjects was HBase, and specifically HBase 0.92. I gather that the major themes to HBase 0.92 are:

HBase coprocessors are Java code that links straight into HBase. As with other DBMS extensions of the “links straight into the DBMS code” kind,* HBase coprocessors seem best suited for very sophisticated users and third parties.** Evidently, coprocessors have already been used to make HBase security more granular — role-based, per-column-family/per-table, etc. Further, Todd thinks coprocessors could serve as a good basis for future HBase enhancements in areas such as aggregation or secondary indexing. Read more

← Previous PageNext Page →

Feed: DBMS (database management system), DW (data warehousing), BI (business intelligence), and analytics technology Subscribe to the Monash Research feed via RSS or email:

Login

Search our blogs and white papers

Monash Research blogs

User consulting

Building a short list? Refining your strategic plan? We can help.

Vendor advisory

We tell vendors what's happening -- and, more important, what they should do about it.

Monash Research highlights

Learn about white papers, webcasts, and blog highlights, by RSS or email.