Advanced DataBricks for Data Engineering
What you'll learn
- Overview of AI tools for developers and their impact on software development
- Setup and configuration of GitHub Copilot with popular programming languages
- Demonstrate your understanding of best practices for collecting, analyzing, and managing lessons learned
- Recognize how best practices and benchmarking support continuous improvement
Requirements
- No special requirements or prerequisites are needed to take this course, but some extra reading about projects, project management, project life cycle, organizational project management, project scope, project schedule, project costs, project quality, project human resources and project communications will help.
Description
Welcome to the "Advanced Data Warehouse Performance Optimization and Data Processing with UDFs - Databricks Intermediate" course, where you'll take your skills in data warehousing and analytics to the next level using the powerful Databricks platform. In this intermediate-level course, we'll dive deep into the art and science of optimizing data warehouse performance and harnessing the capabilities of User-Defined Functions (UDFs) for advanced data processing.
Course Highlights:
1. Advanced Databricks Setup: Begin by setting up an advanced Databricks environment, including cluster configuration and integration with data sources, to prepare for performance optimization and UDF development.
2. Data Warehouse Optimization: Explore advanced techniques for optimizing data warehousing workloads. Learn how to fine-tune performance by optimizing data storage, partitioning strategies, and query optimization.
3. Profiling and Diagnostics: Master the art of profiling and diagnosing performance bottlenecks in your data warehouse workloads. Identify and address performance issues to ensure smooth data processing.
4. Leveraging User-Defined Functions (UDFs): Understand the power of User-Defined Functions (UDFs) in Databricks. Create and utilize UDFs to perform custom data transformations and calculations, expanding the capabilities of your data processing pipelines.
5. Data Lake Integration: Learn how to seamlessly integrate Databricks with data lakes, enabling efficient data extraction, transformation, and loading (ETL) processes. Explore best practices for managing data lakes.
6. Real-time Data Processing: Explore real-time data processing scenarios using Databricks Streaming. Discover how to ingest, process, and analyze streaming data for timely insights.
7. Advanced Data Analytics: Go beyond basic analytics. Explore advanced analytics techniques, including machine learning and predictive analytics, using Databricks libraries and tools.
8. Scalable Data Processing: Understand how to scale your data processing workloads to handle large datasets and complex computations effectively. Utilize Databricks clusters for parallel processing.
9. Monitoring and Performance Tuning: Gain proficiency in monitoring data warehouse performance and fine-tuning your Databricks workloads for optimal efficiency and resource utilization.
10. Best Practices and Case Studies: Learn from real-world case studies and industry best practices. Explore how organizations have achieved significant performance improvements and advanced data processing capabilities using Databricks.
This course is designed for intermediate learners who already have a foundational understanding of Databricks and data warehousing concepts. By the end of this course, you'll have the skills and knowledge to optimize data warehouse performance, develop and deploy UDFs for advanced data processing, and handle complex data analytics scenarios with confidence.
Who this course is for:
- System Administrators
Instructor
- 4.0 Instructor Rating
- 1,144 Reviews
- 72,195 Students
- 46 Courses
Hello, I'm Akhil Vydyula — Lead Data Engineer at Publicis Sapient, and Former Senior Data Scientist at PwC.
With over 5 years of rich industry experience and a strong focus on the BFSI sector, I’ve led and delivered end-to-end data and analytics solutions that power strategic decisions and transform business outcomes.
At Publicis Sapient, I currently lead complex data engineering initiatives, leveraging my deep expertise in cloud-native platforms like AWS to architect robust, scalable data pipelines. My work spans across developing and optimizing ETL workflows using PySpark and Spark SQL, orchestrating data flows via EMR, Step Functions, and EventBridge, and driving real-time and batch data processing into PostgreSQL (RDS/Redshift) environments. I've also implemented AWS Glue and DMS to seamlessly replicate and transform large-scale on-premise data into cloud-native formats.
Previously, at PwC, I specialized in advanced analytics and machine learning within the Advisory Consulting practice. I’ve built and deployed predictive models using statistical analysis, regression, classification, clustering, and text mining—particularly for risk identification and decision modeling. My passion lies in transforming raw data into actionable insights through effective data storytelling and visualization.
In parallel to my corporate career, I bring over 5 years of teaching experience, mentoring hundreds of aspiring data professionals. I’m deeply committed to helping students break into the data industry by translating real-world challenges into practical learning experiences.
Whether it's building data pipelines, uncovering business insights, or shaping the next generation of data talent, I thrive at the intersection of technology, strategy, and impact.
Let’s connect if you're passionate about data, eager to learn, or looking to collaborate on meaningful, data-driven initiatives.