Vertica

Archive for the ‘Dragline’ Category

What’s New in Dragline (7.1.0): Installing HP Vertica Pulse

HP Vertica 7.1.0 introduces the general availability of HP Vertica Pulse, our add-on sentiment analysis package for HP Vertica. Pulse provides a suite of functions that allow you to analyze and extract the sentiment from text, directly from your HP Vertica database. For example, you can use HP Vertica Pulse to analyze sentiment from Tweets or online product reviews to get a feel for how satisfied your customers are about your products or services.

HP Vertica Pulse automatically discovers attributes included in text and scores them using a built-in system dictionary. You can tune user-dictionaries to detect certain words or phrases, to determine how words are scored, and to filter out attributes that are of no interest to you. Because of this flexibility, you can tune HP Vertica Pulse to work for your specific business needs.

Currently, HP Vertica Pulse allows you to analyze English language text only. You can download HP Vertica Pulse as an add-on package for your Enterprise Edition or as a trial for your Community Edition, from my.vertica.com. Additionally, the Innovations section of the HP Vertica Marketplace offers a beta version of Pulse for Spanish only. Take a look at this video to learn how to install Pulse and stay tuned for our next video, ‘Using Pulse’.

HP Vertica Pulse documentation.

Live Aggregate Projections with HP Vertica

Projections

The Dragline release of HP Vertica offers an exciting new feature that is unique in the world of big data analytics platforms. We now offer Live Aggregate projections as part of the platform. The impact is that you can really fly through certain types of big data analytics that typically grind down any analytics system.

Before I get into that, however, it’s important to back up and give some background on HP Vertica projections. Many databases use indexes and materialized views to improve query performance. However, these secondary structures have drawbacks. Materialized views and indexes can bloat and become a very inefficient way to optimize data analytics. They can be time-consuming to keep up-to-date during data loading, can require frequent rebuilding, and they can be tedious to manage.

HP Vertica has always had a better solution to materialized views and indexes. Vertica has no raw uncompressed base tables, no materialized views, and no indexes. Our optimizations consist of optimized collections of table columns, which we call “projections”. There are several different types of projections. At the core, a projection could be an optimized collection of pre-sorted columns than may contain some or all of the columns of one or more tables. A projection that joins one or more tables is called a pre-join projection with the benefit of speeding up joins. A projection that contains a pre-calculated aggregate function such as average, top-K, sum, etc. is called an aggregate projection, which is a new feature of our Dragline release.

What’s cool about aggregate projections is that queries that rely on aggregate functions like SUM, MIN/MAX and COUNT are no longer bog down the system with excessive I/O and calculation. Now, these calculations can be calculated and updated as data loads. The HP Vertica query optimizer creates the projections and always keeps them up-to-date, ready to answer your aggregate queries without having to grind and churn through the data.

In real life analytics situations, this new feature accelerates the speed and performance by computing metrics on the data as it arrives for targeted and personalized analytics without programming accelerator layers. It’s particularly powerful if you’re implementing smart metering applications, for example, where you are helping your customers understand their usage and compare it to others in the neighborhood. The aggregate information is available in the projection without having to recalculate it over and over again so your data analytics system is free to take on other workloads without the fuss.

Speeding up aggregate functions should help with many use cases for today and tomorrow. We live in a world where data volumes from smart devices such as smart buildings, mobile phones, GPS devices and sensors are ever-increasing. We’re finding value in leveraging this data to predict usage based on history, predict equipment failure, maximize heating/cooling/lighting costs, detect fraud and more. HP Vertica continues to believe that projections offer a superior solution to materialized views and indexes. Projections remove the trade-off between performance and data size and offer the ultimate in flexibility for fast big data analytics.

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