|By Srinivasan Sundara Rajan||
|April 24, 2013 05:00 PM EDT||
Traditional RDBMS & New Data Processing
Over the past two decades relational databases have been most successful in serving large scale OLTP and OLAP applications across enterprises. However, in the past couple of years with the advent of Big Data processing, especially for processing unstructured data coupled with the need for processing massive quantities of data, made the industry to look into Non RDBMS solutions. This has lead into the popularity of NOSQL databases as well as massively parallel processing frameworks.
However the traditional RDBMS were quick to react and added several Big Data features as part of their offering so that the enterprises with a heavy investment of traditional RDBMS can have the best of both worlds by properly leveraging these new features.
The following sections provide ideas about Big Data features in the popular SQL Server databases; a similar analysis will be performed against Oracle also in a later article.
1. Column Store Indexes
Column oriented databases differs from RDBMS like SQL Server such that they store data tables as sections of columns of data rather than rows of data. This has proven to be advantageous in certain situations where aggregates are computed over large number of similar data items.
Columnstore index, a feature in SQL Server 2012, groups and stores data for each column and then joins all the columns to complete the whole index. This differs from traditional indexes that group and store data for each row and then join all the rows to complete the whole index. For some types of queries, the SQL Server query processor can take advantage of the columnstore layout to significantly improve query execution times.
2. Hadoop Connectors
The SQL Server-Hadoop Connector is a Sqoop-based connector that facilitates efficient data transfer between SQL Server and Hadoop. Sqoop supports several databases including HDFS. This connector is bidirectional. You can import as well as export the data.
With SQL Server-Hadoop Connector, you can export data from:
- delimited text files on HDFS to SQL Server
- sequenceFiles on HDFS to SQL Server
- hive Tables to tables in SQL Server
3. Full Text Search
There is no question about Sql Server's ability to process relational data and perform queries using JOINs and other traditional means. However much of the Big Data processing needs of the enterprise goes towards processing unstructured data which is generally in the form of textual data.
Full-Text Search in SQL Server lets users and applications run full-text queries against character-based data in SQL Server tables. Before you can run full-text queries on a table, the database administrator must create a full-text index on the table. The full-text index includes one or more character-based columns in the table.
After columns have been added to a full-text index, users and applications can run full-text queries on the text in the columns. These queries can search for any of the following:
- One or more specific words or phrases (simple term)
- A word or a phrase where the words begin with specified text (prefix term)
- Inflectional forms of a specific word (generation term)
- A word or phrase close to another word or phrase (proximity term)
- Synonymous forms of a specific word (thesaurus)
- Words or phrases using weighted values (weighted term)
4. Windows Azure SQL Federation/ Distributed Partition Views
Though not directly related to SQL Server, Windows Azure SQL databases are just the cloud version of the SQL server and this feature provides massively parallel processing capabilities on the database work load on cloud.
SQL Database with federation is a way to achieve greater scalability and performance from the database tier of your application through horizontal partition. One or more tables within a database with federation are split by row and portioned across multiple databases known as federation members. A federation is defined by a federation distribution scheme, or federation scheme. The federation scheme defines a federation distribution key, which determines the distribution of data to partitions within the federation.
The equivalent feature on the SQL Server database is known as DPV (Distributed Partition View). The primary SQL Server feature that allows transparent scale-out is the distributed partitioned view (DPV), sometimes referred to as federated view. In some cases, DPVs are used to help manage very large databases (VLDBs). Instead of creating and maintaining a multi-terabyte database, several smaller databases within the same instance are created.
5. Map Reduce Integration / Polybase
PolyBase is a breakthrough new technology on the data processing engine in SQL Server 2012 Parallel Data Warehouse designed as the simplest way to combine non-relational data and traditional relational data in your analysis.
Polybase unifies the relational and non-relational worlds at the query level. It provides the following Big Data processing features:
- Integrated Query: Accepts a standard T-SQL query that joins tables containing a relational source with tables in a Hadoop cluster without needing to learn MapReduce.
- Advanced query options: Apart from simple SELECT queries, users can perform JOINs and GROUP BYs on data in the Hadoop cluster.
- Polybase is an exciting new technology and requires a separate coverage, but the above information just provides an introduction.
Traditional high performance RDBMS like SQL Server have their strengths. They are very strong in maintaining the data integrity and quality in the form of constraints, foreign keys and other validation mechanisms. They are also strong in transactional integrity by providing superior locking model, automatic dead lock resolution, etc. However initially they are not found to adjust to Big Data processing needs of enterprises.
With the enhancements in the products made by respective vendors , now databases like Sql Server have been enhanced with big data processing features and makes them the best candidate for enterprises looking for best of the breed features between traditional RDBMS and Big Data processing systems, and to leverage the best of existing investments.
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