Udemy

Advanced Data Warehouse Performance Optimization

"Unlocking the Power of Databricks: Advanced Techniques for Data Warehouse Performance Enhancement and UDF-driven Data P
Free tutorial
Rating: 3.9 out of 5 (7 ratings)
967 students
57min of on-demand video
English
English [Auto]

Understanding the principles of data warehousing and its importance in modern data analytics.
Leveraging Databricks-specific tools and features for performance optimization.
Techniques for optimizing query performance, including query tuning and indexing strategies.
Identifying common performance bottlenecks in data warehouses.

Requirements

  • A solid grasp of SQL (Structured Query Language) is essential.
  • Understanding how data is transformed and loaded into a data warehouse is crucial. Knowledge of ETL processes and tools like Apache Spark can be helpful.

Description

Are you ready to take your data warehouse performance optimization and data processing skills to the next level? If so, our Intermediate-level course on Advanced Data Warehouse Performance Optimization and Data Processing with User-Defined Functions (UDFs) in Databricks is the perfect opportunity for you!

Course Overview:

In this Intermediate-level course, you will dive deep into the world of data warehousing and advanced data processing techniques using Databricks, a powerful cloud-based platform. Whether you are a data engineer, data scientist, or analyst, this course is designed to equip you with the knowledge and skills needed to excel in the field.

What You Will Learn:

  1. Advanced Data Warehouse Optimization: Explore advanced optimization techniques to enhance the performance of your data warehouse. Learn how to fine-tune queries, manage clusters effectively, and optimize data storage for lightning-fast query execution.

  2. User-Defined Functions (UDFs): Master the art of creating and using UDFs to perform custom data transformations. Discover how to harness the full potential of UDFs to meet your specific data processing requirements.

  3. Data Processing Pipelines: Build robust data processing pipelines using Databricks. Learn how to efficiently ingest, transform, and load data, ensuring data quality and consistency throughout the pipeline.

  4. Performance Tuning: Dive into the intricacies of performance tuning in Databricks. Explore techniques to identify and resolve bottlenecks, optimize Spark jobs, and scale your data processing tasks.

  5. Best Practices: Gain insights into industry best practices for data warehousing and data processing in Databricks. Learn from real-world examples and case studies.

  6. Hands-On Projects: Apply your knowledge through hands-on projects and exercises. Work on real data scenarios to reinforce your understanding of the concepts covered in the course.

Prerequisites:

This Intermediate-level course is designed for individuals who have a foundational understanding of data warehousing and data processing concepts. Familiarity with Databricks and SQL is recommended but not required.

By the end of this course, you will be well-equipped to optimize data warehouse performance, create powerful UDFs, and design efficient data processing pipelines using Databricks. You'll also receive a certificate of completion, showcasing your expertise in advanced data warehouse optimization and data processing.

Don't miss this opportunity to elevate your skills and career in the field of data engineering and data science. Enroll now and take your data processing capabilities to the next level with Advanced Data Warehouse Performance Optimization and Data Processing with UDFs - Databricks Intermediate!

Enroll today and unlock the potential of your data!

Who this course is for:

  • Data Engineers: Data engineers who have a foundational understanding of data warehousing and data processing using tools like Databricks and are looking to deepen their knowledge and skills.
  • Intermediate Data Analysts: Data analysts with intermediate-level experience who want to enhance their ability to optimize data warehouse performance and work with User-Defined Functions (UDFs) in Databricks.
  • Data Scientists: Data scientists who want to extend their skills to include advanced data processing and optimization techniques using Databricks.
  • Business Intelligence Professionals: BI professionals who work with large datasets and want to gain expertise in optimizing data processing workflows for better reporting and analysis.

Instructor

Educator || 1M+ Students Trained
Akhil Vydyula
  • 4.0 Instructor Rating
  • 221 Reviews
  • 16,124 Students
  • 31 Courses

Hello, I'm Akhil, an Associate Consultant at Atos India with a focus on the Advisory Consulting practice, specializing in Data and Analytics. My professional journey has led me through various facets of data analysis and modeling, particularly in the BFSI sector, where I've had the privilege of overseeing the full lifecycle of development and execution.


My skill set encompasses a wide range of data-related tasks, including data wrangling, feature engineering, algorithm development, model training, and implementation. I thrive on leveraging data mining techniques such as statistical analysis, hypothesis testing, regression analysis, as well as both unsupervised and supervised machine learning processes to extract meaningful insights and drive data-informed decisions. I'm particularly passionate about risk identification through decision models, and I've honed my expertise in Machine Learning Algorithms, Data/Text Mining techniques, and Data Visualization to effectively address these challenges.


Currently, I'm immersed in an exciting Amazon cloud project that involves end-to-end development of ETL processing. In this role, I craft ETL processing code using PySpark/Spark SQL to extract data from S3 buckets, perform necessary transformations, execute scripts using EMR services, and load consolidated data into Postgres SQL (RDS/Redshift) on a full, incremental, and live basis. To streamline this process, I've automated it by creating jobs in Step functions, which trigger EMR instances to run scripts in a specific order and send notifications upon execution status changes. The scheduling of these Step functions is achieved through event bridge rules.


Additionally, I've worked extensively with AWS Glue, using it to replicate source data from on-premises systems to raw-layer S3 buckets via AWS DMS services. One of my key strengths lies in my ability to understand the nuances of data and apply the right transformations to convert data from multiple tables into key-value pairs. Furthermore, I've optimized the performance of stored procedures in Postgres SQL to execute second-level transformations by efficiently joining multiple tables and loading the data into final tables.


I'm passionate about harnessing the power of data to drive actionable insights and improve business outcomes. If you share this passion or are interested in collaborating on data-driven projects, feel free to connect with me. Let's explore the endless possibilities that data analytics has to offer!

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