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Data Engineering on Google Cloud Platform Training
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Drive Team Excellence with Data Engineering on Google Cloud Platform Corporate Training

Data Engineering on Google Cloud Platform Training equips individuals with skills to efficiently collect, store, process, and analyze data using GCP services, covering ingestion, storage, processing, pipeline design, optimization, governance, security, and monitoring. This training enables organizations to leverage Google Cloud services for enhancing decision-making processes, optimizing data workflows, and unlocking the full potential of their data assets to drive organizational growth and innovation.

Edstellar's Data Engineering on the Google Cloud Platform instructor-led training course can be customized to meet an organization's requirements. The virtual/onsite Data Engineering on the Google Cloud Platform training course, led by expert trainers, offers interactive sessions that combine hands-on learning experiences with theoretical knowledge ensuring professionals gain insights to tackle any objectives that arise.

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Skills Your Employees Will Gain

These are the core, hands-on capabilities your team builds during the program.

  • Data Pipeline Design
    Data Pipeline Design is the process of creating systems for efficiently moving and transforming data. This skill is important for data engineers and analysts to ensure accurate, timely insights.
  • Data Lake Architecture
    Data Lake Architecture involves designing and managing scalable storage systems for vast amounts of structured and unstructured data. This skill is important for data engineers and architects, as it enables efficient data processing, analytics, and decision-making in organizations.
  • Data Warehousing
    Data Warehousing is the process of collecting, storing, and managing large volumes of data for analysis. This skill is important for data analysts and business intelligence roles, as it enables informed decision-making through efficient data retrieval and reporting.
  • Batch Data Pipelines
    Batch Data Pipelines involve the automated processing of large data sets at scheduled intervals. This skill is important for data engineers and analysts to ensure efficient data management, timely insights, and informed decision-making.
  • Apache Spark
    Apache Spark is an open-source distributed computing system for big data processing. This skill is important for data engineers and data scientists to efficiently analyze large datasets, enabling faster insights and decision-making.
  • Serverless Data Processing
    Serverless Data Processing is the ability to manage and analyze data without server management. this skill is important for data engineers and analysts to enhance scalability, reduce costs, and streamline workflows.

What Your Team Will Achieve After This Training

  • Apply data engineering concepts to design robust data pipelines on Google Cloud Platform
  • Implement data lake architectures to efficiently store and manage large volumes of structured and unstructured data
  • Construct data warehouses for optimized storage and retrieval of business-critical information
  • Develop batch data pipelines using industry-standard tools and techniques
  • Execute Apache Spark jobs on Cloud Dataproc for scalable data processing
  • Implement serverless data processing solutions with Cloud Dataflow to handle real-time data streams
  • Manage data pipelines effectively using Cloud Data Fusion and Cloud Composer orchestration tools
  • Utilize advanced analytics and AI services to extract actionable insights from large datasets

Topics & Program Outline

The curriculum is organized into focused modules built by industry experts and delivered virtually or on-premise. Interactive sessions reflect the evolving demands of the workplace, keeping the learning both relevant and practical.

  1. Overview of data engineering
    • Introduction to data engineering concepts
    • Role of data engineers in organizations
  2. Data engineering lifecycle
    • Understanding the data lifecycle stages
    • Importance of data quality and governance
  1. Introduction to Data Lakes
    • Definition and architecture of data lakes
    • Benefits and use cases of data lakes
  2. Implementing Data Lakes
    • Designing data lake architecture
    • Ingesting and storing data in data lakes
  1. Data warehouse concepts
    • Understanding data warehousing principles
    • Data modeling for data warehousing
  2. Implementing data warehouses
    • Designing data warehouse architecture
    • Loading and transforming data in data warehouses
  1. Batch processing overview
    • Understanding batch processing concepts
    • Batch processing vs. real-time processing
  2. Building batch data pipelines
    • Designing batch data pipeline architecture
    • Implementing batch processing workflows
  1. Introduction to Apache Spark
    • Overview of Apache Spark framework
    • Benefits of using Apache Spark for data processing
  2. Spark on Cloud Dataproc
    • Setting up and configuring Spark on Google Cloud Dataproc
    • Running Spark jobs on Cloud Dataproc clusters
  1. Introduction to Cloud Dataflow
    • Overview of serverless data processing
    • Features and capabilities of Cloud Dataflow
  2. Building Data Pipelines with Cloud Dataflow
    • Writing and deploying Dataflow pipelines
    • Monitoring and optimizing Dataflow jobs
  1. Introduction to Cloud Data Fusion
    • Overview of Cloud Data Fusion service
    • Benefits of using Cloud Data Fusion for data integration
  2. Managing Data Pipelines with Cloud Composer
    • Orchestration and scheduling of data pipelines with Cloud Composer
    • Integration with other Google Cloud services
  1. Streaming data concepts
    • Understanding streaming data and event-driven architectures
    • Use cases for streaming data processing
  2. Implementing streaming data pipelines
    • Designing and building streaming data pipelines
    • Real-time data processing with Google Cloud services
  1. Introduction to Cloud Pub/Sub
    • Overview of messaging systems and Cloud Pub/Sub
    • Features and capabilities of Cloud Pub/Sub
  2. Implementing Serverless messaging
    • Creating and managing Pub/Sub topics and subscriptions
    • Integrating Pub/Sub with other Google Cloud services
  1. Advanced features of Cloud Dataflow
    • Windowing and watermarking in streaming data processing
    • Handling late data and out-of-order events
  2. Building streaming Data Pipelines with Dataflow
    • Implementing complex streaming data processing logic
    • Optimizing performance and cost of Dataflow pipelines
  1. Introduction to BigQuery and Bigtable
    • Overview of Google Cloud BigQuery and Bigtable services
    • Use cases for high-throughput streaming data processing
  2. Processing streaming data with BigQuery and Bigtable
    • Configuring and optimizing streaming data ingestion in BigQuery
    • Leveraging Bigtable for real-time data storage and retrieval
  1. Advanced BigQuery features
    • Working with nested and repeated data structures
    • Partitioning and clustering tables for improved performance
  2. Optimizing BigQuery performance
    • Query optimization techniques for faster query execution
    • Monitoring and troubleshooting BigQuery performance issues
  1. Overview of Analytics and AI
    • Introduction to analytics and AI capabilities on Google Cloud
    • Use cases for analytics and AI in data engineering workflows
  2. Integrating analytics and AI services
    • Using AI APIs for text and image analysis
    • Building custom ML models with Google Cloud AI Platform
  1. ML model APIs overview
    • Overview of prebuilt ML model APIs available on Google Cloud
    • Benefits of using prebuilt ML models for unstructured data
  2. Implementing ML Model APIs
    • Integrating ML model APIs into data engineering pipelines
    • Processing unstructured data with ML model APIs
  1. Introduction to AI platform notebooks
    • Overview of AI platform notebooks for collaborative data analysis
    • Features and capabilities of AI platform notebooks
  2. Performing Big Data analytics
    • Analyzing large datasets with AI platform notebooks
    • Visualizing and interpreting analytics results
  1. Introduction to Kubeflow
    • Overview of Kubeflow for building and deploying ML pipelines
    • Use cases for Kubeflow in production ML workflows
  2. Building ML Pipelines with Kubeflow
    • Designing end-to-end ML pipelines with Kubeflow components
    • Deploying and managing ML models in production environments
  1. Introduction to BigQuery ML
    • Overview of BigQuery ML for building ML models using SQL
    • Benefits of using BigQuery ML for model development
  2. Building custom ML models
    • Writing SQL queries to create and train ML models in BigQuery
    • Evaluating and deploying custom ML models for predictive analytics

Who Should Attend?

This program suits professionals at many levels across the organization, including:

  • Data Engineers
  • Cloud Engineers
  • Data Analysts
  • Software Developers
  • System Architects
  • Technical Support Engineers
  • IT Administrators
  • Database Administrators
  • DevOps Engineers
  • Site Reliability Engineers
  • Application Developers
  • Managers

What are the Prerequisites?

Professionals with a basic understanding of data engineering concepts and principles can take up the Data Engineering on Google Cloud Platform training course.

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Delivering Training for Organizations across 100 Countries and 10+ Languages

Choose the Format That Fits Your Team

We design training your teams actually engage with, and deliver it the way that suits you best. Through a vetted global trainer network, Edstellar runs sessions in 10+ languages with consistent quality anywhere.

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Virtual Data Engineering on Google Cloud Platform Training

Virtual / online: expert-led live sessions delivered anywhere, with consistency and easy scheduling.

We deliver anywhere worldwide
Standardized content for consistent outcomes
Join from own workspace, no travel
We scale to large groups across sites
Interactive tools keep remote learners engaged
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On-site Data Engineering on Google Cloud Platform Training

On-site (in-house): immersive, instructor-led learning at your office.

Our trainers run face-to-face at your office
We tailor setup/content to your workplace and tools
Group exercises drive collaboration
Live demos +  hands-on practice
Direct trainer access to clarify doubts
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Off-site Data Engineering on Google Cloud Platform Training

Off-site: focused, instructor-led group learning away from everyday workplace distractions.

We host your teams at a venue of your preferred choice
Built-in group activities for bonding
Full uninterrupted schedule for focus/retention
Boosts morale and signals commitment
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Need pricing for onsite, offsite, or virtual delivery? Get a proposal tailored to your team's needs.

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        What Sets Edstellar Apart

        Experienced Trainers

        Our trainers are drawn from a vetted global network and bring years of industry expertise, keeping every session practical and impactful.

        Proven Quality

        With a strong global track record, Edstellar is known for quality and engaging delivery.

        Industry-Relevant Curriculum

        Our programs are built by experts to match the demands of today's industry.

        Fully Customizable

        Every program can be tailored to your organization's goals.

        Comprehensive Support

        We provide pre- and post-session support for a complete learning experience.

        Global Multi-Location & Multilingual Training Delivery

        We deliver in multiple languages to support diverse global teams.

        Hear from Organizations We've Trained

        "The Data Engineering on Google Cloud Platform training exceeded my expectations in every way. As a Lead Data Scientist, I gained comprehensive knowledge of practical applications that transformed my practical and immediately applicable. I've successfully implemented these advanced techniques in production environments with measurable impact. The instructor's expertise in expert-led workshops made complex concepts crystal clear and actionable.”

        Darrin Palmer

        Lead Data Scientist,

        Cloud Security Platform Company

        "This Data Engineering on Google Cloud Platform course transformed my approach to professional expertise solutions. The comprehensive modules on hands-on exercises were invaluable for our professional services projects. I can now diverse client requirements. The deep coverage of practical simulations gave me advanced skills I immediately applied to This expertise enabled us to secure a transformative contract with a Fortune 100 organization.”

        Zhang Hua

        Lead Analytics Engineer,

        Enterprise Cloud Services Provider

        "The Data Engineering on Google Cloud Platform training transformed our team's entire approach to technical mastery management and execution. As a Lead Data Warehouse Engineer, the extensive coverage of industry best proven concepts to enhanced capabilities. Our team has automated eighteen critical business processes, reducing manual effort by 70%. Our team's productivity and solution quality have improved measurably, validating this investment.”

        Amr Basel

        Lead Data Warehouse Engineer,

        Cloud Computing Solutions Provider

        “Edstellar’s IT & Technical training programs have been instrumental in strengthening our engineering teams and building future-ready capabilities. The hands-on approach, practical cloud scenarios, and expert guidance helped our teams improve technical depth, problem-solving skills, and execution across multiple projects. We’re excited to extend more of these impactful programs to other business units.”

        Aditi Rao

        L&D Head,

        A Global Technology Company

        Recognition That Motivates Your Team

        Upon successful completion of the training course offered by Edstellar, employees receive a course completion certificate, symbolizing their dedication to ongoing learning and professional development.

        This certificate validates the employee's acquired skills and is a powerful motivator, inspiring them to enhance their expertise further and contribute effectively to organizational success.

        Recognition That Motivates Your Team

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