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Partner Technical Learning Paths & Certifications

Validate your data and AI skills in the Databricks Lakehouse Platform by getting Databricks certified. Whether you are new to business intelligence or looking to confirm your skills in data analysis, data engineering or machine learning, Databricks can help you achieve your goals.


Role Progression and Certifications

Databricks invests in its partners by providing free access to Databricks Academy courses and at-cost (50% discount) attempts at a certification exam.

Data Analyst

Data analysts transform data into insights by creating queries, data visualizations and dashboards using Databricks SQL and its capabilities

Data Analytics with Databricks

This course provides a comprehensive introduction to Databricks SQL. Learners will ingest data, write queries, produce visualizations and dashboards, and configure alerts.

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Data Engineer

Data engineers design, develop, test and maintain batch and streaming data pipelines using the Databricks Lakehouse Platform and its capabilities.

Data Engineering with Databricks

This course offers hands-on instruction in the Databricks Data Science & Engineering Workspace, Databricks SQL, Delta Live Tables, Databricks Repos, Databricks Task Orchestration, and the Unity Catalog.

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Advanced Data Engineering with Databricks

In this course, students will build upon their existing knowledge of Apache Spark, Structured Streaming, and Delta Lake to unlock the full potential of the data lakehouse by utilizing the suite of tools provided by Databricks.

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ML Data Scientist

Machine learning practitioners develop, deploy, test and maintain machine learning models and pipelines using Databricks Machine Learning and its capabilities.

Scalable Machine Learning with Apache Spark™

This course teaches you how to scale ML pipelines with Spark, including distributed training, hyperparameter tuning, and inference. You will build and tune ML models with SparkML while leveraging MLflow to track, version, and manage these models.

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Machine Learning in Production

In this course, you will learn the best practices for managing machine learning experiments and models with MLflow. There are two main components in this course: (i) using MLflow to track the machine learning lifecycle, package models for deployment, and manage model versions (ii) examining various production issues, different deployment paradigms, and post-production concerns.

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