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Fraxses is a metadata-driven platform that connects disparate data sources so that they function as a single virtual environment. Its distributed architecture provides a flexible framework on which organisations can build a data fabric or mesh. 

Fraxses ships with over 230 connectors to ensure that it can communicate with virtually any type of data source. The platform’s interoperability with other systems is one of the key reasons that Fraxses is so effective in providing fast  and easy access to data. 

Fraxses offers the versatility to push the envelope, and new ways to leverage its capabilities are frequently conceived in response to specific problems. A recent example of this, which demonstrates the platform’s extensive interoperability capacities, was when Fraxses was used to meet the needs of a banking client. The bank has an automated production environment in which no human intervention is permitted. In order to extract data from the environment, it was necessary for development and testing teams to code SQL statements in either Java, Scala or Python, and run these in Databricks. 

The bank required a solution to replace this time-consuming process. As per the bank’s architecture requirements, Fraxses was not deployed in the production environment itself, as would be the case with a standard implementation. 

Instead, the platform’s power was harnessed in a different way: by extracting Fraxses Data Objects as SQL statements and running those against Databricks. This was achieved by leveraging Fraxses’ API capabilities of pedigree and Spark-compliant SQL Query extraction, and creating a custom microservice using the Fraxses Smartwrapper. This solution eradicated the need for coding, presenting the bank with a faster and easier way to access data from its production environment.

Leveraging Fraxses as a no-code front end for business users to generate their own SQL statements opens up many possibilities for organisations. Creating the Fraxses Data Objects the rocess begins with and extracting these from the platform requires only limited technical skills. Empowering more users to engage with data in this way gives businesses the opportunity to establish and drive a data culture in their organisation. 

This example uses Databricks because it is the external engine the client in question has in place; however the same method can be applied to generate SQL statements for other platforms that utilise Spark. And while the SQL statements in this example are Spark-specific, Fraxses’ capability to generate statements in the relevant SQL syntax can easily be extended for engines such as Oracle, SQL Server, PostgreSQL, MySQL, MariaDB and Snowflake, should a client require this. 

The process for extracting Fraxses Data Objects as SQL statements is explained in this video: 

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