Advanced DataBricks: Data Warehouse Performance Optimization
Mastering Databricks: Advanced Techniques for Data Warehouse Performance & Optimizing Data Warehouses s
Lectures -12
Duration -58 mins
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Course Description
Unlock the full potential of your data warehouse with our advanced course on DataBricks performance optimization. This course is designed for data professionals seeking to enhance their skills in data warehouse management and optimization techniques. You will learn to accelerate your data warehouse performance by understanding good practices in data partitioning, compression strategies, and user-defined functions (UDFs). Explore advanced techniques on scaling and resource management and gain practical insights to integrate UI with DataBricks. This course is prepared with extensive hands-on exercises and real-world examples to ensure you understand each concept in detail for practical applications.
Goals
- Overview of AI tools for developers and their impact on software development
- Setting up and configuration of GitHub Copilot using popular programming languages
- Show how best practices for collecting, analyzing, and managing lessons learned can work to your advantage
- Acknowledge how best practices and benchmarking can contribute to continuous improvement
Prerequisites
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.

Curriculum
Check out the detailed breakdown of what’s inside the course
Accelerating Data Warehouses: Mastering Performance Optimization
1 Lectures
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Accelerating Data Warehouses: Mastering Performance Optimization 03:46 03:46
Advanced Data Management: Data Partitioning and Compression Strategies
1 Lectures

Mastering User-Defined Functions (UDFs) for Data Warehousing
1 Lectures

Mastering Data Transformation with Advanced User-Defined Functions (UDFs)
1 Lectures

Advanced Techniques in Scaling and Resource Management for Data Warehousing
1 Lectures

UI and Databricks Integration: A Practical Guid
1 Lectures

Data Diving: A Beginner's Guide to Databricks
6 Lectures

Instructor Details

AKHIL VYDYULA
Data Scientist | Data & Analytics Specialist | EntrepreneurHello, 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!
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