Summary
In this chapter, we looked at a possible architecture for big data analytics. We discussed all the different data dimensions (the four Vs) and how to ingest data coming at different speeds. We also looked at various processing engines to process real-time and batch time series data before we can surface the processed data and analytics to applications and dashboards and share with the other teams. It is important to note that this is just one of the possible architectures. You can build a similar architecture using Azure Databricks or Azure HDInsight. But what we have presented here is a popular architecture that’s typically used by many companies.
In the next chapter, we will look at event-driven analytics using Azure Event Hubs, Azure Stream Analytics, and Azure Machine Learning.
