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Vertica Blog

Analytics

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.

Using Vertica Place to Avoid Cambridge Parking Tickets

Using data sets, the geospatial functionality in Vertica, and R, I was able to create a visualization that provides insight into which metered parking spaces get the most tickets.

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.

vertica.dplyr Part 2: Using vertica.dplyr in the Machine Learning and Predictive Analytics Pipeline

Last week we announced the open-source release of vertica.dplyr, an R package that simplifies accessing the fast and scalable SQL analytics of HP Vertica by enabling the popular semantics of dplyr.

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

Time series analytics is a little-known, but very powerful HP Vertica tool. In HP 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 trades and performance, but find that there are gaps in your collected data.

Gasoline Price Prediction Using Vertica and Distributed R – Part 2: Building the Model

Use two HP Haven products to build a predictive model in R for USA gasoline prices.

Gasoline Price Prediction Using Vertica and Distributed R – Part 1: Installation

Use two HP Haven products to build a predictive model in R for USA gasoline prices.

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, define what “real-time” really means, and provide readers some advice on how to approach this topic in a productive way.