Power Your Big Data Analytics With Pivotal Greenplum Database

Big data and analytics have been receiving attention for a few years, but many long-standing customers are unclear about how to move from traditional databases to modern concepts of data analytics. These customers worry about practical situations of lack of available skills in deploying modern infrastructure with compatibility to original systems, difficulties finding approaches to processing large-scale data, and other concerns make data analytics jobs slow and sometimes painful. Most of today’s general-purpose relational databases (e.g., Oracle, Microsoft SQL Server) originated as OLTP systems. Their shared-disk or shared-everything architectures are optimized for high-transaction rates at the expense of analytical query performance and concurrency. In contrast, Pivotal offers the Greenplum Database (GPDB), which is an extensible relational database platform that uses a shared-nothing, massive parallel processing (MPP) based architecture built atop commodity hardware to vastly accelerate the analytical processing of big data. Recent reports from Gartner highly scored Pivotal GPDB based on existing customer implementations and their experiences with data warehouse DBMS products *. This talk will briefly introduce (1) the architecture of Pivotal GPDB that provides automatic high-performance parallelization of data loading and data, (2) GPDB’s extensive and growing library of in-database analytic functions, and (3) the capability to build up a comprehensive big data platform around Pivotal GPDB. I will provide examples of how data science teams may transform billions of customer records to tackle the real-world problem of identity resolution in one minute. I will also discuss our plan of making Pivotal Greenplum Database open-source in the coming quarters.