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What you'll learn
Develop deep expertise in Databricks and design scalable data-driven solutions using Apache Spark.
- Gain a comprehensive understanding of Databricks architecture and core platform capabilities
- Perform distributed data processing at scale using Apache Spark
- Build and deploy machine learning models seamlessly within the Databricks environment
- Design and optimize robust data pipelines for ETL and real-time processing
- Apply best practices for building responsive analytics systems with enterprise-grade reliability
This course is meticulously crafted for data engineers, analysts, and AI professionals aiming to master unified analytics on Databricks.
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Course Content
- Deep dive into Databricks’ architecture and unified analytics platform
- Fundamental principles of Apache Spark and its distributed computing ecosystem
- Configuring and deploying your first Databricks workspace in a cloud environment
- Ingesting and transforming diverse datasets (structured and unstructured)
- Designing high-throughput ETL pipelines with Spark
- Performance tuning and optimization techniques for Spark-based data workflows
- Building and managing ML models using integrated MLflow tools
- Operationalizing AI through deployment on the Databricks platform
- Leveraging real-world use cases to implement AI across business verticals
Requirements
- Foundational understanding of cloud platforms and data analytics principles
- Prior experience with Python or SQL is advantageous
- Strong interest in scalable data processing and applied artificial intelligence
- No previous experience with Databricks is necessary
Description
- Acquire industry-relevant skills in processing large-scale datasets with Databricks
- Engineer scalable ML pipelines and AI models using Apache Spark
- Explore high-impact applications of AI and optimize performance in unified analytics environments