Vertica Blog

Vertica Blog

Analytics

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Evaluating Classifier Models in Vertica

Co-authored by Elizabeth Michaud Vertica provides an out-of-the box machine learning toolset that covers a complete data science workflow. The toolset includes several distributed functions for evaluating both classifier and regressor machine learning models. The goal of this blog post is to demonstrate how you can use the built-in functions for evaluating the prediction performance...
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Delivering personalized retail promotions using AI and predictive analytics

SO1.ai helps retailers leverage customer basket and loyalty data to deliver smart, automated promotional decisions at scale across millions of customers. The solution is fully automated and designed to fit a retailer’s specific business goals (i.e. revenues, profit optimization, customer satisfaction or brand sales). Want to learn more about the S01.ai solution?
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Announcing Vertica Version 9.2.1 – Take Analytics Efficiency to the Next Level

This week, some very cool things have gone into the newest version of the that make your analytics fly and save on your AWS budget. The theme for this release is Improving Vertica in Eon Mode Integration with Amazon S3, and there are a whole bunch of new features designed to do exactly that. But,...
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In-Database Machine Learning 1 – Why Would You Do That?

Co-authored with Paige Roberts. A lot of modern like Vertica allow you to do machine learning from end to end, right in the database, rather than moving and transforming the data first into something like a Spark dataframe or a Python data structure. Whenever people hear about this capability, they have two questions. The first...
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Data Analytics and Machine Learning for Start-Ups on the Fast Track

Tuesday, 4/30/19 Workbar Back Bay 6:00-8:00 PM Are you a data-driven start-up that makes decisions based on insight from high-performance analytics and machine learning? Would you like to choose the best analytical tool/ML for the job beyond limited open source offerings, but don’t have the budget? Do you want to hear best practices on how...
Vertica One on One with Davin Potts, CEO Appliomics, Founder KNIME, Core Python Committer

One on One with Davin Potts: 3. Exciting News for Upcoming Python Release 3.8

At the recent Data Day Texas event, I sat down with Davin Potts and had a long conversation about a wide variety of subjects. I divided the conversation into multiple chunks by subject, and have been posting them one chunk at a time. In the , we discussed the wide variety of programming languages and...
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Data Day Texas: Keep Your Architecture Open and Avoid Mindset Lock-in

Data Day Texas is an event in Austin that was started about nine years ago by an old acquaintance of mine, Lynn Bender, who founded Global DataGeeks. The one big theme that struck me this year as running through the whole conference was the highly cooperative landscape that has developed between proprietary and open source...

Use MERGE to Update 1 Million Rows in 2 Seconds

This blog post was co-authored by Yassine Faihe, Michael Flower, and Moshe Goldberg. Updating One Million Records in Two Seconds To illustrate the true power of MERGE, this article describes how we used MERGE to demonstrate Vertica's performance at scale. SQL MERGE statements combine INSERT and UPDATE operations. They are a great way to update...

Analyze Mismatched Series with Event Series Joins

Event series occur in tables with a time column, most typically a TIMESTAMP data type. In Vertica, you perform an event series join to analyze two series in different tables when their measurement intervals don't align, such as with mismatched timestamps.

The Benefits of Single Node Queries

If your organization deals with low latency, high concurrency applications and queries, you can benefit from having as few nodes as possible involved in each query.

Time Series Analytics: The Hunt for “Missing Link” in Data

Time series analytics is a little-known, but very powerful Vertica tool. In Vertica, the TIMESERIES clause and time series aggregate functions normalize data into time slices. Then they interpolate missing values that fill in the gaps.Using time series analytics is useful when you want to analyze discrete data collected over time, such as stock market...
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The Real-Time Unicorn

In the first of this multi-part series, I?ll address one of the most common myths my colleagues and I have to confront in the Big Data marketplace today: the notion of ?real-time? data visibility. Whether it?s real-time analytics or real-time data, the same misconception always seems to come up. So I figured I?d address this,...