Advanced hands-on training on Big Data engineering & powerful data collection and warehousing, led by industry’s best professionals.
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Our Data Engineering Bootcamp is an intensive hands-on training course tailor-made for dedicated individuals who want to become successful Data Engineers. Whether you're looking to upskill or transition into a new career, we equip you with the most in-demand data engineer skills companies are looking for, enabling you to launch your career in this exciting field.
Master essential technologies and concepts including Python, PySpark, MongoDB, ETL/ELT processes, Azure Databricks, DevOps, and cloud computing. Build data warehouses, handle Big Data, and be able to shape and understand the business purpose behind every data-driven decision.
Upon completion, you'll be prepared for roles such as Data Engineer, Cloud Engineer, ETL Developer, etc.
Great bootcamp to enter the Data Engineering field in Greece
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Stavros Kalimeris
Designed and taught by experts of the data industry
Project-based curriculum with real-world challenges
Final project to learn how a data professional works, collaborates and communicates their results
Familiarity with important technical and soft skills
Structured GitHub profile and strong portfolio to showcase to recruiters
Career coaching and Job assistance
Hiring Network
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25% theory - 75% practice
During each day we spend max 25% of the time for theory (lecture, live coding, code reviews) in order to get you as fast as possible to the exercises, where the real learning and fun begins!
Real-world knowledge
Our instructors are professionals in the industry. With their real-world experience, they will help you learn practical and soft skills and build a strong foundation to begin your data science career.
In our bootcamp, you will bridge the gap between traditional ETL pipelines and the modern stack. You will master Advanced SQL and leverage industry-standard tools like Databricks, dbt, and Azure cloud to handle Data Modeling, ELT, Data Ingestion, and Big Data Manipulation.
Through a modular curriculum, you will progressively build your skills, culminating in a final capstone project. You don't just learn the theory, you apply it to real-world scenarios, giving you the confidence to build the data architecture your future team will rely on!
Get the curriculumLearn Advanced Python for Data Science essential programming to handle complex JSON file structures.
Become proficient with GitLab and GitHub to organize code and work in teams.
Automate the tasks and navigate and manipulate file systems.
Tools that will be used:
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Use advanced Python libraries, like Pandas, to manipulate and explore datasets, and libraries, like matplotlib, to effectively visualize them to stories.
Understand and manage databases and write complex SQL queries to manipulate data.
Tools that will be used:
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Advanced Data Acquisition Techniques / Datastores & Storage
Learn to make advanced optimized queries in SQL and how to write Python scripts for process automation so you can access data via APIs and web scraping. Use real data engineering use cases and learn how to collaborate effectively in a Data Science team.
Data Engineering Fundamentals
Understand the core principles of Data Engineering by exploring essential concepts, tools, and methodologies.
Data Warehousing, Storage, Compute, Data Lakes
Modern Transformation with dbt (data build tool): Modern Transformation with dbt (data build tool): Master dbt to build data models using modular SQL directly in your warehouse. Automate data testing and documentation to ensure trust and quality in your pipelines.
Stacks: Classic, Hot-Cold, Data Lake.
Governance & Quality: Data quality, Data licenses, Data modeling.
Observability: Data lineage, metadata, testing, monitoring.
Tools that will be used:
Orchestrate and automate workflows, create data pipelines and manage data integration in cloud, using Microsoft Azure.
Build microservices and create, manage, and deploy Docker containers efficiently to the cloud.
Master data processing techniques using Python for Big Data.
Unlock the power of the Lakehouse. Bridge the gap between Data Lakes and Warehouses to manage Big Data infrastructure and run optimized Spark workloads efficiently.
Tools that will be used:
Modern data engineering workflows and analytics, like how to connect to remote linux servers and set up applications.
Design, implement and maintain automated workflows for continuous integration and deployment in data engineering projects using CI/CD pipelines. Orchestrate complex data workflows and ETL/ELT processes using Apache Airflow.
Create dashboards for reporting with Metabase, connect various data sources and make beautiful interactive dashboards.
Streamline your data integration by leveraging Airbyte's extensive library of pre-built connectors. Automate the "Extract" and "Load" phases of your ELT pipelines to efficiently move data from any source to your warehouse without maintaining custom scripts.
The tools that will be used:
Build end-to-end Data Engineering pipelines in the Cloud
After learning all the modern tools, services, and skills, you will work on a complex Data Engineering project and present your work. Work within a team, applying everything you have learned in practice on a real-wold use case to be well-prepated to work as a Data Engineer.
From defining the business problem to communicating the findings.
16 weeks
320 hours
Basic knowledge
in Python
Companies Trusting Us
Projects
Successful Graduates
Teaching Hours
After completing your training, you’ll be confident to apply for various data roles, depending on your prior experience and field.
Workshops on creating a standout CV and LinkedIn profile, mock technical interviews, and support throughout the hiring process.
We link you with industry professionals and companies, expanding your network and supporting your career goals.
Tuition fees are payable in up to 4 installments. For more details about payment have a look at our FAQ.
Special discounts for companies and groups of individuals (2+ participants), contact us.
Fill in and submit your application. Tell us about yourself and your motivation to enroll.
We reach out to schedule an interview with a member of our team. We'll discuss your technical background and career goals, and provide details about the course.
We will invite you to a second interview with one of our Data Scientists to assess your programming skills and critical thinking through a discussion.
You can prepare yourself by watching the Introduction to Python for Data Science course on YouTube.
You're ready to embark on your Data Science journey. You'll receive the Prep-Work exercises to work on before your first day of the Bootcamp.
Work on a real-world data engineering project, present it to an audience and meet prospective employers. Build a job-ready portfolio to show to recruiters.
What is Data Engineering?
Data Engineering refers to the practice of building systems and processes that allow effective collection and analysis of data, for Data Scientists and Data Analysts to process and present.
Many organizations rely heavily on how easily everyone has access to data. As the data grows and becomes more complex in structure, Data Engineering is one of the most important disciplines inside an organization as it helps to organize, transform and serve data effectively to people inside and outside the organization. It forms the foundation for data-driven insights, machine learning, and artificial intelligence applications, empowering businesses to extract value from their data assets and gain a competitive edge.
How can I learn Data Engineering?
If you want to learn Data Engineering you can choose between a Data Engineering Bootcamp (like BigBlue’s), Data Engineering Courses, or a master's in Data Engineering.
In order to get all the necessary knowledge, skills and practical experience you need to start immediately your professional career, the Data Engineering Bootcamp is the right for you.
What's the difference between a Data Engineer and a Data Scientist?
The role of a Data Scientist is to process and analyze data that will give solutions to business problems using Statistics, Machine Learning, and AI, while a Data Engineer develops systems and data architectures to make data available and easily accessible which later the Data Scientist will use for their analysis.
What are the most important Data Engineering skills to have?
Some of the most important Data Engineering skills that our Bootcamp will provide you with are:
Launch your career as a Data Engineer
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