Summary

This chapter's focus comes full circle to the reasons for using Redis in the first chapter of Master Redis. Redis's role as a "glue" technology is well-suited for connecting various data sources to end targets for many extract-transform-load workloads. We saw a simple example of loading a DP.LA dataset that contains the metadata for a collection of images and other content from the University of Illinois. The speed difference was orders of magnitude faster using the bulk loading options in Redis, where you create Redis protocol (RESP) directly from the incoming data source (the extract and transform steps) to finally being loaded with either Netcat or a special mode in the Redis-cli program. We touched upon minimum security strategies for protecting your Redis instance. We then finished up this chapter by examining two common machine learning techniques – Naïve Bayes and simple linear regression – and showed how Redis can be used for turning raw data streams into information and...