Before the explosion of data and the rise of cloud services, the Internet of Things (IoT), and numerous other new sources of data, data scientists could build their machine learning models by querying data from a single, centralized repository, such as a data lake or data warehouse. But today’s modern computing world is undergoing a massive digital transformation; the volume of data is increasing exponentially, resulting in data distributed across various sources that makes it virtually impossible for data to be brought into a single repository to be analyzed by data scientists and analysts.

Due to this inherently distributed nature of modern data, data scientists and analysts find themselves spending 50% of their time wrangling, loading, and cleansing data – which can stifle data science initiatives, frustrate data teams, and negatively impact business operations.

With technology like Starburst Enteprise and its rapid federated query engine, data scientists and analysts can query data in distributed datasets without moving all of it to a central location–enabling minimal data movement, fast search results, better quality of data, and less manual burdens for data teams.

However, with the benefits of rapid federated analytics comes the additional complexity of ensuring that users’ access to data fully respects the privacy, governance, risk and compliance constraints required by industry regulations. To truly empower data teams and securely democratize data, while still remaining compliant, enterprises need a solution for consistent access controls and governance policies across cloud data services from a single user interface.

To read full download the whitepaper:

How To Securely Accelerate Data
Science Initiatives, While Balancing Governance And Compliance

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