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Sumo Logic and UIs for text-oriented data : DBMS 2 : DataBase Management System Services

http://www.dbms2.com/2012/02/06/sumo-logic-and-uis-for-text-oriented-data/ Sumo Logic is text indexing/Lucene-based. Thus, it is reasonable to think of Sumo Logic as “Splunk-like”.
http://www.dbms2.com/2012/02/06/comments-on-the-2012-forrester-wave-enterprise-hadoop-solutions/

Comments on the 2012 Forrester Wave: Enterprise Hadoop Solutions : DBMS 2 : DataBase Management System Services

February 6, 2012 Forrester has released its Q1 2012 Forrester Wave: Enterprise Hadoop Solutions.
http://www.dbms2.com/2011/11/03/marklogic-hadoop-connector/ 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.

MarkLogic’s Hadoop connector : DBMS 2 : DataBase Management System Services

http://www.dbms2.com/2011/10/03/teradata-unity-active-replication/ October 3, 2011 Teradata is having its annual conference, Teradata Partners, at the same time as Oracle OpenWorld this week.

Teradata Unity and the idea of active-active data warehouse replication | DBMS 2 : DataBase Management System Services

http://www.dbms2.com/2011/06/19/investigative-analytics-derived-data/

Investigative analytics and derived data: Enzee Universe 2011 talk | DBMS 2 : DataBase Management System Services

The talk concept started out as “advanced analytics” (as opposed to fast query, a subject amply covered in the rest of any Netezza event), as a lunch break in what is otherwise a detailed “best practices” session. So I suggested we constrain the subject by focusing on a specific application area — customer acquisition and retention, something of importance to almost any enterprise, and which exploits most areas of analytic technology.
*Similar things seem true of ParAccel, but most of the other serious columnar analytic DBMS aren’t actually MPP (Massively Parallel Processing) yet. More precisely, they have shared-everything architectures, especially on the storage level.

The Vertica story (with soundbites!) | DBMS 2 : DataBase Management System Services

http://www.dbms2.com/2011/06/20/vertica-release-5/
“Time travel”/snapshotting — preserving the state of the database at previous points in time. http://www.dbms2.com/2011/06/20/temporal-data-time-series-and-imprecise-predicates/

Temporal data, time series, and imprecise predicates | DBMS 2 : DataBase Management System Services

Dirty data, stored dirt cheap | DBMS 2 : DataBase Management System Services

June 4, 2011 A major driver of Hadoop adoption is the “big bit bucket” use case. http://www.dbms2.com/2011/06/04/dirty-data-stored-dirt-cheap/
http://www.dbms2.com/2011/06/04/hardware-for-hadoop/

Hardware for Hadoop | DBMS 2 : DataBase Management System Services

June 4, 2011 After suggesting that there’s little point to Hadoop appliances , it occurred to me to look into what kinds of hardware actually are used with Hadoop.

Why you would want an appliance — and when you wouldn’t | DBMS 2 : DataBase Management System Services

Data warehouse and other data management appliances are on the upswing. Oracle is pushing Exadata.

Transparent sharding | DBMS 2 : DataBase Management System Services

February 24, 2011 When databases are too big to manage via a single server, responsibility for them is spread among multiple servers. There are numerous names for this strategy, or versions of it — all of them at least somewhat problematic.