The short answer: It’s night and day. The longer answer: Relational databases require deep-level integration across schemas and between datasets. This is difficult and time-consuming. Conversely, Einstein Analytics is a mobile tool that lets you explore information like you would with a search engine. This creates faster time to value, and more flexible data visualization and navigation.
- Relational databases are schema-based. Schemas are based on assumptions about questions that need to be asked ahead of time, and require pre-aggregation due to long query times. This forces rigid searches and questions, preventing users from exploring unique opportunities on the spot.
Instead, Wave Analytics uses search-inspired technology to mash up data across silos, to unify and collapse the systems integration lifecycle.
- Relational databases are brittle. Changes in source data — like updating your product list in your enterprise planning system — can break the whole schema, forcing IT to spend days or weeks fixing things.
Instead, Einstein Analytics is an organic, flexible system that’s 100% adaptable to change or varied data types.
- Relational databases can’t ingest unstructured or semistructured data. Legacy business intelligence and relational databases can only ingest specific types of data, and can only scale to structured datasets. They can’t ingest or combine semistructured data.
Instead, Einstein Analytics can easily handle semistructured data because it works like a search engine, not a spreadsheet. Data is constantly changing, and is shaped differently today than it was 10 years ago. Wave is designed to adapt to all of these changes, and more.
- Relational databases limit exploration; you can’t get to individual records. Databases use pre-aggregation and precomputation, which limits search flexibility. Legacy systems were optimized for performance and storage costs, making aggregation a necessary evil for queries.
Instead, Einstein Analytics uses a key-value store and inverted index that allows slicing and dicing down to individual records, while making it easy to explore hundreds of millions of rows.