To put these concepts into context, let’s look at a real-world application that involves time series data. On the left side of this diagram, you’ll see large Volumes of high-velocity data that is Variable in nature. In this particular example, we have financial data that has information packed into variable-size arrays to improve the speed and performance of writing data to disk as rapidly as possible. Rather than writing a single small record for each piece of financial data, the data is packed into large blocks, without a strict data schema, and pushed to disk as these blocks. Millions of data points per second are logged to disk with this technique. This is a good example of dynamic 3Vs data.
Now, what happens when you want to perform data analysis over this dynamic data? Often the data will go through some form of ETL process on its way to a data warehouse or a relational database. But, if you leave the data in place and perform data analysis over the live data for reporting and web/enterprise/mobile device integration, you save a lot of time and you produce a wealth of opportunities for your business. You’ve also greatly lowered the strain on IT resources. You no longer need to move the data; you reduce system complexity by having a single source of data.
Only a true multimodel database supporting multiple data access techniques can address this very real modern example. This is a classic NoSQL case of schema-less data as it combines a variety of record types in a single table. This mix of formats is at odds with the relational model, which requires all records in a table to conform to a single schema (think of the third V, Variety). FairCom engineers overcame this problem with our unique Multi-Schema Data solution, which provides robust SQL access to non-relational data. When multi-schema data is accessed via SQL, we make it appear as multiple “virtual” tables, one for each schema. The SQL application is unaware of the non-relational structure of the underlying data, as it simply sees multiple “virtual” tables. Since the NoSQL portion of the application isn’t forced to impose a fixed schema over each piece of data being written to the database, the Volume and Velocity of data flowing through the application isn’t impacted. In this example, we show the benefits of c-treeACE No+SQL for data flexibility, reduced code overhead, stellar data throughput, and ease of data integration and reporting through SQL.