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

Empower your teams with expert-led on-site/in-house or virtual/online Data Engineering on Google Cloud Platform Training through Edstellar, a premier Data Engineering on Google Cloud Platform training company for organizations globally. Our customized training program equips your employees with the skills, knowledge, and cutting-edge tools needed for success. Designed to meet your specific training needs, this Data Engineering on Google Cloud Platform group training program ensures your team is primed to drive your business goals. Transform your workforce into a beacon of productivity and efficiency.

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.

Key Skills Employees Gain from Data Engineering on Google Cloud Platform Training

Data Engineering on Google Cloud Platform skills corporate training will enable teams to effectively apply their learnings at work.

  • Data Pipeline Design
  • Data Lake Architecture
  • Data Warehousing
  • Batch Data Pipelines
  • Apache Spark
  • Serverless Data Processing

Key Learning Outcomes of Data Engineering on Google Cloud Platform Training Workshop

Edstellar’s Data Engineering on Google Cloud Platform group training will not only help your teams to acquire fundamental skills but also attain invaluable learning outcomes, enhancing their proficiency and enabling application of knowledge in a professional environment. By completing our Data Engineering on Google Cloud Platform workshop, teams will to master essential Data Engineering on Google Cloud Platform and also focus on introducing key concepts and principles related to Data Engineering on Google Cloud Platform at work.


Employees who complete Data Engineering on Google Cloud Platform training will be able to:

  • 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

Key Benefits of the Data Engineering on Google Cloud Platform Group Training

Attending our Data Engineering on Google Cloud Platform classes tailored for corporations offers numerous advantages. Through our Data Engineering on Google Cloud Platform group training classes, participants will gain confidence and comprehensive insights, enhance their skills, and gain a deeper understanding of Data Engineering on Google Cloud Platform.

  • Enables professionals to build custom machine learning models with SQL in BigQuery ML
  • Facilitates the creation of production ML pipelines using Kubeflow for seamless deployment
  • Develops required skills in professionals to execute batch and streaming data processing tasks
  • Equips the team with advanced data engineering techniques for building scalable data pipelines
  • Empowers professionals with the skills to design and implement data lakes and warehouses on GCP
  • Enhances the team's ability to process and analyze large-scale data with Cloud AI Platform Notebooks
  • Provides teams with the insights into leveraging serverless data processing solutions for efficient resource utilization
  • Instills ideas in professionals for effectively managing data pipelines using GCP services like Data Fusion and Composer

Topics and Outline of Data Engineering on Google Cloud Platform Training

Our virtual and on-premise Data Engineering on Google Cloud Platform training curriculum is divided into multiple modules designed by industry experts. This Data Engineering on Google Cloud Platform training for organizations provides an interactive learning experience focused on the dynamic demands of the field, making it 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

Target Audience for Data Engineering on Google Cloud Platform Training Course

The Data Engineering on Google Cloud Platform training program can also be taken by professionals at various levels in the organization.

  • 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

Prerequisites for Data Engineering on Google Cloud Platform Training

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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At Edstellar, we understand the importance of impactful and engaging training for employees. To ensure the training is more interactive, we offer Face-to-Face onsite/in-house or virtual/online for companies. This approach has proven to be effective, outcome-oriented, and produces a well-rounded training experience for your teams.

 Virtual trainig

Our virtual group training sessions bring expert-led, high-quality training to your teams anywhere, ensuring consistency and seamless integration into their schedules.

With global reach, your employees can get trained from various locations
The consistent training quality ensures uniform learning outcomes
Participants can attend training in their own space without the need for traveling
Organizations can scale learning by accommodating large groups of participants
Interactive tools can be used to enhance learning engagement
 On-site trainig

Edstellar's onsite group training delivers immersive and insightful learning experiences right in the comfort of your office.

Higher engagement and better learning experience through face-to-face interaction
Workplace environment can be tailored to learning requirements
Team collaboration and knowledge sharing improves training effectiveness
Demonstration of processes for hands-on learning and better understanding
Participants can get their doubts clarified and gain valuable insights through direct interaction
 Off-site trainig

Edstellar's off-site group training programs offer a unique opportunity for teams to immerse themselves in focused and dynamic learning environments away from their usual workplace distractions.

Distraction-free environment improves learning engagement
Team bonding can be improved through activities
Dedicated schedule for training away from office set up can improve learning effectiveness
Boosts employee morale and reflects organization's commitment to employee development

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Data Engineering on Google Cloud Platform Corporate Training

Elevate your team's performance with our customized Data Engineering on Google Cloud Platform training. Find transparent pricing options to match your training needs. Start maximizing your team's potential now.

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        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.

        Course Completion Certificate

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