Udemy – Databricks: Master Data Engineering, Big Data, Analytics, AI 2025-3

Apr 18, 2025 - 19:32

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Databricks Course: Master Data Engineering, Big Data, Analytics, AI. This course will help you become an expert in data engineering, big data analytics, and artificial intelligence using the Databricks platform. Databricks is a cloud-based data engineering, analytics, and machine learning platform built on Apache Spark. The platform provides a unified environment for processing big data, performing analytics, and deploying machine learning models. Databricks simplifies data engineering and collaboration by providing a single workspace where data engineers, data scientists, and analysts can work together efficiently. The platform is available on Microsoft Azure, Amazon Web Services, and Google Cloud, making it a versatile choice for companies working with large data sets. Databricks is widely used in industries such as finance, healthcare, retail, and technology to efficiently manage big data workloads. The platform offers a powerful and scalable solution for organizations looking to leverage big data for analytics, machine learning, and business intelligence.

What you will learn:

Understanding Databricks architecture: Learn the key components, workspace features, and advantages of Databricks over traditional data platforms.
Setting up and configuring Databricks: Creating a Databricks workspace, managing clusters, and navigating notebooks for data processing.
Perform ETL operations: Use Apache Spark on Databricks to extract, transform, and load (ETL) large data sets efficiently.
Working with Delta Lake: Implementing incremental data loading, schema evolution, and time travel features using Delta Lake.
Running SQL queries in Databricks: Use Databricks SQL to query and analyze structured data, optimize performance, and create dashboards.
Building and deploying machine learning models: Using MLflow for model tracking, hyperparameter tuning, and deploying ML models in Databricks.
Databricks integration with cloud services: Connect Databricks with AWS S3, Azure Data Factory, Snowflake, and BI tools like Power BI.
Cluster Performance Optimization: Learn autoscaling, partitioning, bucketing, and performance tuning techniques to manage big data workloads.
Implementing real-time data processing: Developing streaming analytics pipelines for processing IoT and real-time events in Databricks.
Securing data in Databricks: Apply role-based access control (RBAC), encryption, and auditing to protect sensitive data.
Developing CI/CD pipelines for Databricks: Automate deployment and testing using GitHub, Azure DevOps, and the Databricks REST API.
Data Warehouse Management in Databricks: Designing 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.
Databricks Workload Monitoring and Auditing: Track resource usage, job performance, and cost optimization strategies for efficient cloud usage.
Applying Databricks to real-world use cases: Working on projects like customer segmentation, predictive maintenance, and fraud detection using Databricks.

Who is this course suitable for?

Data Engineers: Professionals who work 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: People who work with large data sets, SQL queries, and business intelligence tools.
Cloud Engineers: Experts who integrate Databricks with AWS, Azure, and Google Cloud for scalable data solutions.
Machine Learning Engineers: Those who build and deploy ML models using MLflow, hyperparameter tuning, and automation.
Business Intelligence Professionals: Users who work with Databricks SQL, Power BI, and dashboard tools.
Database Administrators: DBAs who manage data lakes, Delta Lake, Hive tables, and metadata in Databricks.
Software Engineers: Developers looking to understand Apache Spark, API integration, and data pipeline automation.
AI and IoT specialists: Experts working on real-time analytics, IoT data processing, and AI-based insights.
Enterprise Architects: Those who design scalable, cost-effective, and high-performance data platforms.
Cloud data specialists: People who manage data migration, cost optimization, and auto-scaling clusters.
Students and Graduates: Learners interested in big data technologies, cloud computing, and machine learning.
Financial and healthcare analysts: professionals who work with big data sets for fraud detection, risk analysis, and patient insights.
Consultants and Freelancers: Independent professionals who provide Databricks consulting, cloud data engineering, and analytics solutions.
Technology leaders and decision makers: CTOs, data managers, and technology leaders looking to implement Databricks for business transformation.