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Vertica Announces Vertica 12 for Future-Proof Analytics
Latest version of analytics database enables more deployment flexibility, advanced analytics, and enhanced machine learning
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Vertica Inside – Embedded Analytics at Scale
Seize the huge growth opportunity for OEM software developers
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In today’s data driven world, where effective decisions are based on a company’s ability to access information in seconds or minutes rather than hours or days, selecting the right analytical database platform is critical.
While the volume of our data universe has grown exponentially over the past two decades, many companies struggle with underlying data infrastructure not designed or architected for high performance analytics at massive scale. This means that a database decision made 15 years ago could be impacting your ability to compete today and tomorrow. For many companies, that decision was to adopt PostgreSQL, an open source, ANSI SQL database.
In the beginning, most solutions would prove successful on PostgreSQL, but over time it has become clear that more muscle is needed. If your organization is struggling to achieve acceptable analytics performance with PostgreSQL hosting or regularly need PostgreSQL support, consider adopting a massively parallel processing (MPP) database like Vertica.
PostgreSQL just isn’t designed to do analytic-type queries. Those are big aggregations, and Postgres, even though it is a great relational database, is really tailored for single-record lookup.
– Chris “CB” Bohn, Senior Data Engineer, Etsy
Nimble Storage deployed Vertica Analytics Platform to replace its legacy open-source database management (DBM) system, PostgreSQL
As with many data management deployments, the company experienced improved productivity and avoided additional hardware costs. However, Nimble Storage was also able to identify and close more deals with the Vertica Analytics Platform, shortening their sales cycle and automating many customer service tasks.
ROI: 287%
Payback: 0.5 years
Annual Benefit: $13.6M
Considering a transition away from PostgreSQL server? Here are a few things to consider
Over time, analytic data volumes will continue to soar. Companies with solutions on PostgreSQL (and similar entry-level or legacy platforms) need to make sure that they transition to a proven platform. Given the evolving yet strategic importance of the marketplace, the technical architecture for an analytic database platform should be, among other things:
Scalable
The solution should be scalable in both performance capacity and incremental data volume growth. Make sure the proposed solution scales in a near-linear fashion and behaves consistently with growth in all of database size, number of concurrent users, and complexity of queries.
Powerful
Designed for complex decision support activity in a multi-user mixed workload environment. Check on the maturity of the optimizer for supporting every type of query with good performance and to determine the best execution plan based on changing data demographics.
Manageable
The solution should be manageable through minimal support tasks requiring DBA/system administrator intervention. It should provide a single point of control to simplify system administration.
| To be successful, analytic databases need to provide | |||
|---|---|---|---|
| High-quality data | |||
| Low long-term total cost of ownership | |||
| Excellent query performance and interactive usage | |||
| Ability to get to real-time feeds | |||
| A platform to support mixed, unpredictable workloads | |||
| A scalable path forward as data needs grow | |||
Webcast: Transitioning from PostgreSQL to an Advanced Analytics Database
On this webcast you will learn the criteria for choosing an analytical database, the process for transitioning away from PostgreSQL, and how three leading data-driven companies successfully transitioned to Vertica for a scalable, high-performance analytics solution.
Transitioning from PostgreSQL to an Analytical Database for Higher Performance and Massive Scale
Read this McKnight white paper and PostgreSQL tutorial to learn:
- Which criteria to consider for an analytical database
- The process for transitioning away from PostgreSQL
- Transition success stories from Etsy, TravelBird and Nimble Storage
