The Greenplum Database is undoubtedly an exceptional database that falls under the analytical MPP (massively parallel processing) umbrella. It is designed to handle complex query workloads and real-time fast data retrieval. However, its systems are not well suited for processing high volume transactions, which typically runs on online transaction processing (OLTP) databases, such as DB2, Oracle and SQL Server. There are a number of programs designed to deliver high volume data into the Greenplum database in a reliable and efficient manner from OLTP databases. Database replication into Greenplum solutions provide a viable way to leverage real-time analytics to create vital insights into the data.
Quick access to information allows organizations to operate optimally in today’s fast-paced business environment. Real-time analytics and data play a critical role when it comes to delivering value to shareholders, employees and customers. One of the most innovative and non-intrusive techniques to capture changes to the data without slowing transactional processing is to use CDC (Change Data Capture) technique out of the OLTP log and into Greenplum Database. To implement high volume changes into Greenplum, users need the high volume replicator (HVR). The replication system comes with end-to-end load capabilities, which make it easier to replicate high volumes of data in real-time.
The system is capable of integrated loads using CDC to prevent data losses when source databases like Ingres and PostgreSQL are active. In addition, the replicating method allows automatic data type translation and DDL generation for the Greenplum schema based on the source database. The high volume replicator also enables detailed comparisons to ensure that tables are in sync. Burst optimization boosts system performance during real-time synchronization into the Greenplum MPP database. These capabilities eliminate the need to refresh huge amounts of data on a daily basis, which strains the source databases. In turn, organizations are in a position to update their analytics platform or data warehouse in sync with the production data.
Some replicators take an initial snapshot of a given table from the source database like Oracle and load it into Greenplum. The procedure allows it to automatically create a log table, which in turn leads to capture of the delete, insert and update commands. The commands are typically executed in the Oracle or SQL Server system. Once identified, the changes are loaded into the Greenplum database in bulk. An optimized and sophisticated Greenplum function then applies the changes. Some Change Data Capture (CDC) techniques lack the capacity to address the requirements more comprehensively. They tend to read the database log files directly rather than making use of triggers, which are arguably more effective.
