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Batch Date: Aug 8th @8:00PM

Faculty: Mr. Raghavender (15+ Yrs of Exp,.. & Real Time Expert)

Duration: 35 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/Big Query

Course Content:

• Introduction to Google Cloud Platform (GCP)
• Brief overview of cloud computing and its benefits.
• GCP Core Services: cloud computing services
• Overview of essential GCP services
  • Compute Engine
  • Kubernetes Engine
  • App Engine
  • Cloud Functions
• Explanation of GCP storage and database services:
  Object storage service: Cloud Storage
  Block storage service: Persistent Disk
  Relational Databases:
  • Cloud SQL
  • Cloud Spanner
• NoSQL database
  • Firestore
  • Bigtable
  • Memory Store
• Steps to get started with GCP:
  • Creating an account
  • Accessing the Console
• Overview of networking services:
  • Virtual Private Cloud (VPC)
  • Cloud Load Balancing
  • Cloud DNS
• Introduction to analytics and big data services:
  • BigQuery
  • Dataflow
  • Dataproc
  • Data fusion
  • Data prep
  • Looker
  • Pub/Sub
  • Cloud run
  • Cloud Composer
• Introduction of machine learning and AI services:
  • AI Platform
  • AutoML
  • Vision AI
• Introduction of Management and Monitoring
  • Cloud Console
  • IAM
  • Cloud Monitoring
• Introduction of security and identity services:
  • Cloud IAM
• Google Big Query Introduction
  • Explanation of key features: Basics and Architecture
  • Big Query sandbox environment and account creation.
  • Scalability
  • SQL-like querying
• Data storage and processing separation
  • Explore Big Query resources
  • Big Query roles and resources
  • Datatype reference
• Advantages of using BigQuery for data analysis and processing:
  • Fast and efficient querying
  • No infrastructure management
  • Integration with other GCP services
• Overview of BigQuery's data structure:
  • Datasets
  • Tables
  • Views
  • List Datasets and work with Tables
  • External Tables
  • Views and Authorised views
  • Materialized Views
• Google Big Query Data Manipulation Language
  • Basics of DML
  • ETL, EL, and ELT
  • INSERT: Working with Tables/Columns in BQ
  • UPDATE: Working with Tables/Columns in BQ
• Loading Data into BigQuery
  • How to load data into BigQuery:
  • Batch loading
  • Streaming data
• Explaining how to perform queries in BigQuery:
  • Using SQL-like syntax
  • Aggregations and filtering
  • Visualizing BigQuery data using tools like Data Studio.
  • Integration with other GCP services for comprehensive analysis.
  • Partitioning and clustering
  • Choosing appropriate storage options
• Google Big Query Usecases: Load data from Big Query
  • Storing data : Working with Cloud Storage, buckets
  • Importing data from File using Big Query Web User Interface
  • Importing data from Google Drive
  • Batch Loads with data pipelines
  • Streaming Loads with data pipelines
• Google Big Query: Read data from Big Query storage
  • Big Query Connections
  • ELT with BQ with dbt
  • Working with Tables/Columns in BQ
• Data Build Tool: (Demo Only)
  • Building and maintaining data pipelines
  • Standardizing data transformation processes
  • Prerequisites, Configurations and connections
  • Source, Models, Tests
  • Create Models, Run and documentation
  • Scheduling
• Airflow: (Demo Only)
  • Working with DAG’s, creation of DAGS
  • Scheduling
  • Creating DAG groups
• GIT: (Demo Only)
  • Versioning and source code control
  • Code management
  • Pull, Push and merge code