Databricks: Master Data Engineering, Big Data, Analytics, Ai

Master Databricks for data engineering, analytics, machine learning, and cloud integration with real-world applications.

What you'll learn

Understand Databricks Architecture – Learn the key components, workspace features, and advantages of Databricks over traditional data platforms.

Set Up and Configure Databricks – Create a Databricks workspace, manage clusters, and navigate notebooks for data processing.

Perform ETL Operations – Use Apache Spark in Databricks for extracting, transforming, and loading (ETL) large datasets efficiently.

Work with Delta Lake – Implement incremental data loading, schema evolution, and time travel features using Delta Lake.

Run SQL Queries in Databricks – Utilize Databricks SQL for querying and analyzing structured data, optimizing performance, and creating dashboards.

Build and Deploy Machine Learning Models – Use MLflow for model tracking, hyperparameter tuning, and deploying ML models within Databricks.

Integrate Databricks with Cloud Services – Connect Databricks with AWS S3, Azure Data Factory, Snowflake, and BI tools like Power BI.

Optimize Cluster Performance – Learn auto-scaling, partitioning, bucketing, and performance tuning techniques for handling big data workloads.

Implement Real-Time Data Processing – Develop streaming analytics pipelines for IoT and real-time event processing in Databricks.

Secure Data in Databricks – Apply role-based access control (RBAC), encryption, and auditing to protect sensitive data.

Develop CI/CD Pipelines for Databricks – Automate deployment and testing using GitHub, Azure DevOps, and Databricks REST API.

Manage Data Warehousing in Databricks – Design scalable data lakes, data marts, and warehouse architectures for enterprise solutions.

Perform Graph and Time Series Analysis – Use GraphFrames for graph processing and time-series forecasting in Databricks.

Monitor and Audit Databricks Workloads – Track resource utilization, job performance, and cost optimization strategies for efficient cloud usage.

Apply Databricks to Real-World Use Cases – Work on projects like customer segmentation, predictive maintenance, and fraud detection using Databricks.

Requirements

Enthusiasm and determination to make your mark on the world!

Description

A warm welcome to the Databricks: Master Data Engineering, Big Data, Analytics, AI course by Uplatz.Databricks is a cloud-based data engineering, analytics, and machine learning platform built on Apache Spark. It provides an integrated environment for processing big data, performing analytics, and deploying machine learning models. Databricks simplifies data engineering and collaboration by offering a unified workspace where data engineers, data scientists, and analysts can work together efficiently. It is available on Microsoft Azure, Amazon Web Services, and Google Cloud, making it a versatile choice for enterprises working with large datasets.Databricks is widely used in industries such as finance, healthcare, retail, and technology for handling large-scale data workloads efficiently. It provides a powerful and scalable solution for organizations looking to leverage big data for analytics, machine learning, and business intelligence.How Databricks WorksDatabricks operates as a fully managed, cloud-based platform that automates and optimizes big data processing. The workflow typically involves:Creating a workspace where users manage notebooks, clusters, and data assets.Configuring clusters using Apache Spark for scalable and distributed computing.Importing and processing data from multiple sources, including data lakes, relational databases, and cloud storage.Running analytics and SQL queries using Databricks SQL for high-performance querying and data visualization.Building and deploying machine learning models using MLflow for tracking experiments, hyperparameter tuning, and deployment.Optimizing performance through auto-scaling, caching, and parallel processing to handle large-scale data workloads efficiently.Integrating with cloud services and APIs such as Azure Data Factory, AWS S3, Power BI, Snowflake, and REST APIs for seamless workflows.Core Features of DatabricksUnified data analytics platform combining data engineering, analytics, and machine learning in a single environment.Optimized runtime for Apache Spark, improving performance for big data workloads.Delta Lake for improved data reliability, versioning, and schema evolution in data lakes.Databricks SQL for running high-performance SQL queries and building interactive dashboards.MLflow for streamlined machine learning development, including model tracking, experimentation, and deployment.Auto-scaling clusters that dynamically allocate resources based on workload requirements.Real-time streaming analytics for processing event-driven data from IoT devices, logs, and real-time applications.Advanced security features, including role-based access control, encryption, and audit logging for compliance.Multi-cloud support with deployment options across AWS, Azure, and Google Cloud.Seamless integration with third-party analytics and business intelligence tools like Power BI, Tableau, and Snowflake.Benefits of Using DatabricksAccelerates data processing by optimizing Spark-based computations for better efficiency.Simplifies data engineering by automating ETL processes, reducing manual intervention.Enhances collaboration by allowing engineers, analysts, and data scientists to work in a shared, cloud-based workspace.Supports AI and machine learning with an integrated framework for training and deploying models at scale.Reduces cloud computing costs through auto-scaling and optimized resource allocation.Ensures data reliability with Delta Lake, enabling ACID transactions and schema enforcement in large datasets.Provides real-time analytics capabilities for fraud detection, IoT applications, and event-driven processing.Offers flexibility with multi-cloud deployment, making it easier to integrate with existing enterprise infrastructure.Meets enterprise security and compliance standards, ensuring data protection and regulatory adherence.Improves business intelligence with Databricks SQL, enabling organizations to gain deeper insights and make data-driven decisions.Databricks - Course Curriculum1. Introduction to DatabricksIntroduction to DatabricksWhat is Databricks? Platform OverviewKey Features of Databricks WorkspaceDatabricks Architecture and ComponentsDatabricks vs Traditional Data Platforms2. Getting Started with DatabricksSetting Up a Databricks WorkspaceDatabricks Notebook BasicsImporting and Organizing Datasets in DatabricksExploring Databricks ClustersDatabricks Community Edition: Features and Limitations3. Data Engineering in DatabricksIntroduction to ETL in DatabricksUsing Apache Spark with DatabricksWorking with Delta Lake in DatabricksIncremental Data Loading Using Delta LakeData Schema Evolution in Databricks4. Data Analysis with DatabricksRunning SQL Queries in DatabricksCreating and Visualizing DashboardsOptimizing Queries in Databricks SQLWorking with Databricks Connect for BI ToolsUsing the Databricks SQL REST API5. Machine Learning & Data ScienceIntroduction to Machine Learning with DatabricksFeature Engineering in DatabricksBuilding ML Models with Databricks MLFlowHyperparameter Tuning in DatabricksDeploying ML Models with Databricks6. Integration and APIsIntegrating Databricks with Azure Data FactoryConnecting Databricks with AWS S3 BucketsDatabricks REST API BasicsConnecting Power BI with DatabricksIntegrating Snowflake with Databricks7. Performance OptimizationUnderstanding Databricks Auto-ScalingCluster Performance Optimization TechniquesPartitioning and Bucketing in DatabricksManaging Metadata with Hive Tables in DatabricksCost Optimization in Databricks8. Security and ComplianceSecuring Data in Databricks Using Role-Based Access Control (RBAC)Setting Up Secure Connections in DatabricksManaging Encryption in DatabricksAuditing and Monitoring in Databricks9. Real-World ApplicationsReal-Time Streaming Analytics with DatabricksData Warehousing Use Cases in DatabricksBuilding Customer Segmentation Models with DatabricksPredictive Maintenance Using DatabricksIoT Data Analysis in Databricks10. Advanced Topics in DatabricksUsing GraphFrames for Graph Processing in DatabricksTime Series Analysis with DatabricksData Lineage Tracking in DatabricksBuilding Custom Libraries for DatabricksCI/CD Pipelines for Databricks Projects11. Closing & Best PracticesBest Practices for Managing Databricks Projects

Overview

Section 1: Introduction to Databricks

Lecture 1 Introduction to Databricks

Section 2: Databricks Platform Overview

Lecture 2 Databricks Platform Overview

Section 3: Key Features of Databricks Workspace

Lecture 3 Key Features of Databricks Workspace

Section 4: Databricks Architecture and Components

Lecture 4 Databricks Architecture and Components

Section 5: Databricks vs. Traditional Data Platforms

Lecture 5 Databricks vs. Traditional Data Platforms

Section 6: Setting up a Databricks Workspace

Lecture 6 Setting up a Databricks Workspace

Section 7: Databricks Notebook Basics

Lecture 7 Databricks Notebook Basics

Section 8: Importing and Organizing Datasets in Databricks

Lecture 8 Importing and Organizing Datasets in Databricks

Section 9: Exploring Databricks Clusters

Lecture 9 Exploring Databricks Clusters

Section 10: Databricks Community Edition: Features and Limitations

Lecture 10 Databricks Community Edition: Features and Limitations

Section 11: Introduction to ETL in Databricks

Lecture 11 Introduction to ETL in Databricks

Section 12: Using Apache Spark with Databricks

Lecture 12 Using Apache Spark with Databricks

Section 13: Working with Delta Lake in Databricks

Lecture 13 Working with Delta Lake in Databricks

Section 14: Incremental Data Loading using Delta Lake

Lecture 14 Incremental Data Loading using Delta Lake

Section 15: Data Schema Evolution in Databricks

Lecture 15 Data Schema Evolution in Databricks

Section 16: Running SQL Queries in Databricks

Lecture 16 Running SQL Queries in Databricks

Section 17: Creating and Visualizing Dashboards

Lecture 17 Creating and Visualizing Dashboards

Section 18: Optimizing Queries in Databricks SQL

Lecture 18 Optimizing Queries in Databricks SQL

Section 19: Working with Databricks Connect for BI Tools

Lecture 19 Working with Databricks Connect for BI Tools

Section 20: Using the Databricks SQL REST API

Lecture 20 Using the Databricks SQL REST API

Section 21: Introduction to Machine Learning with Databricks

Lecture 21 Introduction to Machine Learning with Databricks

Section 22: Feature Engineering in Databricks

Lecture 22 Feature Engineering in Databricks

Section 23: Building ML Models with Databricks MLFlow

Lecture 23 Building ML Models with Databricks MLFlow

Section 24: Hyperparameter Tuning in Databricks

Lecture 24 Part 1 - Hyperparameter Tuning in Databricks

Lecture 25 Part 2 - Hyperparameter Tuning in Databricks

Section 25: Deploying ML Models with Databricks

Lecture 26 Deploying ML Models with Databricks

Section 26: Integrating Databricks with Azure Data Factory

Lecture 27 Integrating Databricks with Azure Data Factory

Section 27: Connecting Databricks with AWS S3 Buckets

Lecture 28 Connecting Databricks with AWS S3 Buckets

Section 28: Databricks REST API Basics

Lecture 29 Databricks REST API Basics

Section 29: Connecting Power BI with Databricks

Lecture 30 Connecting Power BI with Databricks

Section 30: Integrating Snowflake with Databricks

Lecture 31 Integrating Snowflake with Databricks

Section 31: Understanding Databricks Auto-Scaling

Lecture 32 Understanding Databricks Auto-Scaling

Section 32: Cluster Performance Optimization Techniques

Lecture 33 Cluster Performance Optimization Techniques

Section 33: Partitioning and Bucketing in Databricks

Lecture 34 Part 1 - Partitioning and Bucketing in Databricks

Lecture 35 Part 2 - Partitioning and Bucketing in Databricks

Section 34: Managing Metadata with Hive Tables in Databricks

Lecture 36 Managing Metadata with Hive Tables in Databricks

Section 35: Cost Optimization in Databricks

Lecture 37 Cost Optimization in Databricks

Section 36: Securing Data in Databricks using Role-Based Access Control

Lecture 38 Securing Data in Databricks using Role-Based Access Control

Section 37: Setting up Secure Connections in Databricks

Lecture 39 Setting up Secure Connections in Databricks

Section 38: Managing Encryption in Databricks

Lecture 40 Managing Encryption in Databricks

Section 39: Auditing and Monitoring in Databricks

Lecture 41 Auditing and Monitoring in Databricks

Section 40: Real-Time Streaming Analytics with Databricks

Lecture 42 Real-Time Streaming Analytics with Databricks

Section 41: Data Warehousing Use Cases in Databricks

Lecture 43 Data Warehousing Use Cases in Databricks

Section 42: Building Customer Segmentation Models with Databricks

Lecture 44 Building Customer Segmentation Models with Databricks

Section 43: Predictive Maintenance using Databricks

Lecture 45 Predictive Maintenance using Databricks

Section 44: IoT Data Analysis in Databricks

Lecture 46 IoT Data Analysis in Databricks

Section 45: Using GraphFrames for Graph Processing in Databricks

Lecture 47 Using GraphFrames for Graph Processing in Databricks

Section 46: Time Series Analysis with Databricks

Lecture 48 Time Series Analysis with Databricks

Section 47: Data Lineage Techniques in Databricks

Lecture 49 Data Lineage Techniques in Databricks

Section 48: Building Custom Libraries for Databricks

Lecture 50 Building Custom Libraries for Databricks

Section 49: CI/CD Pipelines for Databricks Projects

Lecture 51 CI/CD Pipelines for Databricks Projects

Section 50: Best Practices for Managing Databricks Projects

Lecture 52 Best Practices for Managing Databricks Projects

Data Engineers – Professionals working with ETL pipelines, data transformation, and big data processing.,Data Scientists – Those looking to use Databricks for machine learning, feature engineering, and predictive analytics.,Big Data Analysts – Individuals working with large-scale datasets, SQL queries, and business intelligence tools.,Cloud Engineers – Professionals integrating Databricks with AWS, Azure, and Google Cloud for scalable data solutions.,Machine Learning Engineers – Those building and deploying ML models using MLflow, hyperparameter tuning, and automation.,Business Intelligence Professionals – Users working with Databricks SQL, Power BI, and dashboarding tools.,Database Administrators – DBAs managing data lakes, Delta Lake, Hive tables, and metadata in Databricks.,Software Engineers – Developers looking to understand Apache Spark, API integrations, and data pipeline automation.,AI & IoT Specialists – Professionals working on real-time analytics, IoT data processing, and AI-driven insights.,Enterprise Architects – Those designing scalable, cost-effective, and high-performance data platforms.,Cloud Data Professionals – Individuals managing data migration, cost optimization, and auto-scaling clusters.,Students & Graduates – Learners interested in big data technologies, cloud computing, and machine learning.,Finance & Healthcare Analysts – Professionals working with large datasets for fraud detection, risk analysis, and patient insights.,Consultants & Freelancers – Independent professionals offering Databricks consulting, cloud data engineering, and analytics solutions.,Technology Leaders & Decision Makers – CTOs, data managers, and tech leads looking to implement Databricks for business transformation.