Advanced Data Warehouse Performance Optimization
Requirements
- A solid understanding of SQL (Structured Query Language) is required.
- Familiarity with ETL (Extract, Transform, Load) processes and concepts is recommended.
- Basic experience with data engineering tools like Apache Spark or Databricks is helpful but not mandatory.
- A willingness to dive deep into performance optimization techniques!
Description
Are you ready to supercharge your data warehouse performance optimization and data processing capabilities? In this Intermediate-level course, you'll dive deep into advanced techniques using Databricks and User-Defined Functions (UDFs) to enhance data processing workflows and boost query performance.
Course Overview:
This course is designed to take you beyond the basics, giving you the tools to optimize data warehouse performance and build efficient, scalable data pipelines. By utilizing Databricks—a powerful cloud-based platform for big data and AI—you'll gain hands-on experience in data warehouse optimization, UDF creation, and performance tuning.
What You Will Learn:
Advanced Data Warehouse Optimization: Learn to fine-tune queries, manage clusters, and optimize data storage for faster query execution.
User-Defined Functions (UDFs): Master UDF creation to handle custom data transformations and enhance processing efficiency.
Data Processing Pipelines: Build robust pipelines with Databricks, optimizing data ingestion, transformation, and consistency across processes.
Performance Tuning: Dive into performance diagnostics, tackle bottlenecks, and scale your Spark jobs for large datasets.
Best Practices: Discover industry best practices for efficient data processing and optimization within Databricks, backed by real-world case studies.
Hands-On Projects:
Work through practical examples and real data scenarios to consolidate your learning and build a strong portfolio.
Prerequisites:
This course is ideal for individuals with a foundational understanding of data warehousing and SQL. Familiarity with Databricks is recommended but not mandatory.
By the end of the course, you'll be proficient in optimizing data warehouse performance, creating custom UDFs, and building efficient, high-performance data pipelines. A certificate of completion will be awarded to recognize your expertise in advanced data warehouse optimization.
Don’t miss the chance to unlock the full potential of your data! Enroll now and elevate your career in data engineering, data science, or business intelligence!
Who this course is for:
- Data Engineers: Professionals with foundational knowledge of data warehousing and Databricks who want to sharpen their performance optimization skills.
- Intermediate Data Analysts: Analysts looking to optimize query performance and master UDFs (User-Defined Functions) within Databricks environments.
- Data Scientists: Practitioners seeking to extend their data handling and optimization capabilities for faster model training and better insights.
- Business Intelligence (BI) Professionals: Those working with large datasets who want to streamline data workflows and enhance reporting efficiency.
Instructor
- 4.1 Instructor Rating
- 1,093 Reviews
- 66,541 Students
- 51 Courses
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!