Azure Databricks with Spark Live Batch-1 Session -1 by Abhishek Agarwal at Raj Cloud technologies

Mode of Training : Online

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💡 About Raj Cloud Technologies :

Raj Cloud Technologies is the No.1 quality training institute in India for the candidates who want to build their future in Information Technology. We are into online training, class room training, corporate training and one to one training with more passion and dedication. Raj Cloud Technologies aims in providing best quality realtime oriented trainings on , Informatica, Azure Data engineering & Azure Data Bricks, IICS/IDMC ,Python, DJango, Angular, React JS, Salesforce, Testing, Android, Docker, Kubernates, Manual Testing, AWS , DevOps , Tableau , Power-BI and Selenium

Our Online Training Features
🎈 Training with Real-time Working Professionals
🎈 RealTime Projects
🎈 Industry Matching use cases
🎈 Live Coding
🎈 Real-time Environment
🎈 Class Notes
🎈 Doubts Clarifications in Each Session
🎈 Resume Preperation
🎈 Interview Questions
🎈 Mock Interview
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Big Data Processing Using Data Bricks and Spark
Introduction to Data Bricks and Spark Architecture
• Overview of Databricks and Spark Architecture
Databricks Workspace Overview
• Understanding Databricks Services and Features
• Cluster Management: Creation, Autoscaling, and Administration
RDD and Data Frame Fundamentals
• RDD (Resilient Distributed Dataset) Overview
• Data Frame Spark API and Data Source API Fundamentals
• Conversion between PySpark and Pandas
• Common Transformation Techniques in PySpark
• Transformations vs. Actions
Dbutils and Parameterization
• Usage of Dbutils for File System Interaction
• Parameterization Techniques in Databricks
Delta Lake and Delta Table
• Table Manipulation with Delta Lake
Read and Process CSV, JSON, and XML Files
• Types of Views in Databricks (Global, Local, Temporary)
• Managed vs. Unmanaged Tables
• Versioning and Time Travel in Delta Lake
Azure DevOps and Git Integration
• Azure DevOps and Git Workflow Integration
• Cherry Pick and Git Revert Commands
Secret Scope Creation and Management
JDBC Connector for SQL Server
Project 1 - Retail Dashboarding with Azure Databricks
Data processing using Pyspark and creating Delta Lake
Databricks CLI and Backup Process
Databricks CLI Overview and Installation
• Backup Process Setup for Notebooks and Configurations

Understanding Spark UI
• Navigating the Spark UI for Job Monitoring
• Understanding the Stages, Tasks, and Execution Plans
Unity Catalog and SCD (Slowly Changing Dimensions) Implementation.
Overview of Unity Catalog for Data Governance
SCD Types (Type 1, Type 2, Type 3) and Implementation
Lakehouse and Medallion Architecture
Introduction to Lakehouse Architecture
• Medallion Architecture (Bronze, Silver, Gold Layers) Overview
Spark Optimization Techniques
User-Defined Functions (UDF) for Custom Transformations
• Catalyst Optimizer and Data Frame. explain for Optimization
Directed Acyclic Graphs (DAG) and Adaptive Query Execution (AQE)
Predicate Pushdown and Projection Pushdown
• Repartition, Coalesce, Cache, and Persist

Project 2-Medallion Architecture with Unity Catalog
Process data using PySpark and create Delta Lake while applying Medallion Architecture for structured data layers.
Handling Complex Data and Advanced Joins
• Handling Complex JSON, Struct, and Nested Data Types
Data Skew and Techniques for Handling Skewed Data
Sort Merge Join, Broadcast Join, and Optimizing Joins
• Z-Ordering for Efficient Querying
Orchestration and Scheduling Techniques
• Job Orchestration and Scheduling with Databricks Jobs API

Best Practices for Workflow Orchestration
Resume Preparation and Interview tips
#AzureDataEngineering #AzureDatabricks #DeltaLake #LakehouseArchitecture #DataAI #DataAISummit #UnifiedAnalytics #GenAI #ApacheSpark #SparkSQL #DatabricksCommunity #UnityCatalog #Raj cloud tecchnologies

Видео Azure Databricks with Spark Live Batch-1 Session -1 by Abhishek Agarwal at Raj Cloud technologies канала Raj Informatica Realtime HandsOn JobBased Training