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.
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 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 trades and performance, but find that there are gaps in your collected data.
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.