What You Need to Know About Real Time Architectures for IoT / Time Series Problem Sets

In previous posts, I discussed that not a single time series database or product rules all solutions, however, there are some common patterns that are reusable to allow a time series solution to scale to the problem set…with one key caveat — integration. It would be a bit presumptuous to propose a single technology such as Phoenix/HBase or InfluxDB as the be-all and end-all for every time series use case, because as I discussed previously, they are not. The problem set is too broad. Instead, what I would like to explore is leveraging architectural patterns and practices for time series. In this and the next few posts, I will focus on the lambda and kappa (event sourcing) architectures, specifically optimizing them for time series. These patterns use a combination of technology that allow them to scale to the need of the problem, but additionally provide a level of flexibility and protection that one product on its own would have a tough time replicating.

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BIg Data,AI,iOT,Lambda,iOT Architecture,Big Data Architecture