Looker is excited to support HPE Vertica OnDemand, HPE’s cloud deployment option for their high performing data warehouse. The value of Looker’s data platform has already been proven for many HPE Vertica customers, and now HPE has made it even easier to deploy Vertica in the way that your business requires: either in the cloud and on-prem.
Looker is the only analytics and BI tool that can fully utilize all the capabilities of HPE Vertica:
Looker works in-database, directly leveraging the power of the underlying Vertica engine and enabling full access to Vertica’s proprietary advanced analytics capabilities.
Because Looker directly connects to Vertica, data availability is real-time: there is no latency between when data is available in Looker and when it’s available in the data warehouse. Your entire organization can access all of the data...not just a subset of it.
In-database transformation is accomplished via the LookML modeling layer, which serves as an abstraction of SQL to define reusable dimensions and measures that can be exposed to your end users - so that all reporting and analytics are completed from a single source of truth.
Looker can be deployed both in the cloud OR on-premises, depending on what makes most sense for you.
Because of Looker’s in-database architecture and modeling layer, Looker can enable exploration and visualization of some of Vertica’s most unique features natively:
Advanced Analytical Functions in Data Modeling: Vertica includes a number of advanced analytical capabilities that make it unique in the market. Because Looker writes SQL directly to Vertica, these functions can be directly leveraged in the LookML modeling layer, for example Vertica Place Geospatial Analytics and Distributed R.
Flex Tables and Semi-Structured Data: Vertica’s Flex Zone allows semi-structured data to be quickly loaded and analyzed using Vertica, without having to create schemas up front. The LookML modeling layer allows you to logically create any schema you would like, natively leveraging the parsing functions, making semi-structured data much more accessible to organizational analysis. For example, Twitter data in JSON format, as detailed in this post.