In this video you will learn how to use Fivetran to ingest data from Salesforce into your Lakehouse. After the data has been ingested, you will then learn how you can transform your data using dbt. Then we will use Databricks SQL to query, visualize and govern your data. Lastly, we will show you how you can use AI functions in Databricks SQL to call language learning models.

Read more about Databricks SQL https://docs.databricks.com/en/sql/index.html#what-is-databricks-sql

In this session we’ll present Mosaic, a new Databricks Labs project with a geospatial flavour.

Mosaic provides users of Spark and Databricks with a unified framework for distributing geospatial analytics. Users can choose to employ existing Java-based tools such as JTS or Esri's Geometry API for Java and Mosaic will handle the task of parallelizing these tools' operations: e.g. efficiently reading and writing geospatial data and performing spatial functions on geometries. Mosaic helps users scale these operations by providing spatial indexing capabilities (using, for example, Uber's H3 library) and advanced techniques for optimising common point-in-polygon and polygon-polygon intersection operations.

The development of Mosaic builds upon techniques developed with Ordnance Survey (the central hub for geospatial data across UK Government) and described in this blog post: https://databricks.com/blog/2021/10/11/efficient-point-in-polygon-joins-via-pyspark-and-bng-geospatial-indexing.html

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Data breach is a concern for any data collection company including Northwestern mutual. Every measure is taken to avoid the identity theft and fraud for our customers; however they are still not sufficient if the security around it is not updated periodically. A multiple layer of encryption is the most common approach utilized to avoid breaches however unauthorized internal access to this sensitive data still poses a threat

This presentation will walk you following steps: - Design to build encryption at column level - How to protect PII data that is used as key for joins - Ability for authorized users to decrypt data at run time - Ability to rotate the encryption keys if needed

At Northwestern Mutual, a combination of Fernet, AES encryption libraries, user-defined functions (UDFs), and Databricks secrets, were utilized to develop a process to encrypt PII information. Access was only provided to those with a business need to decrypt it, this helps avoids the internal threat. This is also done without data duplication or metadata (view/tables) duplication. Our goal is to help you understand on how you can build a secure data lake for your organization which can eliminate threats of data breach internally and externally. Associated blog: https://databricks.com/blog/2020/11/20/enforcing-column-level-encryption-and-avoiding-data-duplication-with-pii.html

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