Windowing Operations over Timeseries Data, Part 2

This post is a continuation of the previous post, Windowing Operations over Timeseries Data in Paradigm4. Repeated from part 1 of the post:The SciDB array, showing elevation and time as dimensions Now, let’s look at how this array could be represented in SciDB, as...

Windowing Operations over Timeseries Data in Paradigm4

Windowing operations—aggregating functions over a rolling subset of data—are useful in many applications. For example, rolling average calculations can help smooth over short-term fluctuations, thereby revealing long-term trends. Sensors used for testing global...

Product Tip: Loading Data Into SciDB from HDFS

Some customers have inquired about loading files from HDFS. Current versions of SciDB support this. Remember, HDFS is not a specific file format, it is a file system that uses distributed storage. HDFS can store CSV files. Although HDFS files are not directly visible...

What Does A SciDB Cluster Look Like?

We get asked about hardware configuration for SciDB clusters. We start with a rule of thumb for the ratios among SciDB instances, CPU cores, RAM, and disks. That rule of thumb is that each SciDB instance should have: 1-2 CPU cores :: 4-8 GB RAM :: 1 Disk. For example,...

Subscribe for Newsletter