Data Engineering on Google Cloud Training in Kazakhstan

  • Learn via: Classroom / Virtual Classroom / Online
  • Duration: 4 Days
  • Level: Intermediate
  • Price: From €3,789+VAT
  • Upcoming Date:
  • UK & Türkiye Based Global Training Provider

Get hands-on experience designing and building data processing systems on Google Cloud. This course uses lectures, demos, and hands-on labs to show you how to design data processing systems, build end-to-end data pipelines, and analyze data. This course covers structured, unstructured, and streaming data.

Products:

  • BigQuery
  • Bigtable
  • Cloud Storage
  • Cloud SQL
  • Spanner
  • Dataproc
  • Dataflow
  • Cloud Data Fusion
  • Cloud Composer
  • Pub/Sub
We can organize this training at your preferred date and location. Contact Us!

Prerequisites

Participants should have:

  • Prior Google Cloud experience using Cloud Shell and accessing products from the Google Cloud console.
  • Basic proficiency with a common query language such as SQL.
  • Experience with data modeling and ETL (extract, transform, load) activities.
  • Experience developing applications using a common programming language such as Python

Target audience

This course is designed for:

  • Data engineers
  • Database administrators
  • System administrators

What You Will Learn

By the end of this course, learners will be able to:

  • Design and build data processing systems on Google Cloud.
  • Process batch and streaming data by implementing autoscaling data pipelines on Dataflow.
  • Derive business insights from extremely large datasets using BigQuery.
  • Leverage unstructured data using Spark and ML APIs on Dataproc.
  • Enable instant insights from streaming data.

Training Outline

Module 01: Data engineering tasks and components

  • The role of a data engineer
  • Data sources versus data syncs
  • Data formats
  • Storage solution options on Google Cloud
  • Metadata management options on Google Cloud
  • Share datasets using Analytics Hub

Module 02: Data replication and migration

  • Replication and migration architecture
  • The gcloud command line tool
  • Moving datasets
  • Datastream

Module 03: The extract and load data pipeline pattern

  • Extract and load architecture
  • The bq command line tool
  • BigQuery Data Transfer Service
  • BigLake

Module 04: The extract, load, and transform data pipeline pattern

  • Extract, load, and transform (ELT) architecture
  • SQL scripting and scheduling with BigQuery
  • Dataform

Module 05: The extract, transform, and load data pipeline pattern

  • Extract, transform, and load (ETL) architecture
  • Google Cloud GUI tools for ETL data pipelines
  • Batch data processing using Dataproc
  • Streaming data processing options
  • Bigtable and data pipelines

Module 06: Automation techniques

  • Automation patterns and options for pipelines
  • Cloud Scheduler and Workflows
  • Cloud Composer
  • Cloud Run functions
  • Eventarc

Module 07: Introduction to data engineering

  • Data engineer’s role
  • Data engineering challenges
  • Introduction to BigQuery
  • Data lakes and data warehouses
  • Transactional databases versus data warehouses
  • Effective partnership with other data teams
  • Management of data access and governance
  • Building of production-ready pipelines
  • Google Cloud customer case study

Module 08: Build a Data Lake

  • Introduction to data lakes
  • Data storage and ETL options on Google Cloud
  • Building of a data lake using Cloud Storage
  • Secure Cloud Storage
  • Store all sorts of data types
  • Cloud SQL as your OLTP system

Module 09: Build a data warehouse

  • The modern data warehouse
  • Introduction to BigQuery
  • Get started with BigQuery
  • Loading of data into BigQuery
  • Exploration of schemas
  • Schema design
  • Nested and repeated fields
  • Optimization with partitioning and clustering

Module 10: Introduction to building batch data pipelines

  • EL, ELT, ETL
  • Quality considerations
  • Ways of executing operations in BigQuery
  • Shortcomings
  • ETL to solve data quality issues

Module 11: Execute Spark on Dataproc

  • The Hadoop ecosystem
  • Run Hadoop on Dataproc
  • Cloud Storage instead of HDFS
  • Optimize Dataproc

Module 12: Serverless data processing with Dataflow

  • Introduction to Dataflow
  • Reasons why customers value Dataflow
  • Dataflow pipelines
  • Aggregating with GroupByKey and Combine
  • Side inputs and windows
  • Dataflow templates

Module 13: Manage data pipelines with Cloud Data Fusion and Cloud Composer

  • Build batch data pipelines visually with Cloud Data Fusion
  • Components
  • Overview
  • Building a pipeline
  • Exploring data using Wrangler
  • Orchestrate work between Google Cloud services with Cloud Composer
  • Apache Airflow environment
  • DAGs and operators
  • Workflow scheduling
  • Monitoring and logging

Module 14: Serverless messaging with Pub/Sub

  • Introduction to Pub/Sub
  • Pub/Sub push versus pull
  • Publishing with Pub/Sub code

Module 16: Dataflow streaming features

  • Streaming data challenges
  • Dataflow windowing

Module 17: High-throughput BigQuery and Bigtable streaming features

  • Streaming into BigQuery and visualizing results
  • High-throughput streaming with Bigtable
  • Optimizing Bigtable performance

Module 18: Advanced BigQuery functionality and performance

  • Analytic window functions
  • GIS functions
  • Performance considerations

Exams and assessments

There is no specific certification related to this course.

Hands-on learning

There are practical labs in this course.

Why Choose Bilginç IT Academy

At Bilginç IT Academy, we combine our strong presence in both the UK and Türkiye to deliver high-quality, practical training solutions for organizations worldwide.

International Presence with Local Expertise
With operations in the United Kingdom and Türkiye, we bring together global standards and local market understanding to deliver effective training experiences across regions.

Expert Instructors with Real-World Experience
Our courses are delivered by certified trainers with extensive industry experience, ensuring you gain practical knowledge that can be applied immediately.

Corporate-Focused Training Approach
We specialize in training corporate teams, tailoring our programs to meet your organization’s goals, technologies, and project requirements.

Flexible Training Delivery Worldwide
We offer classroom, virtual classroom, and onsite training options globally, tailored to your organization’s needs.

Hands-On, Practical Learning
Our training sessions include real-world scenarios, case studies, and interactive exercises to ensure lasting understanding and skill development.

Proven Track Record
With over 10 years of experience, we have successfully trained professionals from leading organizations across different industries and regions.


Contact us for more detail about our trainings and for all other enquiries!

Avaible Training Dates

Join our public courses in our Kazakhstan facilities. Private class trainings will be organized at the location of your preference, according to your schedule.

We can organize this training at your preferred date and location.
05 сәуір 2026 (4 Days)
Almaty, Astana, Shymkent
€3,789 +VAT
12 сәуір 2026 (4 Days)
Almaty, Astana, Shymkent
€3,789 +VAT
30 сәуір 2026 (4 Days)
Almaty, Astana, Shymkent
€3,789 +VAT
01 мамыр 2026 (4 Days)
Almaty, Astana, Shymkent
€3,789 +VAT
18 мамыр 2026 (4 Days)
Almaty, Astana, Shymkent
€3,789 +VAT
22 маусым 2026 (4 Days)
Almaty, Astana, Shymkent
€3,789 +VAT
25 маусым 2026 (4 Days)
Almaty, Astana, Shymkent
€3,789 +VAT
31 шілде 2026 (4 Days)
Almaty, Astana, Shymkent
€3,789 +VAT

Kazakhstan stands as the preeminent technological and financial powerhouse of Central Asia, with the dynamic cities of Almaty and Astana serving as global magnets for innovation. The country is home to the Astana Hub, an international tech startup center, and Nazarbayev University, both of which are at the forefront of pioneering research in Artificial Intelligence, Blockchain, and Big Data analytics. Kazakhstan has achieved worldwide recognition for its advancements in digital mining and financial technologies, supported by a national strategy that prioritizes high-quality IT education and continuous professional development. Our comprehensive training programs are strategically designed to empower professionals in Kazakhstan to master complex corporate systems and lead large-scale digital innovation processes. By bridging the gap between local talent and global industry standards, we ensure that the Kazakh workforce remains highly competitive in the rapidly evolving Eurasian digital economy.

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