Implement Data Analytics Solution with Databricks

Implement Data Analytics Solution with Databricks

Transform Your Data Workflow with Real-Time Processing and Automation in Databricks.

What does the Course Offer

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Objectives

Practice Tests

Training Hours

Exercises

Knowledge Checks

This course teaches you how to implement scalable data analytics solutions using Azure Databricks. You’ll learn to process, analyze, and visualize large datasets with Apache Spark and Delta Lake. The course covers key topics like data ingestion, transformation, real-time streaming, and building data pipelines with Delta Live Tables. You will also explore performance optimization, CI/CD workflows, and data governance to automate tasks and ensure data privacy. By the end, you’ll be equipped to create end-to-end data solutions for efficient, real-time analytics.

Course Overview

This course provides hands-on experience in building powerful data analytics solutions using Azure Databricks, a unified analytics platform powered by Apache Spark. You will learn to efficiently process and analyze large datasets, manage data with Delta Lake, and build real-time data pipelines using Delta Live Tables.

The course covers essential skills such as data transformation, performance optimization, incremental data processing, and automating workflows with CI/CD.

Additionally, you will learn how to integrate data governance practices and privacy management. By the end of this course, you'll have the knowledge to create end-to-end data pipelines for scalable, high-performance analytics in Azure Databricks.

Learning Outcomes

• Gain proficiency in using Databricks for data analytics and machine learning tasks.

• Master data ingestion, transformation, and cleaning using Apache Spark.

• Build and evaluate machine learning models in a distributed environment.

• Create interactive visualizations and collaborate effectively using Databricks notebooks.

• Implement scalable data analytics solutions for real-world use cases.

Audience Profile

• Data Engineers

• DBA

• ETL Developers

• Data Analysts

• Data Scientists

Prerequisites

• Familiarity with Azure and the Azure portal.

• Experience programming with C# or Python.

• Data Engineering Fundamentals

Course Objectives

• Explore Databricks

• Perform data analysis with Azure Databricks

• Use Apache Spark in Azure Databricks

• Manage data with Delta Lake

• Build data pipelines with Delta Live Tables

• Deploy workloads with Azure Databricks Workflows

• Perform incremental processing with spark structured streaming

• Implement streaming architecture patterns with Delta Live Tables

• Optimize performance with Spark and Delta Live Tables

• Implement CI/CD workflows in Azure Databricks

• Automate workloads with Azure Databricks Jobs

• Manage data privacy and governance with Azure Databricks

• Use SQL Warehouses in Azure Databricks

• Run Azure Databricks Notebooks with Azure Data Factory

Format

Blended (Online Training + Discussions)

Streaming Platform

Microsoft Teams Online

Course Schedule

On Demand

Trainer

Kappagantula Srikanth

Duration

16 Hours

Course Fee

$1500

Course Outline

Prerequisites (1 Hour)

• Install Visual Studio

• Install Azure PowerShell & CLI

• Create/Access Microsoft Learn Account

• Create/Access GitHub Account

• Create/Access Microsoft Azure Account

Explore Databricks

• Get started with Azure Databricks

• Identify Azure Databricks workloads

• Understand key concepts

• Data governance using Unity Catalog and Microsoft Purview

• Exercise - Explore Azure Databricks

Perform data analysis with Azure Databricks

• Ingest data with Azure Databricks

• Data exploration tools in Azure Databricks

• Data analysis using DataFrame APIs

• Exercise - Explore data with Azure Databricks

Use Apache Spark in Azure Databricks

• Get to know Spark

• Create a Spark cluster

• Use Spark in notebooks

• Use Spark to work with data files

• Visualize data

• Exercise - Use Spark in Azure Databricks

Manage data with Delta Lake

• Get started with Delta Lake

• Manage ACID transactions

• Implement schema enforcement

• Data versioning and time travel in Delta Lake

• Data integrity with Delta Lake

• Exercise - Use Delta Lake in Azure Databricks

Build data pipelines with Delta Live Tables

• Explore Delta Live Tables

• Data ingestion and integration

• Real-time processing

• Exercise - Create a data pipeline with Delta Live Tables

Deploy workloads with Azure Databricks Workflows

• What are Azure Databricks Workflows?

• Understand key components of Azure Databricks Workflows

• Explore the benefits of Azure Databricks Workflows

• Deploy workloads using Azure Databricks Workflows

• Exercise - Create an Azure Databricks Workflow

Perform incremental processing with spark structured streaming

• Set up real-time data sources for incremental processing

• Optimize Delta Lake for incremental processing in Azure Databricks

• Handle late data and out-of-order events in incremental processing

• Monitoring and performance tuning strategies for incremental processing in Azure Databricks

• Exercise - Real-time ingestion and processing with Delta Live Tables with Azure Databricks

Implement streaming architecture patterns with Delta Live Tables

• Event driven architectures with Delta Live tables

• Ingest data with structured streaming

• Maintain data consistency and reliability with structured streaming

• Scale streaming workloads with Delta Live tables

• Exercise - end-to-end streaming pipeline with Delta Live tables

Optimize performance with Spark and Delta Live Tables

• Optimize performance with Spark and Delta Live Tables

• Perform cost-based optimization and query tuning

• Use change data capture (CDC)

• Use enhanced autoscaling

• Implement observability and data quality metrics

• Exercise - optimize data pipelines for better performance in Azure Databricks

Implement CI/CD workflows in Azure Databricks

• Implement version control and Git integration

• Perform unit testing and integration testing

• Manage and configure your environment

• Implement rollback and roll-forward strategies

• Exercise - Implement CI/CD workflows

Automate workloads with Azure Databricks Jobs

• Implement job scheduling and automation

• Optimize workflows with parameters

• Handle dependency management

• Implement error handling and retry mechanisms

• Explore best practices and guidelines

• Exercise - Automate data ingestion and processing

Manage data privacy and governance with Azure Databricks

• Implement data encryption techniques in Azure Databricks

• Manage access controls in Azure Databricks

• Implement data masking and anonymization in Azure Databricks

• Use compliance frameworks and secure data sharing in Azure Databricks

• Use data lineage and metadata management

• Implement governance automation in Azure Databricks

• Exercise - Practice the implementation of Unity Catalog

Use SQL Warehouses in Azure Databricks

• Get started with SQL Warehouses

• Create databases and tables

• Create queries and dashboards

• Exercise - Use a SQL Warehouse in Azure Databricks

Run Azure Databricks Notebooks with Azure Data Factory

• Understand Azure Databricks notebooks and pipelines

• Create a linked service for Azure Databricks

• Use a Notebook activity in a pipeline

• Use parameters in a notebook

• Exercise - Run an Azure Databricks Notebook with Azure Data Factory

Enquire about the Course

You can also reach out to us through the following options

Phone

+65-91709407

Email

info@empowerone.cloud

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