Data Warehousing: What is the Difference Between SQL and NoSQL?

When businesses evaluate database structures and analytics processes, there are two primary types of databases to choose from: SQL and NoSQL. Given that SQL databases work with highly structured data, the problem for many enterprises is how to accommodate the growing volume of unstructured and particularly semi-structured data that is collected from both inside and outside the business. The exponential growth in semi-structured data arising from IoT, Web and mobile devices is pushing the need for data formats
that can support flexible database schemas (JSON, Parquet, XML, Avro, and ORC) to move and store that data.

The SQL or NoSQL Debate and the SaaS Data Warehouse

So, when it comes to making database and data analysis decisions, what is the difference between SQL and NoSQL?  Too often this debate has focused on choosing one option over the other and transforming all corporate data to match one set of database schemas and specifications. In reality, businesses can save the time, resources, and cost needed to  harmonize structured and semi-structured data streams by taking a different path: the cloud-based data warehouse.

Unlike most databases and data stores, Snowflake Cloud  Data Warehouse features native support for both semi-structured data formats such as JSON and XML and relational data. With Snowflake, users can:

• Load semi-structured data without the need for transformation
• Decide whether to flatten semi-structured, nested data formats into SQL relational tables or store in native format
• Deploy fast and efficient SQL-based querying across all data types, which are automatically converted to the optimized internal storage format

With the SaaS-delivered Snowflake Data Warehouse, companies can spend time analyzing business-critical data and sharing it across the wider organizational ecosystem instead of managing cumbersome data transformation tasks that can throttle the process of getting real-time business insight.