Data Engineer

Azure Data Engineer Training Content :
1.Azure Data Factory

2.Azure Data Bricks

3.Pyspark

4.Python

5.SparkSQL  

Duration: 60 Days

Module 1: Introduction to Azure Data Factory

• Overview of ADF: What is Azure Data Factory? Features and Use Cases.
• Components of ADF: Pipelines, Activities, Datasets, Linked Services, and Triggers.
• ADF Architecture: Integration Runtime, Data Flows, and Monitoring.
• Hands-On: Create your first ADF pipeline with a Copy Data Activity.

Module 2: Linked Services and Datasets

• Understanding Linked Services: Connecting to Azure Storage, SQL Databases, and more.
• Creating Datasets: Defining input and output data structures.
• Parameterization: Using parameters to make pipelines reusable.
• Hands-On: Connect to ADLS and SQL Database using Linked Services.

Module 3: Pipelines and Activities

• Understanding Pipelines: Building end-to-end workflows.
• Types of Activities: Copy, Lookup, ForEach, Filter, Web, and Stored Procedure Activities.
• Data Movement Activities: Copying data from source to destination.
• Hands-On: Create a pipeline to copy data from ADLS to Azure Synapse.

Module 4: Control Flow and Pipeline Parameters

• Control Flow Activities: ForEach, Until, If Condition, Switch.
• Using Parameters: Passing values dynamically to pipelines.
• Variables and Expressions: Storing and manipulating values within pipelines.
• Hands-On: Build a pipeline with a ForEach loop to process multiple files.

Module 5: Mapping Data Flows

• Introduction to Data Flows: What are Mapping Data Flows?
• Transformations: Aggregate, Join, Union, Lookup, Pivot, and Derived Column.
• Data Flow Optimizations: Partitioning and Performance Tuning.
• Hands-On: Create a Data Flow to clean and transform customer data.

Module 6: Triggers and Scheduling Pipelines

• Types of Triggers: Manual, Scheduled, Tumbling Window, and Event-Based Triggers.
• Trigger Dependencies: Setting up pipeline dependencies.
• Monitoring and Alerts: Using ADF Monitoring Dashboard.
• Hands-On: Schedule a pipeline to run every day at midnight.

Module 7: Integration with Other Azure Services

• Integrating with Azure Databricks: Running notebooks from ADF.
• Loading Data into Azure Synapse: Using Copy and Stored Procedure Activities.
• Integration with Azure Key Vault: Securely managing credentials.
• Hands-On: Build a pipeline to run a Databricks notebook and save output to Synapse.

Module 8: Error Handling and Monitoring

• Error Handling Mechanisms: Using Try-Catch with Fail, Continue, or Retry policies.
• Monitoring Pipelines: Using ADF Monitoring Hub and Azure Monitor.
• Setting Alerts: Configuring alerts for pipeline failures.
• Hands-On: Add error-handling logic to a pipeline and test it.

Module 9: CI/CD for ADF Pipelines

• Introduction to CI/CD in ADF: What is CI/CD?
• Integrating with Azure DevOps: Connecting ADF to a Git repository.
• Building and Deploying Pipelines: Using DevOps release pipelines.
• Hands-On: Implement a CI/CD pipeline for deploying ADF from Dev to Prod.

Module 10: Real-World Project – End-to-End ETL Pipeline

• Project Overview: Build a Data Integration Pipeline for Sales Analytics.
• Step 1: Ingest Raw Data from SFTP to ADLS (Bronze Layer).
• Step 2: Transform Data using Azure Databricks (Silver Layer).
• Step 3: Load Aggregated Data into Azure Synapse (Gold Layer).
• Step 4: Schedule Pipelines and Monitor Performance.
• Step 5: Implement CI/CD for the pipeline using Azure DevOps.
• Final Outcome: A fully automated and production-ready ETL pipeline.

Databricks with PySpark:

Module 1: Introduction to Databricks and PySpark

• Overview of Databricks Platform: What is Databricks? Features and Advantages.
• Introduction to PySpark: Basics of Spark, Spark Architecture, and Components.
• Databricks Workspace: Notebooks, Clusters, Jobs, and Workflows.
• Hands-On: Creating a Databricks Notebook and running basic PySpark commands.

Module 2: PySpark DataFrames and Transformations

• Creating DataFrames: From CSV, Parquet, JSON, and databases.
• DataFrame Operations: select(), filter(), groupBy(), agg(), withColumn(), join()
• Data Types and Schemas: Defining and Managing Schemas.
• Hands-On: Reading a CSV file from ADLS and performing transformations.

Module 3: Working with Azure Data Lake Storage (ADLS)

• Connecting Databricks to ADLS: Using dbutils and Mounting ADLS.
• Reading and Writing Data: Parquet, Delta, and JSON formats.
• Managing Partitions: Optimizing data storage and queries.
• Hands-On: Load data from ADLS, transform it, and save it as Delta tables.

Module 4: PySpark SQL and Querying Data

• Introduction to Spark SQL: Writing SQL queries in Databricks.
• Creating Temp Views: Using createOrReplaceTempView() for querying.
• Joins and Aggregations: Inner, Outer, Left, and Right Joins.
• Hands-On: Create a SQL view from a DataFrame and run analytical queries.

Module 5: Managing Incremental Loads

• Understanding Incremental Loads: Full vs. Incremental data loads.
• Using Delta Tables for Incremental Updates: merge, update, delete.
• Handling Late Arriving Data: Using upsert operations.
• Hands-On: Build an incremental data pipeline using PySpark and Delta Lake.

Module 6: Optimizing PySpark Jobs in Databricks

• Performance Tuning Techniques: Partitioning, Caching, and Broadcast Joins.
• Understanding Job Execution Plans: Spark UI and DAG Visualization.
• Handling Large Datasets: Bucketing and Shuffling.
• Hands-On: Optimize a slow PySpark job and compare execution times.

Module 7: Scheduling and Automation in Databricks

• Creating and Scheduling Jobs: Using Databricks Jobs and Task Dependencies.
• Integrating with Azure Data Factory (ADF): Automating pipeline execution.
• Alerting and Monitoring: Setting up alerts for job failures.
• Hands-On: Schedule a daily job and monitor its execution.

Module 8: Real-World Project – End-to-End Data Pipeline

• Project Overview: Build a Sales Analytics Dashboard.
• Step 1: Load Raw Data from ADLS into Bronze Layer.
• Step 2: Clean and Transform Data in Silver Layer.
• Step 3: Aggregate and Store Data in Gold Layer.
• Step 4: Visualize Results in Power BI.
• Step 5: Schedule the pipeline and monitor performance.
• Final Outcome: A fully automated and optimized data pipeline.     
  

About Data Engineer

A Data Engineer is a professional responsible for designing, building, and maintaining systems that collect, store, and process large volumes of data. Their primary goal is to ensure that data is accessible, reliable, and ready for analysis by data scientists, analysts, and business teams.
Topics Covered:

Azure Data Factory
Azure Databricks

Python

Pyspark
SparkSQL

Key Responsibilities of a Data Engineer:

• Data Pipeline Development: Build and maintain ETL (Extract, Transform, Load) pipelines to move data from various sources to data warehouses or data lakes.
• Data Integration: Combine data from different sources (e.g., databases, APIs, cloud storage) into a unified format.
• Data Storage: Design and manage databases, data lakes, and data warehouses to store structured and unstructured data.
• Data Transformation: Clean, normalize, and enrich raw data to make it suitable for analysis.
• Performance Optimization: Improve query performance and ensure systems handle large-scale data efficiently.
• Collaboration: Work with data scientists, analysts, and business teams to understand data needs and deliver solutions.
• Data Governance: Implement security, privacy, and compliance measures to protect sensitive data.
• Automation: Develop scripts and workflows for data ingestion, transformation, and reporting.

 Common Tools and Technologies Used by Data Engineers:

• Programming Languages: Python, SQL, Scala, Java
• Big Data Technologies: Apache Spark, Hadoop
• Data Storage: Azure Data Lake, Amazon S3, Google BigQuery, Snowflake
• Databases: SQL Server, PostgreSQL, MySQL, Cassandra
• ETL Tools: Azure Data Factory, Apache NiFi, Talend
• Workflow Orchestration: Apache Airflow, Luigi
• Cloud Platforms: Azure, AWS, GCP
• CI/CD: Azure DevOps, Jenkins
• Reporting Tools: Power BI, Tableau

Difference Between a Data Engineer and a Data Scientist:

Aspect	Data Engineer	Data Scientist
Focus	Building pipelines, managing infrastructure	Analyzing data, building models
Skills	Programming, ETL, database management	Statistics, machine learning, data visualization
Tools	Spark, Hadoop, Azure Data Factory	Jupyter, TensorFlow, Scikit-learn
Outcome	Reliable, accessible data for analysis	Insights, predictions, and business strategies