![]() In this blog, the focus is on Databricks Datalakehouse Platform. It also supports popular compliances such as SOC 2 Type II, HIPAA, ISO 27001, GDPR, and more. The platform allows all modern data & analytics concepts of data engineering, machine learning, and SQL analytics while supporting multiple languages (Python, Scala, R, Java, and SQL) for easy, seamless, and improved collaborations.ĭatabricks environment is separated into three main workspaces as below based on different types of workloads used in organizations:Īll the workspaces provide a unified environment with enterprise capabilities such as provide role-based access control (RBAC), encryption, networking, automatic scaling, versioning, and more. The Databricks Lakehouse Platform allows organizations to facilitate data engineering, analytics, BI, data science, and machine learning by providing the governance and performance capabilities of a data warehouse combined with the flexibility and machine learning support of data lakes. Many customers newer to the data science space don’t understand the depth of capabilities aggregated in the Databricks Lakehouse Platform and its importance as a technology. The purpose of this blog post is to elaborate on this, explain why so many firms are implementing Databricks Lakehouse Platforms within their data estate, and outline several of the ML, AI, and data science, streaming, and analytics areas where Databricks can add value, as per the list of scenarios and associated value below: Why Databricks Lakehouse Platform? ![]() Why is this unicorn so valuable? What can their technology do for you? Increasingly, many customers and companies are interested in Databricks. ![]()
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