Vertica Blog

Vertica Blog

machine learning

How to Code Vertica UDx

This blog post was authored by Ding-Qiang Liu. In analytic businesses supported by Vertica, complex processing logic is sometimes unavoidable. Using ANSI SQL might cause query strings to be much longer, and will slow the query with a huge volume data to query. If using Vertica SDKs, you can encapsulate that general computing logic in...

Make data analysis easier with dimensionality reduction

This blog post was authored by Anh Le. Introduction As the number of features in your data set grows, it becomes harder to work with. Visualizing 2D or 3D data is straightforward, but for higher dimensions you can only select a subset of two or three features to plot at a time, or turn to...

Machine Learning Key Terms

This blog post was authored by Soniya Shah. Machine learning seems to be everywhere these days – in the online recommendations you get on Netflix, the self-driving cars that hyped in the media, and in serious cases, like fraud detection. Data is a huge part of machine learning, and so are the key terms. Unless...
Programmer

What’s New in Vertica 9.1: Python SDK Expands

This blog post was authored by Monica Cellio. Using the Vertica SDK, you can write several types of user-defined extensions (UDxs) to add your own customizations. In a previous release the SDK added Python support for one type, scalar functions (UDSFs). In 9.1 we added Python support for transform functions (UDTFs). User-Defined Transform Functions (UDTFs)...

Vertica in Eon Mode: Revive

This blog post was authored by Soniya Shah. Overview An Eon Mode database keeps an updated version of its data and metadata in a communal storage location. After you shut down the database, the data continues to reside in communal storage. When you are ready to use the storage again, you can revive the database...

What’s New in Vertica 9.1: Precision-Recall Curve and F1-Score Machine Learning Evaluation Functions

This blog post was authored by Ginger Ni. The precision-recall curve is a measure for evaluating binary classifiers. It is a basic measure derived from the confusion matrix. In Vertica 9.1, we provide a new machine learning evaluation function PRC() for calculating precision and recall values from the results of binary classifiers. Along with the...

Unlock Data Analytics for Dynamic Workloads with Vertica 9.1

This blog post was authored by Sanjay Baronia. Today, cloud infrastructure has made it easier for organizations to consume services and deploy business applications with a pay-as-you-go, OPEX model. This provides a number of incentives to move data to the cloud, especially for variable workloads and use cases that require heavy compute for finite periods...

How do you use Vertica analytics and machine learning today?

One of the coolest aspects of Vertica is its in-database machine learning functionality. As part of our third round of product management surveys, we're asking you to take the time to answer how you use Vertica analytics today. We're hoping to get answers we can use to expand upon our growing analytics and machine learning...

What’s New in Vertica 9.1?

This blog post was authored by Soniya Shah. In Vertica 9.1 we introduce new functionality including: • Eon Mode, now available in production environments • Machine Learning Enhancements • Management Console Updates • Voltage SecureData Integration • Python UDTF • AWS Licensing Updates • Security Updates • Upgrade and Installation Changes • S3 Session Parameter...

Using Vertica Machine Learning to Analyze Smart Meter Data

Machine learning and data science have the potential to transform businesses because of their ability to deliver non-obvious, valuable insights from massive amounts of data. However, many data scientist's workflows are hindered by computational constraints, especially when working with very large data sets. While most real-world data science workflows require more than multiple cores on...
Programmer

How Cisco and Vertica empower high performance analytics for the most demanding workloads

This blog post was authored by Steve Sarsfield. Hadoop and HDFS is capable of storing massive volumes of data, but performing analytics on Hadoop can be challenging. Despite the apparent low-cost cost of Hadoop, it is best suited for data lake and data science solutions, where the number of concurrent analytical users is low. In...

AWS re:Invent 2017

Advanced Analytics Anywhere, Anytime, on Any Major Cloud. Attending AWS re:Invent this year?