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Batch Date: Dec 15th @7:30AM

Faculty: Mr. Shaik Saidhul (7+ Yrs of Exp,.. & Real Time Expert)

(Google Certified Professional Data Engineer)

Duration: 45 Days

Venue :
DURGA SOFTWARE SOLUTIONS,
Flat No : 202, 2nd Floor,
HUDA Maitrivanam,
Ameerpet, Hyderabad - 500038

Ph.No: +91 - 9246212143, 80 96 96 96 96

Syllabus:

Google Cloud Data Engineering Training
with Real-world Projects and Case Studies

GCP Cloud Basics

GCP Introduction

• The need for cloud computing in modern businesses.
• Key features and offerings of Google Cloud Platform (GCP).
• Overview of core GCP services and products.
• Benefits and advantages of using cloud infrastructure.
• Step-by-step guide to creating a free-tier account on GCP.

GCP Interfaces

• Console
  • Navigating the GCP Console
  • Configuring the GCP Console for Efficiency
  • Using the GCP Console for Service Management
• Shell
  • Introduction to GCP Shell
  • Command-line Interface (CLI) Basics
  • GCP Shell Commands for Service Deployment and Management
• SDK
  • Overview of GCP Software Development Kits (SDKs)
  • Installing and Configuring SDKs
  • Writing and Executing GCP SDK Commands

GCP Locations

• Regions
  • Understanding GCP Regions
  • Selecting Regions for Service Deployment
  • Impact of Region on Service Performance
• Zones
  • Exploring GCP Zones
  • Distributing Resources Across Zones
  • High Availability and Disaster Recovery Considerations
    
• Importance
• Significance of Choosing the Right Location
• Global vs. Regional Resources
• Factors Influencing Location Decisions

GCP IAM & Admin

• Identities
  • Introduction to Identity and Access Management (IAM)
  • Users, Groups, and Service Accounts
  • Best Practices for Identity Management
• Roles
  • GCP IAM Roles Overview
  • Defining Custom Roles
  • Role-Based Access Control (RBAC) Implementation
• Policy
  • Resource-based Policies
  • Understanding and Implementing Organization Policies
  • Auditing and Monitoring Policies
• Resource Hierarchy
  • GCP Resource Hierarchy Structure
  • Managing Resources in a Hierarchy
  • Organizational Structure Best Practices

Linux Basics on Cloud Shell

• Getting started with Linux
• Linux Installation
• Basic Linux Commands
• Cloud shell tips
• File and Directory Operations
  (ls, cd, pwd, mkdir, rmdir, cp, mv, touch, rm, nano)
• File Content Manipulation (cat, less, head, tail, grep)
• Text Processing (awk, sed, cut, sort, uniq)
• User and Permission related (whoami, id, su, sudo, chmod, chown)

Python for Data Engineer

• Data Types
• Strings
• Operators
• Numbers (Int, Float)
• Booleans
• Data Structures
  • Lists
  • Tuples
  • Dictionaries
  • Sets
• Python Programming Constructs
  • if, elif, else statements
  • for loops, while loops
  • Exception Handling
  • File I/O operations
• Modular Programming in Python
  • Functions & Lambda Functions
  • Classes

GCP Data Engineering Tools

Google Cloud Storage

• Overview of Cloud Storage as a scalable and durable object storage service.
• Understanding buckets and objects in Cloud Storage.
• Use cases for Cloud Storage, such as data backup, multimedia storage, and website content
• Creating and managing Cloud Storage buckets.
• Uploading and downloading objects to and from Cloud Storage.
• Setting access controls and permissions for buckets and objects.
• Data Transfer and Lifecycle Management
• Versioning and Object Versioning
• Integration with Other GCP Services
• Implementing best practices for optimizing Cloud Storage performance.
• Securing data in Cloud Storage with encryption and access controls.
• Monitoring and logging for Cloud Storage operations.

Cloud SQL

• Introduction to Cloud SQL
• Creating and Managing Cloud SQL Instances
• Configuring database settings, users, and access controls.
• Connecting to Cloud SQL instances using Cloud SQL studio, Shell, Workbenches
• Importing and exporting data in Cloud SQL.
• Backups and High Availability
• Integration with Other GCP Services
• Managing database user roles and permissions.
• Introduction to DMS
• End to End Database migration Project
• Offline: Export and Import method
• Online: DMS method

BigQuery (SQL Development)

• Introduction to BigQuery
• BigQuery Architecture
• Use cases for BigQuery in business intelligence and analytics.
• Various method of creating table in BigQuery
• BigQuery Data Sources and File Formats
• Native table and External Tables
• SQL Queries and Performance Optimization
  • Writing and optimizing SQL queries in BigQuery.
  • Understanding query execution plans and best practices.
  • Partitioning and clustering tables for performance.
• Data Integration and Export
  • Loading data into BigQuery from Cloud Storage, Cloud SQL, and other sources.
  • Exporting data from BigQuery to various formats.
  • Real-time data streaming into BigQuery.
• Configuring access controls and permissions in BigQuery.
• BigQuery Views:
  • Views
  • Materialized Views
  • Authorized Views
• Integration with Other GCP Services
  • Integrating BigQuery with Dataflow for ETL processes.
  • Building data pipelines with BigQuery and Composer.
• Case Study-1: Spotify
• Case Study-2: Social Media

DataProc (Pyspark Development)

• Introduction to Hadoop and Apache Spark
• Understanding the difference between Spark and MapReduce
• What is Spark and Pyspark.
• Understanding Spark framework and its functionalities
• Overview of DataProc as a fully managed Apache Spark and Hadoop service.
• Use cases for DataProc in data processing and analytics.
• Cluster Creation and Configuration
  • Creating and managing DataProc clusters.
  • Configuring cluster properties for performance and scalability.
  • Preemptible instances and cost optimization.
• Running Jobs on DataProc
  • Submitting and monitoring Spark and Hadoop jobs on DataProc.
  • Use of initialization actions and custom scripts.
  • Job debugging and troubleshooting.
• Integration with Storage and BigQuery
  • Reading and writing data from/to Cloud Storage and BigQuery.
  • Integrating DataProc with other storage solutions.
  • Performance optimization for data access.
• Automation and scheduling of recurring jobs.
• Case Study-1: Data Cleaning of Employee Travel Records
• End to End Batch Pyspark pipeline using Dataproc, BigQuery, GCS

Databricks on GCP

• What is Databricks lakehouse platform
• Databricks architecture and components
• Setting up and Administering a Databricks workspace
• Managing data with Delta Lake
• Databricks Unity Catalog
• Note books and clusters
• ELT with Spark SQL and Python
• optimize performance within Databricks.
• Incremental Data Processing
• Delta Live tables
• Case study: creating end to end workflows

DataFlow (Apache Beam development)

• Introduction to DataFlow
• Use cases for DataFlow in real-time analytics and ETL.
• Understanding the difference between Apache Spark and Apache Beam
• How Dataflow is different from Dataproc
• Building Data Pipelines with Apache Beam
• Writing Apache Beam pipelines for batch and stream processing.
• Custom Pipelines and Pre-defined pipelines
• Transformations and windowing concepts.
• Integration with Other GCP Services
  • Integrating DataFlow with BigQuery, Pub/Sub, and other GCP services.
  • Real-time analytics and visualization using DataFlow and BigQuery.
  • Workflow orchestration with Composer.
• End to End Streaming Pipeline using Apache beam with Dataflow, Python app, PubSub, BigQuery, GCS
• Template method of creating pipelines

Cloud Pub/Sub

• Introduction to Pub/Sub
• Understanding the role of Pub/Sub in event-driven architectures.
• Key Pub/Sub concepts: topics, subscriptions, messages, and acknowledgments.
• Creating and Managing Topics and Subscriptions
  • Using the GCP Console to create Pub/Sub topics and subscriptions.
  • Configuring message retention policies and acknowledgment settings.
• Publishing and Consuming Messages
  • Writing and deploying code to publish messages to a topic.
  • Implementing subscribers to consume and process messages from subscriptions.
• Integration with Other GCP Services
  • Connecting Pub/Sub with Cloud Functions for serverless event-driven computing.
  • Integrating Pub/Sub with Dataflow for real-time stream processing.
• Streaming use-case using Dataflow

Cloud Composer (DAG Creations)

• Introduction to Composer/Airflow
• Overview of Airflow Architecture
• Use cases for Composer in managing and scheduling workflows.
• Creating and Managing Workflows
  • Creating and configuring Composer environments.
  • Defining and scheduling workflows using Apache Airflow.
  • Monitoring and managing workflow executions.
• Integration with Data Engineering Services
  • Orchestrating workflows involving BigQuery, DataFlow, and other services.
  • Coordinating ETL processes with Composer.
  • Integrating with external systems and APIs.
• Error Handling and Troubleshooting
  • Handling errors and retries in Composer workflows.
  • Debugging and troubleshooting failed workflow executions.
  • Logging and monitoring for Composer workflows.
• Level-1-DAG: Orchestrating the BigQuery pipelines
• Level-2-DAG: Orchestrating the DataProc pipelines
• Level-3-DAG: Orchestrating the Dataflow pipelines
• Implementing CI/CD in Composer Using Cloud Build and GitHub

Data Fusion

• Introduction to Data Fusion
  • Overview of Data Fusion as a fully managed data integration service.
  • Use cases for Data Fusion in ETL and data migration.
• Building Data Integration Pipelines
• Creating ETL pipelines using the visual interface.
• Configuring data sources, transformations, and sinks.
• Using pre-built templates for common integration scenarios.
• Integration with GCP and External Services
  • Integrating Data Fusion with BigQuery, Cloud Storage, and other GCP services.
• End to End pipeline using Data fusion with Wrangler, GCS, BigQuery

Cloud Functions

• Cloud Functions Introduction
• Setting up Cloud Functions in GCP
• Event-driven architecture and use cases
• Writing and deploying Cloud Functions
• Triggering Cloud Functions:
  • HTTP triggers
  • Pub/Sub triggers
  • Cloud Storage triggers
• Monitoring and logging Cloud Functions
• Usecase-1: Loading the files from GCS to BigQuery as soon as it is uploaded.

Terraform

• Terraform Introduction
• Installing and configuring Terraform.
• Infrastructure Provisioning
• Terraform basic commands
• Init, plan, apply, destroy
• Create Resources in Google Cloud Platform
  • GCS buckets
  • Dataproc cluster
  • BigQuery Datasets and tables
  • And more resources as needed

By the End of the course What Students can Expect

Proficient in SQL Development:

• Mastering SQL for querying and manipulating data within Google BigQuery and Cloud SQL.
• Writing complex queries and optimizing performance for large-scale datasets.
• Understanding schema design and best practices for efficient data storage.

Pyspark Development Skills:

• Proficiency in using PySpark for large-scale data processing on Google Cloud.
• Developing and optimizing Spark jobs for distributed data processing.
• Understanding Spark's RDDs, DataFrames, and transformations for data manipulation.

Apache Beam Development Mastery:

• Creating data processing pipelines using Apache Beam.
• Understanding the concepts of parallel processing and data parallelism.
• Implementing transformations and integrating with other GCP services.

DAG Creations with Cloud Composer:

• Designing and implementing Directed Acyclic Graphs (DAGs) for orchestrating workflows.
• Using Cloud Composer for workflow automation and managing dependencies.
• Developing DAGs that integrate various GCP services for end-to-end data processing.

Notebooks, Workflows with Databricks:

• Understand how to build and manage data pipelines using Databricks and Delta Lake.
• Efficiently query and analyze large datasets with Databricks SQL and Apache Spark.
• Implement scalable workflows and optimize performance within Databricks.

Architecture Planning:

• Proficient in architecting end-to-end data solutions on GCP.
• Understanding the principles of designing scalable, reliable, and cost-effective data architectures.

Certification Readiness

• Prepare for the Google Cloud Professional Data Engineer (PDE) and
• Associate Cloud Engineer (ACE) certifications through a combination of theoretical knowledge and hands-on experience.

The course will empower students with practical skills in SQL, PySpark, Apache Beam, DAG creations, and architecture planning, ensuring they are well-prepared to tackle real-world data engineering challenges and successfully obtain GCP certifications.