Storage is undoubtedly the masterpiece of the data-driven architecture. It provides the essential, long-term retention of your data. It also provides the core functionality to search, analyze, and discover insights in your data. It is the heart of the process. The action will depend on the nature of the technology. Here are some aspects that the storage layer usually brings:

• Scalability is the main aspect, that is, the storage used for various volumes of data that could start from gigabytes to terabytes to petabytes of data. The scalability is horizontal, which means that, as demand and volume grow, you should be able to increase the capacity of the storage seamlessly by adding more machines.
• Most of the time, a non-relational and highly distributed data store, which allows fast data access and analysis at a high volume and on a variety of data types, is used, namely, a NoSQL data store. Data is partitioned and spread over a set of machines in order to balance the load while reading or writing data.
• For data visualization, it's essential that the storage exposes an API to make analysis on top of the data. Letting the visualization layer do the statistical analysis, such as grouping data over a given dimension (aggregation), wouldn't
  scale.
• The nature of the API can depend on the expectation of the visualization layer, but most of the time it's about aggregations. The visualization should only render the result of the heavy lifting done at the storage level.
• A data-driven architecture can serve data to a lot of different applications and users, and for different levels of SLAs. High availability becomes the norm in such architectures, and, like scalability, it should be part of the nature of the solution.