TS Technologies is a forward-thinking IT solutions provider specializing in cloud computing, data engineering, and enterprise transformation. With a focus on innovation and scalable solutions, TS Technologies empowers businesses to harness the full potential of modern data platforms, ensuring they stay ahead in a competitive digital landscape.
Azure Data Engineer with Fabric:
Our “Azure Data Engineer with Fabric” service is designed for organizations looking to unlock the full power of Microsoft’s unified data platform – Microsoft Fabric. Combining the robust capabilities of Azure Data Services with the modern features of Fabric, our solution enables seamless data integration, transformation, and analytics across your entire data estate.
Why Choose TS Technologies?
- Expertise in Microsoft Azure and Fabric ecosystem
- Proven track record in large-scale data modernization projects
- Dedicated support and advisory for end-to-end data strategy
- Accelerators and frameworks for faster time to value
Course Content
Azure for the Data Engineer
- Cloud Computing basics
- Introduction to Microsoft Azure
- Applications of Azure
- Azure Services
- Understanding Data
- On-premises vs Cloud-based servers
- Azure Data Engineer roles & tasks
- Data Engineering processes
- Use cases for the Cloud
Data Services for the Azure Data Engineer
- Azure Datalake Storage (ADLS) gen2
- Azure Blob Storage
- Azure SQL database
- Azure Data Factory
- Azure Databricks
- Azure Synapse Analytics
Data Engineering using Microsoft Fabric
- Introduction to Microsoft Fabric
- Understanding the Fabric Analytics platform
- Ingesting data using Pipelines in Fabric
- Introduction to Dataflows gen2
- Ingest and Transform data using Dataflows gen2
- Analyze data with Apache Spark in Fabric
- Working with Lakehouse in Fabric Analytics
- Working with Delta Tables
- Real-Time Dashboards in Fabric
- Analyzing data in a Data Warehouse in Microsoft Fabric
- Load, Query & Monitor data in a Data Warehouse
- Perform Data Analysis using Fabric Notebooks
- Perform Data Analysis using Power BI
- Workspace and Permissions
- Implementing Row-level & Column-level security
- Data Access Roles and Access controls
Data Storages on Azure
- File stores
- Relational data store
- Non-Relational data stores
- Azure Blob storage
- NoSQL & Azure Cosmos DB
- Azure Blob Storage
- Azure Data Lake Storage
- Why Data Lake
- Data Lake architecture
- Azure SQL Database
- Azure database for MySQL
- Azure database for MariaDB
- Azure database for PostgreSQL
- Azure Synapse Analytics
Hands-on Real-time ADE Scenarios
- Exploring Data services on Azure Portal
- Creating and setting-up Blob & ADLS accounts
- Creating Linked Service, Integration Runtime in ADF
- Creating ETL/ELT Pipelines in Azure Data Factory
- Working with Control Flow activities in ADF Pipelines
- Working with Data Flow Transformations in ADF Pipelines
- Ingesting various File sources using ADF pipelines
- Ingesting On-prem SQL Server data to Azure SQL database
- Ingesting multiple file sources using a single Pipeline
- Ingesting multiple SQL tables using single Pipeline
- Copying data from Blob storage to ADLS
- Copying data from Azure SQL database to ADLS
- Copying data from Blob storage to Azure SQL database
- Implementing Batch load & Sequential load
- Incremental loading and Change data capture
- SCD1 Implementation using ADF Mapping dataflows
- SCD2 Implementation using ADF Mapping dataflows
- Creating Azure Databricks Workspace and Cluster
- DBFS and DBUTILS
- Analyzing data with Databricks Notebooks
- ADLS integration with Azure Databricks
- Mounting ADLS to Azure Databricks using Access Keys
- Secret scopes, Service Principal & SAS Tokens
- Creating Delta Tables
- Working with Lakehouse architecture
Data Integration, CI/CD Pipelines using Azure Data Factory (ADF)
- Introduction to ADF
- ETL vs ELT
- Data Integration Patterns
- Code-free ETL as a Service
- ADF Components
- Linked Services & Connectors
- Integration Runtime
- Pipelines and activities
- Data Ingestion methods
- Data Movement
- Data Transformations
- Control flows
- Scheduling & Triggering Pipelines
- Monitoring Pipelines
- CI/CD Pipelines with ADF
Data Engineering with Azure Databricks
- Introduction to Azure Databricks
- Databricks runtime engine
- Cluster & Workspace
- Databricks file system (DBFS)
- Accessing data using Databricks UI
- Accessing data with Databricks Notebooks
- Working with different Data formats
- Data Analysis using Apache Spark
- Processing data with PySpark
- Apache Spark Ecosystem
- Apache Spark architecture
- Spark Transformations & Actions
- Working with Spark SQL
- Delta Lakes & Tables
- Lakehouse architecture
- Structured Streaming
- Scheduling Databricks jobs using ADF pipelines
Real-time Projects
- Data Migration using ADF (migrating on-prem SQL Server data to Azure)
- End-to-End Pipelines, data processing using ADF and Azure Databricks
- Incremental load & CDC
- Data Ingestion using Pipelines in Microsoft Fabric
- Working with Apache Spark in Fabric
- Implementing Data Warehouse in Fabric
- Analyzing data with Power BI in Fabric
- Loading data from AWS S3 to ADLS using ADF Pipeline
- Loading data from ADLS to Snowflake
- CI/CD pipelines using ADF
Bonuses
- ADE Interview Questions and Answers
- Mock Interviews
- Placement Assistance
- ADE Certification (DP700) Support
- Databricks certification support
- Resume/CV Preparation
- LinkedIn Profile Optimization
