Friday, October 2, 2009

The Integration of Analytic DBMS and Hadoop

Recently, tow famous vendors of analytic DBMS, Vertica and Aster-Data announced their integration with Hadoop. The analytic DBMS and Hadoop, each address distinct but complementary problems for managing large data.


Currently it is a light integration.
  • ETL, ELT, data cleansing, data mining, etc.
  • Moving data between Hadoop and Vertica.
  • InputFormat (InputSplit , VerticaRecord, push down relational map operations by parameterizing the database query).
  • OutputFormat (to existing or create a new table).
  • Easy for Hadoop developers to push down Map operations to Vertica databases in parallel by specifying parameterized queries which result in pre-aggregated data for each mapper.
  • Support Hadoop streaming interface.

Typical usages:

(1) Raw Data->Hadoop(ETL)->Vertical (for fast ad-hoc query, near realtime)
(2) Vertical -> Hadoop(ETL) ->Vertical (for fast ad-hoc query, near realtime)
(3) Vertical -> Hadoop (sophisticated query for analysis or mining)

We can expect to see tighter integration and higher performance.

[1] The Scoop on Hadoop and Vertica:
[2] Using Vertica as a Structured Data Repository for Apache Hadoop:
[3] Cloudera DBInputFormat interface:
[4] Managing Big Data with Hadoop and Vertica:


AsterData already provide in-database MapReduce.

The new Aster-Hadoop Data Connector, which utilizes Aster’s patent-pending SQL-MapReduce capabilities for two-way, high-speed, data transfer between Apache Hadoop and Aster Data’s massively parallel data warehouse.
  • ETL processing or data mining, and then pull that data into Aster for interactive queries or ad-hoc analytics on massive data scales.
  • The Connector utilizes key new SQL-MapReduce functions to provide ultra-fast, two-way data loading between HDFS (Hadoop Distributed File System) and Aster Data’s MPP Database.
  • Parallel loader.
  • LoadFromHadoop: Parallel data loading from HDFS to Aster nCluster.
  • LoadToHadoop: Parallel data loading from Aster nCluster to HDFS.

Key advantages of Aster’s Hadoop Connector include:
  • High-performance: Fast, parallel data transfer between Hadoop and Aster nCluster.
  • Ease-of-use: Analysts can now seamlessly invoke a SQL command for ultra-simple import of Hadoop-MapReduce jobs, for deeper data analysis. Aster intelligently and automatically parallelizes the load.
  • Data Consistency: Aster Data's data integrity and transactional consistency capabilities treat the data load as a 'transaction', ensuring that the data load or export is always consistent and can be carried out while other queries are running in parallel in Aster.
  • Extensibility: Customers can easily further extend the Connector using SQL-MapReduce, to provide further customization for their specific environment.

The typical usages are similar to Vertica.

[1] Aster Data Announces Seamless Connectivity With Hadoop:
[2] DBMS2 - MapReduce tidbits
[3] AstaData Blog: Aster Data Seamlessly Connects to Hadoop,

Another Integration of Analytic DBMS and Hadoop case is HadoopDB project.

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