Hadoop
Discussion of open source MapReduce implementation Hadoop. Related subjects include:
Notes on the Oracle Big Data Appliance
Oracle announced its Big Data Appliance. Specs may be found in the Oracle Big Data Appliance press release. Beyond that:
- The most important software on the Oracle Big Data Appliance is a full set of Cloudera Enterprise code. Oracle will do Tier 1 Cloudera/Hadoop support, while Cloudera handles Tiers 2 and 3.
- The key spec ratios are 1 core/4 GB RAM/3 TB raw disk. That’s reasonably in line with Cloudera figures I published in June, 2010.
- This is really Oracle’s multi-structured big data appliance. Oracle’s relational big data appliance is Exadata, which has been out for years and has comparable capacity to Oracle’s new “Big Data Appliance.” (Chris Preimesberger made a similar point.)
- The Oracle Big Data Appliance list price is $450,000 for 18 12-core servers, plus $54,000/year maintenance.
- That’s around $25,000 per server (and associated storage).
- That’s also around $2,000/core.
- That’s also around $500/TB of spinning disk, before compression.
- None of those per-unit figures sounds ridiculous …
- … but because of Oracle’s appliance configuration there’s indeed a hefty minimum initial purchase.
A couple of links explaining Cloudera Manager
Predictably, I wasn’t pre-briefed on the details of Oracle’s Big Data Appliance announcement today, and an inquiry to partner Cloudera doesn’t happen to have been immediately answered.* But anyhow, it’s clear from coverage by Larry Dignan and Derrick Harris that Oracle’s Big Data Appliance includes:
- Some version of Cloudera Manager (I’m guessing more or less the best one).*
- Some version of Apache Hadoop (I’m guessing the same distribution that Cloudera prefers to use).*
- Some kind of support.
In other words, it’s a lot like getting Cloudera Enterprise,* plus some hardware, plus some other stuff.
*Edit: About 2 minutes after I posted this, I got email from Cloudera CEO Mike Olson. Yes, the Oracle Big Data Appliance bundles Cloudera Enterprise.
That raises an anyway recurring question: What exactly is Cloudera Manager? Read more
| Categories: Cloudera, Data warehouse appliances, Hadoop, MapReduce, Oracle | Leave a Comment |
Big data terminology and positioning
Recently, I observed that Big Data terminology is seriously broken. It is reasonable to reduce the subject to two quasi-dimensions:
- Bigness — Volume, Velocity, size
- Structure — Variety, Variability, Complexity
given that
- High-velocity “big data” problems are usually high-volume as well.*
- Variety, variability, and complexity all relate to the simply-structured/poly-structured distinction.
But the conflation should stop there.
*Low-volume/high-velocity problems are commonly referred to as “event processing” and/or “streaming”.
When people claim that bigness and structure are the same issue, they oversimplify into mush. So I think we need four pieces of terminology, reflective of a 2×2 matrix of possibilities. For want of better alternatives, my suggestions are:
- Relational big data is data of high volume that fits well into a relational DBMS.
- Multi-structured big data is data of high volume that doesn’t fit well into a relational DBMS. Alternative: Poly-structured big data.
- Conventional relational data is data of not-so-high volume that fits well into a relational DBMS. Alternatives: Ordinary/normal/smaller relational data.
- Smaller poly-structured data is data for which dynamic schema capabilities are important, but which doesn’t rise to “big data” volume.
Some big-vendor execution questions, and why they matter
When I drafted a list of key analytics-sector issues in honor of look-ahead season, the first item was “execution of various big vendors’ ambitious initiatives”. By “execute” I mean mainly:
- “Deliver products that really meet customers’ desires and needs.”
- “Successfully convince them that you’re doing so …”
- “… at an attractive overall cost.”
Vendors mentioned here are Oracle, SAP, HP, and IBM. Anybody smaller got left out due to the length of this post. Among the bigger omissions were:
- salesforce.com (multiple subjects).
- SAS HPA.
- The evolution of Hadoop.
Hadapt is moving forward
I’ve talked with my clients at Hadapt a couple of times recently. News highlights include:
- The Hadapt 1.0 product is going “Early Access” today.
- General availability of Hadapt 1.0 is targeted for an officially unspecified time frame, but it’s soon.
- Hadapt raised a nice round of venture capital.
- Hadapt added Sharmila Mulligan to the board.
- Dave Kellogg is in the picture too, albeit not as involved as Sharmila.
- Hadapt has moved the company to Cambridge, which is preferable to Yale environs for obvious reasons. (First location = space they’re borrowing from their investors at Bessemer.)
- Headcount is in the low teens, with a target of doubling fast.
The Hadapt product story hasn’t changed significantly from what it was before. Specific points I can add include: Read more
| Categories: Hadapt, Hadoop, MapReduce, PostgreSQL, Theory and architecture, Workload management | 4 Comments |
MarkLogic’s Hadoop connector
It’s time to circle back to a subject I skipped when I otherwise wrote about MarkLogic 5: MarkLogic’s new Hadoop connector.
Most of what’s confusing about the MarkLogic Hadoop Connector lies in two pairs of options it presents you:
- Hadoop can talk XQuery to MarkLogic. But alternatively, Hadoop can use a long-established simple(r) Java API for streaming documents into or out of a MarkLogic database.
- Hadoop can make requests to MarkLogic in MarkLogic’s normal mode of operation, namely to address any node in the MarkLogic cluster, which then serves as a “head” node for the duration of that particular request. But alternatively, Hadoop can use a long-standing MarkLogic option to circumvent the whole DBMS cluster and only talk to one specific MarkLogic node.
Otherwise, the whole thing is just what you would think:
- Hadoop can read from and write to MarkLogic, in parallel at both ends.
- If Hadoop is just writing to MarkLogic, there’s a good chance the process is properly called “ETL.”
- If Hadoop is reading a lot from MarkLogic, there’s a good chance the process is properly called “batch analytics.”
MarkLogic said that it wrote this Hadoop connector itself.
| Categories: Clustering, EAI, EII, ETL, ELT, ETLT, Hadoop, MapReduce, MarkLogic, Parallelization, Workload management | 2 Comments |
The cool aspects of Odiago WibiData
Christophe Bisciglia and Aaron Kimball have a new company.
- It’s called Odiago, and is one of my gratifyingly more numerous tiny clients.
- Odiago’s product line is called WibiData, after the justly popular We Be Sushi restaurants.
- We’ve agreed on a split exclusive de-stealthing launch. You can read about the company/founder/investor stuff on TechCrunch. But this is the place for — well, for the tech crunch.
WibiData is designed for management of, investigative analytics on, and operational analytics on consumer internet data, the main examples of which are web site traffic and personalization and their analogues for games and/or mobile devices. The core WibiData technology, built on HBase and Hadoop,* is a data management and analytic execution layer. That’s where the secret sauce resides. Also included are:
- REST APIs for interactive access.
- Import/export tools, including JDBC access.
- Management tools.
- Analytic libraries — data mining, predictive analytics, machine learning, and so on.
The whole thing is in beta, with about three (paying) beta customers.
*And Avro and so on.
The core ideas of WibiData include:
- ALL data pertaining to a single user (or mobile device) is kept in a single, possibly very long, HBase row.
- There are two primary operators in WibiData, Produce and Gather.
- Produce operates on single rows. It can operate on one row at HBase speed (milliseconds) if you need to inform an interactive user response. Or it can operate on the whole database in batch via Hadoop MapReduce.
- It is reasonable to think of Produce as mainly doing two things. One is the aforementioned serving of data out of WibiData into interactive applications. The other is scoring, classifying, recommending, etc. on individual users (i.e. rows), in line with an analytic model.
- Gather typically operates on all your rows at once, and emits suitable input for a MapReduce Reduce step. It is reasonable to think of Gather as being a key cog in the training of analytic models.
- HBase schema management is done at the WibiData system level, not directly in applications. There’s a WibiData HBase data dictionary, powered by a set of system tables, that specifies cell data types/record types and, in effect, primitive schemas.
| Categories: Data models and architecture, Hadoop, HBase, NoSQL, Predictive modeling and advanced analytics, Web analytics, WibiData | 12 Comments |
MarkLogic 5, and why you might care
MarkLogic is releasing MarkLogic 5. Key elements of the announcement are:
- More-of-the-same in line with MarkLogic’s core positioning.
- A new bi-directional Hadoop connector.
- A free MarkLogic Express edition, limited in license terms more than in actual features, as per Slide 27 of the deck MarkLogic graciously supplied for me to post.
Also, MarkLogic is early with a feature that most serious DBMS vendors will soon have – support for tiered storage, with writes going first to solid-state storage, then being flushed to disk via a caching-style algorithm.* And as befits a sometime search-engine-substitute, MarkLogic has finally licensed a large set of document filters, from an Australian company called Isys. Apparently, the special virtue of the Isys filters is that they’re good at extracting not only text, but metadata as well.
*If there’s a caching algorithm that doesn’t contain a major element of LRU (Least Recently Used), I don’t recall ever hearing about it.
MarkLogic seems to have settled on a positioning that, although distressingly buzzword-heavy, is at least partly based upon reality. The real part includes:
- MarkLogic is a serious, enterprise-class DBMS (see for example Slide 12 of the MarkLogic deck) …
- … which has been optimized from the getgo for poly-structured data.
- MarkLogic can and does scale out to handle large amounts of data.
- MarkLogic is a general-purpose DBMS, suitable for both short-request and analytic tasks.
- MarkLogic is particularly well suited for analyses with long chains of “progressive enhancement” (MarkLogic’s favorite term when talking about derived data).
- MarkLogic often plays the role of a content assembler and/or search engine, and the people who use MarkLogic in those ways are commonly doing things that can be described as research and analysis.
Based on that reality, MarkLogic talks a lot about Volume, Velocity, Variety, Big Data, unstructured data, semi-structured data, and big data analytics.
| Categories: Hadoop, Market share and customer counts, MarkLogic, Scientific research, Solid-state memory, Structured documents, Text | 1 Comment |
Where Datameer is positioned
I’ve chatted with Datameer a couple of times recently, mainly with CEO Stefan Groschupf, most recently after XLDB last Tuesday. Nothing I learned greatly contradicts what I wrote about Datameer 1 1/2 years ago. In a nutshell, Datameer is designed to let you do simple stuff on large amounts of data, where “large amounts of data” typically means data in Hadoop, and “simple stuff” includes basic versions of a spreadsheet, of BI, and of EtL (Extract/Transform/Load, without much in the way of T).
Stefan reports that these capabilities are appealing to a significant fraction of enterprise or other commercial Hadoop users, especially the EtL and the BI. I don’t doubt him.
| Categories: Business intelligence, Datameer, EAI, EII, ETL, ELT, ETLT, Hadoop | 2 Comments |
IBM is buying parallelization expert Platform Computing
IBM is acquiring Platform Computing, a company with which I had one briefing, last August. Quick background includes: Read more