Databricks

What is Databricks?

Databricks is a Data Intelligence Platform built on an open lakehouse architecture, offering a unified foundation for data and governance with AI models customized to an organization's specific needs. It enables anyone in an organization to harness automation and natural language for data exploration and utilization, while allowing technical teams to easily develop and deploy secure data and AI applications and products. Originating from academia and the open-source community, Databricks was established in 2013 by the original creators of the lakehouse architecture and key open-source projects such as Apache Spark™, Delta Lake, MLflow, and Unity Catalog.

Who created Databricks?

Databricks was created by the creators of Apache Spark at the University of California, Berkeley. The company was founded by Ali Ghodsi, Andy Konwinski, Ion Stoica, Matei Zaharia, Patrick Wendell, Reynold Xin, and Scott Shenker. Databricks is a big data analytics and artificial intelligence company that provides a unified data analytics platform. Ali Ghodsi, the CEO of Databricks, has a background in research and industry, contributing to the growth and success of the company. They offer a cloud-based solution for big data processing and machine learning.

Who is Databricks for?

• Data Scientist
• Data Engineer
• Business Analyst
• Machine learning engineer
• Data Analyst
• Software Engineer
• Devops engineer
• Research Scientist
• Product Manager
• Statistician

How to use Databricks?

To use Databricks effectively, follow these steps:

1. Create an Account: Visit the Databricks website and sign up for an account to access the platform.

2. Navigate the Interface: Familiarize yourself with the Databricks interface, which includes sections like "Training Overview," "Databricks Academy," and "Certification."

3. Training: Utilize the training resources available on Databricks to enhance your skills. Explore the curriculum tailored to your needs.

4. Databricks Academy: Access the Databricks Academy through the provided link to sign in and engage with the learning platform.

5. Certification: Consider pursuing certification on Databricks to gain recognition and differentiation in the field.

6. Advanced Learning: For more advanced learning, check out the University Alliance section to explore opportunities to teach Databricks.

By following these steps, you can harness the power of Databricks for your data analytics and machine learning needs.