Overview
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
System Administrators
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.
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.
Akhil Vydyula
Hello, I'm Akhil, a Senior Data Scientist at PwC specializing in the Advisory Consulting practice with a focus on Data and Analytics.
My career journey has provided me with the opportunity to delve into various aspects of data analysis and modelling, particularly within the BFSI sector, where I've managed the full lifecycle of development and execution.
I possess a diverse skill set that includes data wrangling, feature engineering, algorithm development, and model implementation. My expertise lies in leveraging advanced data mining techniques, such as statistical analysis, hypothesis testing, regression analysis, and both unsupervised and supervised machine learning, to uncover valuable insights and drive data-informed decisions. I'm especially passionate about risk identification through decision models, and I've honed my skills in machine learning algorithms, data/text mining, and data visualization to tackle these challenges effectively.
Currently, I am deeply involved in an exciting Amazon cloud project, focusing on the end-to-end development of ETL processes. I write ETL code using PySpark/Spark SQL to extract data from S3 buckets, perform necessary transformations, and execute scripts via EMR services. The processed data is then loaded into Postgres SQL (RDS/Redshift) in full, incremental, and live modes. To streamline operations, I’ve automated this process by setting up jobs in Step Functions, which trigger EMR instances in a specified sequence and provide execution status notifications. These Step Functions are scheduled through EventBridge rules.
Moreover, I've extensively utilized AWS Glue to replicate source data from on-premises systems to raw-layer S3 buckets using AWS DMS services. One of my key strengths is understanding the intricacies of data and applying precise transformations to convert data from multiple tables into key-value pairs. I’ve also optimized stored procedures in Postgres SQL to efficiently perform second-level transformations, joining multiple tables and loading the data into final tables.
I am passionate about harnessing the power of data to generate actionable insights and improve business outcomes. If you share this passion or are interested in collaborating on data-driven projects, I would love to connect. Let’s explore the endless possibilities that data analytics can offer!