This Data Engineering on Google course Cloud The 4-day course provides complete training for the design, construction and operation of data processing systems on Google Cloud Platform (GCP)Participants will work with major data engineering services such as BigQuery, Dataflow, Dataproc, Pub/Sub, Dataplex and Cloud Storage, exploring modern concepts of data ingestion, storage, transformation, analysis and orchestration.
The training includes demonstrations, hands-on labs, and real-world scenarios, guiding participants to build scalable pipelines, optimize performance, and implement robust data architectures in cloud.
Data Engineering Course on Google Cloud It is recommended for:
• data engineers who build or manage pipelines in cloud
• specialists Big Data migrating to the Google ecosystem Cloud
• database administrators and ETL specialists
• analytics engineers working with BigQuery
• professionals preparing for certification Data Engineer, within the official Google route Cloud
At the end of the Data Engineering on Google course Cloud, participants will be able to:
• design scalable data processing systems in GCP
• build batch and streaming pipelines using Google services Cloud
• implement data models and end-to-end flows
• operate data systems with a focus on reliability, security and cost
• apply best practices for automation, orchestration and optimization
- basic SQL knowledge
- recommended: experience in a programming language (Python, Java, etc.)
- understanding the fundamental concepts of cloud
Module 01 – Data engineering tasks and components
- the role of a data engineer
• data sources vs. data sinks
• data formats
• Google storage options Cloud
• metadata management
• sharing datasets with Analytics Hub
Laboratory: Loading Data into BigQuery
Module 02 – Data replication and migration
- replication and migration architecture
• gcloud CLI
• moving datasets
• Datastream and use cases
Laboratory: PostgreSQL → BigQuery Replication with Datastream
Module 03 – Extract & Load pipeline pattern (EL)
- EL architecture
• bq CLI
• BigQuery Data Transfer Service
• BigLake as an EL alternative
Laboratory: BigLake Qwik Start
Module 04 – Extract, Load & Transform pattern (ELT)
- ELT architecture
• SQL scripting and scheduling in BigQuery
• Dataform
Laboratory: Create & Execute SQL Workflow in Dataform
Module 05 – Extract, Transform & Load pattern (ETL)
- ETL architecture
• GUI tools in Google Cloud
• batch processing with Dataproc
• streaming processing – options
• Bigtable in data pipelines
Labs:
• Dataproc Serverless for Spark → Load BigQuery
• Dataflow Real-Time Dashboard Pipeline
Module 06 – Automation techniques
- automation patterns
• Cloud Scheduler and Workflows
• Cloud Compose
• Cloud Run Functions
• Eventarc
Laboratory: Cloud Run Functions → Load BigQuery
Module 07 – Introduction to Data Engineering
- the role of the data engineer
• data engineering challenges
• introduction to BigQuery
• data lakes vs. data warehouses
• governance, access and collaboration
• case study
Laboratory: Using BigQuery for Analysis
Module 08 – Build a Data Lake
- data lake architecture
• storage and ETL options
• Cloud Storage as the main data lake
• security Cloud Storage
• use Cloud SQL
Laboratory: Loading Taxi Data into Cloud SQL
Module 09 – Build a Data Warehouse
- modern data warehouse architecture
• BigQuery – concepts, data loading
• exploring schemes
• nested & repeated fields
• partitioning & clustering
Labs:
• JSON & Array Handling in BigQuery
• Partitioned Tables in BigQuery
Module 10 – Introduction to building batch pipelines
- EL / ELT / ETL
• data quality
• executing operations in BigQuery
Demo: ELT to Improve Data Quality
Module 11 – Execute Spark on Dataproc
- Hadoop ecosystem
• running workloads on Dataproc
• use Cloud Storage instead of HDFS
• Dataproc optimization
Laboratory: Running Spark Jobs on Dataproc
Module 12 – Serverless data processing with Dataflow
- introduction to Dataflow
• aggregations, side inputs, windowing
• SQL dataflow & templates
Labs:
• Simple Dataflow Pipeline
• MapReduce in Beam
• Side Inputs
Module 13 – Manage pipelines with Cloud Data Fusion & Cloud Compose
- creating visual pipelines with Data Fusion
• Wrangler – data exploration and transformation
• orchestration with Cloud Compose
• Airflow: DAGs, operators, workflows
Labs:
• Build & Execute Pipeline in Data Fusion
• Introduction to Cloud Compose
Module 14 – Introduction to streaming data processing
- streaming concepts
• GCP tools for streaming
Module 15 – Serverless messaging with Pub/Sub
- Pub/Sub push vs pull
• publishing by code
Laboratory: Publish Streaming Data into Pub/Sub
Module 16 – Dataflow streaming features
- streaming challenges
• windowing, latency, triggers
Laboratory: Streaming Data Pipelines
Module 17 – High-throughput BigQuery & Bigtable streaming
- streaming in BigQuery + dashboards
• high-throughput ingestion into Bigtable
• Bigtable optimization
Labs:
• Streaming Analytics & Dashboards
• Streaming into Bigtable
Module 18 – Advanced BigQuery functionality and performance
- analytical window functions
• GIS functions
• BigQuery optimization
Laboratory: Optimizing BigQuery Queries
Note: The agenda may be adjusted depending on the assigned trainer. For the final version, please contact the team Bittnet Training.
- Machine Learning on Google Cloud – 5 days
- Introduction to AI and Machine Learning on Google Cloud – 1 day
These courses extend the training of a Data Engineer to the fields of machine learning and AI, relevant for advanced projects based on BigQuery ML, Dataflow ML, and custom models.
The course is included in the official path for the Professional Data Engineer certification, representing the learning base recommended by Google. Cloud for this role.
Data Engineering on Google course FAQ Cloud
How does a "Data Engineering on Google" course contribute Cloud” to increasing ROI in an organization?
Training data engineers at Google Cloud optimizes the management of large data streams, automates processing, and extracts valuable insights. This reduces the cost of manual errors, accelerates the delivery of analytics projects, and supports data-driven business decisions, which translates into significant ROI through operational efficiency and faster strategic decisions.
Why is BigQuery training essential for data-driven companies?
BigQuery offers petabyte-scale storage and analytics at predictable costs. Companies that master BigQuery can transform large volumes of data into actionable insights without massive infrastructure investments, reducing TCO and maximizing the return on analytics investments.
How Google optimizes Cloud Pipelinedata and what impact does it have on costs?
Pipeline-data managed in Google Cloud enable automated and scalable data processing. This automation eliminates repetitive tasks and associated errors, reducing operating costs and development time, which brings direct financial benefits.
What role does Dataflow play in data transformation and integration and how does it impact ROI?
Dataflow offers unified processing for both streaming and batch, with automatic scaling. This reduces architectural complexity and the need for coordination between disparate tools, leading to lower operating costs and faster response times for data projects.
How the course supports the practical adoption of ETL/ELT at Google Cloud?
The course focuses on data extraction, transformation, and loading techniques using native tools like Dataflow and Dataprep, which enables teams to build cost-effective and scalable pipelines, eliminating data silos and increasing the value of data to the business.
How does it reduce Cloud Storage costs of large-scale data storage?
Cloud Storage offers durable, scalable storage and cost-per-use. This flexibility allows organizations to optimize budgets for inactive or infrequently accessed data through effective archiving policies, reducing TCO and maximizing resource utilization.
What benefits does Dataproc integration bring to Hadoop/Spark processing and how does it impact efficiency?
Dataproc enables Hadoop and Spark clusters to run on-demand with low cost and rapid scaling. This eliminates the need for ongoing cluster management and minimizes traditional infrastructure costs, providing significant savings and flexibility in your operations. big data.
How security and governance knowledge helps in data engineering at Google Cloud?
Implementing good security and governance practices ensures the protection of sensitive data and regulatory compliance. This reduces the risk of fines, reputational damage, and costly incidents, protecting revenue and ensuring business continuity in a cost-effective manner.
Why is the ability to optimize processing and storage costs important in data projects?
Optimizing processing and storage costs is essential for managing IT budgets. Well-prepared teams can implement optimization policies that reduce monthly expenses without compromising performance, which increases profitability and long-term ROI.
How can Data Engineering training at Google Cloud to accelerate innovation in the organization?
Data engineering skills enable organizations to strategically exploit data, generating predictive analytics, operational optimizations, and innovative digital products. This agility in generating competitive insights translates into increased revenue and sustainable competitive advantage.
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