Databricks Training in Tashkent

Databricks serves as a comprehensive Lakehouse platform that integrates Spark, Delta Lake, and governance capabilities (Unity Catalog) to facilitate scalable data engineering and analytics operations.

This instructor-led, live training (available online or onsite) is designed for intermediate-level technology managers with a background in data engineering who aim to migrate complex procedural OLAP logic to a Lakehouse architecture. The curriculum utilizes Databricks, Spark, Delta Lake, Unity Catalog, and native workflows to achieve this goal.

Upon completing this training, participants will be equipped to:

• Articulate Lakehouse architecture principles and the Bronze→Silver→Gold (Medallion) pattern.
• Convert stored-procedure logic into Spark DataFrame implementations and notebooks.
• Architect and deploy incremental ingestion, merge, and optimization processes leveraging Delta Lake.
• Construct end-to-end orchestrated pipelines incorporating Databricks Workflows, version control, testing protocols, and governance.

Course Format

• Intensive, instructor-led sessions featuring focused demonstrations and detailed explanations.
• Daily hands-on laboratories utilizing representative datasets and migration exercises.
• Guided code reviews, performance tuning clinics, and practical workflow orchestration experience.

Customization Options

• This course can be tailored to align with your specific environment, datasets, and governance requirements. Please contact us to arrange for customization.

Azure Databricks is a cloud-based collaborative platform that supports big data analytics and machine learning using Apache Spark and the Microsoft Azure ecosystem.

This instructor-led, live training (online or onsite) is aimed at intermediate-level data professionals who wish to design, build, and deploy machine learning models using Azure Databricks for financial use cases.

By the end of this training, participants will be able to:

• Build and train machine learning models using financial data in Azure Databricks.
• Use MLflow to track experiments, manage models, and deploy solutions.
• Apply models to detect anomalies, assess risks, or forecast trends in finance.
• Integrate ML models into scalable pipelines for continuous evaluation.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Databricks is a unified analytics platform designed for big data and machine learning in collaborative environments.

This instructor-led, live training (online or on-site) is aimed at beginner-level to intermediate-level data analysts and financial professionals who wish to perform large-scale data analysis using Databricks in finance-related use cases.

By the end of this training, participants will be able to:

• Use Databricks notebooks to query and analyse financial datasets.
• Clean and transform raw financial data for reporting and insights.
• Create dashboards and visualisations for performance tracking.
• Collaborate with team members in real time on shared notebooks.

Format of the Course

• Interactive lecture and discussion.
• Extensive exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customisation Options

• To request customised training for this course, please contact us to arrange.

This instructor-led, live training in Tashkent (delivered online or on-site) is designed for data scientists and developers who wish to set up, deploy, and manage data analytics solutions using Databricks.

By the end of this training, participants will be able to:

• Set up and configure Databricks.
• Understand how Databricks and Apache Spark work together.
• Learn how to load and transform data in Databricks.