Greenplum, a data warehousing vendor, announced advancements in their product offerings with their new “MPP Scatter/Gather Streaming” (SG Streaming) technology aimed at increasing the speed of data loading within large-scale data warehousing projects.
With data volumes and demands for right time data increasing, it becomes important for businesses to be able to load and update multiple terabytes of data in a timely fashion and in some cases, multiple times daily. Because of this, data warehousing vendors are constantly looking for ways to enhance their technology and bring more value to their customers. In general, many of these solutions are similar; therefore, added value such as loading times, the ability to support near real time, and compression rates, create slight differentiations in the performance of data warehouses and usefulness to organizations.
Greenplum’s announcement is one such move to bring more value to an organization’s data warehousing and analytics environment by offering quicker loading times and parallel data flows that extend across all nodes of the system. With a number of reference customers such as O’Reilly Media and Fox Interactive Media already touting the benefits of Greenplum’s SG Streaming technology, it seems as if customers with high data volume requirements will benefit from their new improvements.
The premise of Greenplum’s SG Streaming technology is that data loads push data from one or more source systems to every node of the database. These parallel data streams scatter data across all nodes of the database to ensure that data flows simultaneously across the nodes of the database. Records of data loading of 4 terabytes per hour have been measured and customers can take advantage of right time data loading if they have requirements for intra daily data updates. In addition, data compression and ELT (extract-load-transform) and ETLT (extract-transform-load-transform) processes can be performed in flight to help increase the effectiveness of the data transformation process. These features can be beneficial when organizations want to increase the effectiveness of their data warehousing and analytics environment.
About the Author
Lyndsay Wise is an industry analyst for business intelligence. For over seven years, she has assisted clients in business systems analysis, software selection and implementation of enterprise applications. Lyndsay is the channel expert for BI for the Mid-Market at B-eye-Network and conducts research of leading technologies, products and vendors in business intelligence, marketing performance management, master data management, and unstructured data. She can be reached at firstname.lastname@example.org. And please visit Lyndsay's blog at myblog.wiseanalytics.com.
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