Precog shows us that in the adverse events dataset, drug characterizations are denoted as numbers. These numbers represent whether or not the drug was suspected as causing the adverse reaction or interacting with a suspected drug.
Using Precog we can push the tabulated data into SQL databases and warehouses such as Snowflake and Postgres.
Tables in Precog are streaming and virtualised. We can think of this as though the Precog table were a grocery list rather than the groceries themselves. We can use the same list in different stores the same way we can use the same Precog table with different datasets. We can also export tables and import them into different copies of Precog.
Engineered to transform variable, complex JSON data, Precog draws source data from file output, API URLs, and data repositories such as AWS S3 and Azure Blob Storage.
The Precog solution enables data analysts and engineers to access complex JSON data as tables. In many cases we want those tables to be stored in Microsoft SQL Server or some other SQL database engine. Such requirement can be implemented easily using Precog and Azure Data Factory.
Whether you’re a professional data scientist or studying to become a data scientist, you’ll likely need to work with a JSON dataset. JSON isn’t easy to work with. It’s not tabular, and you can’t just push it to a SQL database — at least not without a “bit” of work.
