Vertica Blog

Vertica Blog

machine learning

Data Preparation Tools – Technical Brief

Curtis Bennett authored this blog Vertica supports a number of industry standard data preparation tools for use in the data science life-cycle. In addition to the functions described here, Vertica has a wide array of analytic capabilities which can be leveraged for additional data preparations including time-series analytics (with missing value imputation), analytic windowing and...
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vertica-python Becomes Vertica’s First Officially Supported Open Source Database Client

Tom Wall authored this post. We are very excited to announce that vertica-python, the open source python database client for Vertica created by Uber Technologies Inc., will officially become a supported database client for Vertica! This is a great milestone for Vertica and our community of users and developers. Not only do we expect to...
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Thanks, Google!

This week began with a compliment from Google that made me so proud on behalf of Vertica! On Wednesday, July 25, at the Google Cloud Next 2018 event, Google launched a Machine Learning (ML) beta with two algorithms – linear and logistic regression – and also confirmed that these new machine learning functions could be...
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The Internet of Things (IoT) and Smart Metering

Every month, my husband receives a driver’s report from Chevrolet, providing him with “feedback” on his driving behavior – everything from the intensity of his braking to the number of lane changes without a blinker to the distance and speed he drove in his Silverado truck. The report also provides specific details on the upcoming...
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Announcing Vertica in Eon Mode for Amazon Linux 2, now available in AWS Marketplace

Vertica is a blazingly fast SQL analytics database, enabling enterprises to access and derive meaningful insight into big data in sub-seconds or minutes rather than hours or days. Vertica powers the world’s most data driven organizations, delivering unmatched speed and scale with the full suite of advanced analytics and in database machine learning. Vertica for...

Introducing the VerticaPy Library for Jupyter Notebooks

One of the coolest things about working at Vertica is our amazing intern program, which often leads to full-time hires. Last year, the VerticaPy library, also known as vpython, was started as an internship project by Badr Ouali. A year later, he works for Vertica full time and has seen his project through into an...
Modern Database Analytics

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...
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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...