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GCP DATA ENGINEERING Course Details
 

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Batch Date: July 17th @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 Platform Training
with Real-Time Projects

GCP Basics

GCP Introduction

  • Why we need Cloud.
  • Overview of Google Cloud Platform (GCP)
  • Key GCP Services and Products
  • Understanding Cloud Computing and its Benefits
  • How to create Free Tier Account in 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

GCP Networking

  • VPC (Virtual Private Cloud)
    • Creating and Configuring VPCs
    • Subnetting and IP Address Management
  • Load Balancer
    • Types of Load Balancers in GCP
    • Configuring and Managing Load Balancers
  • Firewalls
    • GCP Firewall Rules
    • Network Security Best Practices

Compute Options

  • Google Compute Engine (GCE)
    • Introduction to GCE and Virtual Machines (VMs)
    • Creating and Configuring VM Instances
    • Custom Images and Snapshots
  • Google Kubernetes Engine (GKE)
    • Overview of Kubernetes and Container Orchestration
    • Deploying and Managing Containerized Applications on GKE
    • Kubernetes Clusters and Node Pools
  • Google App Engine (GAE)
    • Understanding the App Engine Platform
    • Deploying Applications with App Engine
    • Configuring App Engine Services
  • Cloud Functions
    • Serverless Computing with Cloud Functions
    • Writing and Deploying Serverless Functions
    • Triggers and Events in Cloud Functions

    GCP Data Engineering Services

Google Cloud Storage

  • Introduction to 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 delivery.
  • Cloud Storage Operations
    • 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
    • Strategies for efficient data transfer to and from Cloud Storage.
    • Implementing data lifecycle policies for automatic object deletion or archival.
    • Utilizing Transfer Service for large-scale data transfers.
  • Versioning and Object Versioning
    • Enabling and managing versioning for Cloud Storage buckets.
    • Understanding how object versioning works.
    • Use cases for object versioning in data resilience and recovery.
  • Integration with Other GCP Services
    • Integrating Cloud Storage with BigQuery for data analytics.
    • Using Cloud Storage as a data source for Dataflow and Dataproc.
    • Exploring options for serving static content on websites.
  • Best Practices and Security
    • 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
    • Overview of Cloud SQL as a fully managed relational database service.
    • Supported database engines and use cases for Cloud SQL.
  • Creating and Managing Cloud SQL Instances
    • Creating MySQL or PostgreSQL instances.
    • Configuring database settings, users, and access controls.
    • Importing and exporting data in Cloud SQL.
  • Backups and High Availability
    • Configuring automated backups and performing manual backups.
    • Implementing high availability with failover replicas.
    • Strategies for restoring data from backups.
  • Scaling and Performance Optimization
    • Vertical and horizontal scaling options in Cloud SQL.
    • Performance optimization tips for database queries.
    • Monitoring and troubleshooting database performance.
  • Integration with Other GCP Services
    • Connecting Cloud SQL with App Engine, Compute Engine, and Kubernetes Engine.
    • Using Cloud SQL as a backend database for applications.
    • Data synchronization with Cloud Storage and BigQuery.
  • Security and Compliance
    • Implementing data encryption in transit and at rest.
    • Managing database user roles and permissions.
    • Ensuring compliance with industry standards.

Bigtable

  • Introduction to Bigtable
    • Overview of Bigtable as a fully managed NoSQL wide-column store.
    • Use cases for Bigtable in real-time analytics and IoT applications.
  • Key Concepts and Data Modeling
    • Understanding the key concepts of Bigtable: tables, rows, columns, and timestamps.
    • Designing effective data models for optimal performance.
  • Operations and Administration
    • Creating and managing Bigtable instances.
    • Configuring and monitoring clusters for performance.
    • Backing up and restoring data in Bigtable.
  • Integration with Data Processing Services
    • Integrating Bigtable with Dataflow and Dataproc for data processing.
    • Using Bigtable as a storage backend for Apache HBase.
  • Security Best Practices
    • Configuring access controls and permissions in Bigtable.
    • Implementing encryption for data at rest and in transit.
    • Auditing and monitoring for security compliance.
  • Advanced Topics
    • Exploring Bigtable replication for data redundancy.
    • Optimizing Bigtable performance for specific use cases.
    • Handling schema evolution and data migration.

BigQuery (SQL development)

  • Introduction to BigQuery
    • Overview of BigQuery as a fully managed, serverless data warehouse.
    • Use cases for BigQuery in business intelligence and analytics.
  • 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.
  • Access Controls and Security
  • Configuring access controls and permissions in BigQuery.
    • Implementing encryption for data in BigQuery.
    • Auditing and monitoring for security compliance.
  • Integration with Other GCP Services
    • Integrating BigQuery with Dataflow for ETL processes.
    • Using BigQuery in conjunction with Data Studio for visualization.
    • Building data pipelines with BigQuery and Composer.

DataProc (Pyspark Development)

  • Introduction to DataProc
    • 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.
  • Security and Access Controls
    • Configuring access controls for DataProc clusters.
    • Implementing encryption for data at rest and in transit.
    • Managing security configurations for DataProc.
  • Scaling and Automation
    • Autoscaling DataProc clusters based on workload.
    • Using Dataprep or other tools for data preparation before processing.
    • Automation and scheduling of recurring jobs.

DataFlow (Apache Beam development)

  • Introduction to DataFlow
    • Overview of DataFlow as a fully managed stream and batch processing service.
    • Use cases for DataFlow in real-time analytics and ETL.
  • Building Data Pipelines with Apache Beam
    • Writing Apache Beam pipelines for batch and stream processing.
    • Transformations and windowing concepts.
    • Error handling and testing of DataFlow pipelines.
  • Monitoring and Optimization
    • Monitoring and troubleshooting DataFlow pipelines.
    • Optimizing pipeline performance and resource utilization.
    • Utilizing DataFlow templates for reusable pipelines.
  • 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.
  • Windowing and Watermarking
    • Understanding windowing concepts for stream processing.
    • Implementing watermarks for event time processing.
    • Handling late data and out-of-order events.
  • Security and Access Controls
    • Configuring access controls for DataFlow jobs.
    • Implementing encryption for data in transit and at rest.
    • Best practices for securing DataFlow 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.
  • Error Handling and Retry Policies
    • Configuring error handling mechanisms.
    • Implementing retry policies for fault-tolerant message processing.
  • 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.
  • Monitoring and Logging
    • Setting up monitoring and logging for Pub/Sub.
    • Analyzing metrics and logs to troubleshoot and optimize message processing.

Cloud Composer (DAG Creations)

  • Introduction to Composer
    • Overview of Composer as a fully managed workflow orchestration service.
    • 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.
  • Extending and Customizing Composer
    • Extending Apache Airflow with custom operators and sensors.
    • Creating and managing Composer plugins.
    • Versioning and managing workflow code.
  • Security and Access Controls
    • Configuring access controls for Composer environments.
    • Implementing encryption for data and workflow metadata.
    • Best practices for securing Composer workflows.
  • Error Handling and Troubleshooting
    • Handling errors and retries in Composer workflows.
    • Debugging and troubleshooting failed workflow executions.
    • Logging and monitoring for Composer workflows.

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.
    • Connecting to external databases, APIs, and data sources.
    • Real-time data integration and streaming support.
  • Versioning and Collaboration
    • Managing version control for Data Fusion pipelines.
    • Collaborating with team members on pipeline development.
    • Best practices for maintaining and updating pipelines.
  • Security and Access Controls
    • Configuring access controls for Data Fusion environments and pipelines.
    • Implementing encryption for data in transit and at rest.
    • Security considerations for handling sensitive data.
  • Monitoring and Optimization
    • Monitoring pipeline executions and job statuses.
    • Optimizing Data Fusion pipelines for performance.
    • Utilizing logs and metrics for troubleshooting.

Terraform

  • Terraform Basics
    • Installing and configuring Terraform.
    • Writing Terraform configurations using HashiCorp Configuration Language (HCL).
    • Initializing and applying Terraform configurations.
  • Infrastructure Provisioning
    • Creating and managing infrastructure resources using Terraform.
    • Terraform state and remote backends.
    • Importing existing infrastructure into Terraform.
  • Module and Provider Usage
    • Organizing Terraform configurations using modules.
    • Utilizing different providers for various cloud services.
    • Best practices for reusable and modular Terraform code.
  • Variables, Outputs, and Functions
    • Defining and using variables in Terraform.
    • Outputting values from Terraform configurations.
  • Terraform Workflow and Best Practices
    • Terraform workflows: plan, apply, and destroy.
    • Managing Terraform environments and workspaces.

GCP Data Engineering Projects

  1. Data Analysis in BigQuery using SQL.
  2. ETL case study with PySpark in Dataproc
  3. Processing Streaming Data with Pub/Sub and Dataflow
  4. Building Orchestration for Batch Data Loading Using Cloud Composer

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.

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.