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Part-1 Introduction to Big SQL   When considering SQL-on-Hadoop, the most fundamental question is: What is the right tool for the job? For interactive queries that require a few seconds (or even milliseconds) of response time, MapReduce (MR) is the wrong choice. On the other hand, for queries that require massive scale and runtime fault tolerance, an MR framework works well. MR was built for large-scale processing on big data, viewed mostly as “batch” processing. As enterprises start using Apache Hadoop as a central data repository for all data — originating from sources as varied as operational systems, sensors, smart devices, metadata and internal applications — SQL processing becomes an optimal choice. A fundamental reason is that most enterprise data management and analytical tools rely on SQL. As a tool for interactive query execution, SQL processing (of relational data) benefits from decades of research, usage experience and optimizations. Clearly, the SQL sk...

What is BIG SQL? This question coming to every software professional. We all know what is SQL. SQL is a language is used to access data from RDBMS. Big SQL- to provide ANSI SQL access to data across any system from Hadoop, via JDBC or ODBC - seamlessly whether that data exists in Hadoop or a relational data base. This means that developers familiar with the SQL programming language can access data in Hadoop without having to learn new languages or skills. There are different types of queries in Bg SQL: Point queries - These are queries that need to return very fast, like HBase queries, for example.  In these types of queries, you cannot use MapReduce Big ad-hoc queries - In larger, more complex jobs MapReduce parallelism becomes very important to be able to break down these massive data sets. Standards-compliant via JDBC -This is how most applications access databases and in this usage pattern, you can use the same to access your Hadoop-based data store. St...