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Data Engineering
Big Data Integration & ETL

In the age of big data and data-driven AI, many companies start to realize the importance of establishing data engineering best practices. As a result, the demand for data engineering has been growing rapidly. Currently there is a huge supply and demand mismatch in the talent market. One reason for the imbalance is that modern data engineering requires new tools/skills and traditional learning environments such as universities, colleges, and bootcamps don’t keep up with the trends. Another reason is that data engineering is hard to teach! Curriculums need to be extremely hands-on, and it requires very seasoned instructors who work in the fields to teach in the most practical way.

At a Glance
Advanced
What you will learn

In this 12-week part-time course you will learn how to build complex data pipelines for various real-world use cases. The core skills you will develop in this course include: Docker and Kubernetes, Spark, Spark Streaming, Delta Lake, Data Lakehouse, Data Pipelines, Data Ingestioon, Data Integration, Data Governance.

Summary
Learn the hottest tools
Spark, Kubernetes, Airflow, AWS, GCP
Learn from the best instructors
Learn to think like a data engineer
Build your first data engineer portfolio project
Learn and practice through projects
Get coding interview ready
Weekly Leetcode practice and solution sharing

Part-time

Online Live

12 weeks

About the Program

Data engineers are usually harder to train and source because the program needs to be very practical/hands-on and there is not much theory to teach. The open-source communities are also pushing out new tools and platforms on a regular basis which makes teaching data engineering challenging because materials need to be updated rapidly to keep up with the latest trends. At WeCloudData, we have heard from many hiring managers and recruiting agencies say that while the demand for data engineers is great, data engineer talents are even harder to find compared to data scientists.

The Big Data Integration & ETL course is a sequel to the Data Engineering Fundamentals course. In 12 weeks, you will learn how to apply data engineering skills to solve data migration, integration, real-time streaming data processing, as well as data governance, and big data. Completing this course will give you the skills and confidence you need to get a modern data engineer job.

for those who want to
  • Data Engineers from traditional ETL background who want to acquire modern DE skills
  • Developers and Software Engineers who are interested in building data applications
  • IT Professionals who want to explore new paths
  • Data Scientists who want to switch to the engineering world

Speak to our advisor

Our Program Advisor can answer all your questions and help you pick a program that best suits your need. Please fill in your information below and we will contact you.

You can also contact us at info@weclouddata.com or (647) 588-4206

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What you will learn

Most online courses will teach specific tools. But knowing 20+ tools doesn't mean you can solve real data problems. We strongly believe that problem-solving skills are the most essential for a successful career in data engineering. Therefore, we've designed a very unique approach to teaching learners how to solve tough problems and become independent thinkers.
  • Build a strong foundation in DE concepts and know the right questions to ask
  • Become a Google/Stackoverflow pro. Know the most relevant keywords to use for problem solving
  • Develop new skills in how to approach new projects and problems
  • Learn modern tools and platforms and know how to put them to work together
  • Become a better team player through group assignments, projects, and client projects
While docker and kubernetes are containerization and orchestration tools that DevOps engineers and infrastructure engineers will work on, data engineers also need to have a good understanding of it. Whether it's setting up airflow servers using docker compose, or running spark jobs on kubernetes, docker is an essential tool that data engineers need to be comfortable with.
  • Learn how docker containers work
  • Know how to deploy Flask apps using docker and docker compose
  • Understand the basics of kubernetes and container orchestration
  • Build hands-on experience with deploying airflow and submit airflow jobs using docker on Kubernetes
Cloud computing plays a more and more important role in current days. Applications and services are typically run on servers, which are comprised of CPU-processor, RAM-memory and storage-HDD, SSD. Instead of owning and provisioning servers in the on-prem data center, you could RENT the compute power and move it into the cloud. This means cloud computing is an on-demand delivery of computing power, database storage, applications and other IT resources through cloud services platforms via the internet. Cloud providers(Amazon Web Services-AWS, Google Cloud Platform(GCP), Microsoft Azure) provide rapid access to flexible and low-cost IT resources.
  • Work with several data engineering services on AWS and GCP
  • Hands-on experience with setting up an end-to-end pipeline across multiple services on AWS and GCP
  • Learn how to deploy pipelines in the cloud
Apache Airflow is an open-source workflow management platform to programmatically author, schedule, and monitor workflows. It is one of the most robust platforms used by Data Engineers for orchestrating workflows or pipelines. You can easily visualize your data pipelines' dependencies, progress, logs, code, trigger tasks, and success status.
  • Become an advanced Apache Airflow user
  • Hands-on experience with automating your data pipeline using Apache Airflow
  • Develop your ability to maintain and trouble-shoot an Airflow pipeline
Data Ingestion is the first step of a big data processing system and it is essential for the Data ETL pipeline. The data ingestion layer is the backbone of any analytics architecture. Downstream reporting and analytics systems rely on consistent and accessible data. As a Data Engineer, Apache NiFi, Apache Kafka, AWS Kinesis are important and commonly used tools for Data Ingestion.
  • Learn different Data Ingestion tools
  • Ability to process data from different sources using the Data Ingestion tools
  • Hands-on experiences with connecting Data Ingestion tools with the downstream tasks
What makes data engineering both exciting and challenging in recent years is the shift from the traditional data warehouse to the data lake and even the new data lakehouse approach. In the data warehouse part of the program, we will cover the fundamental concepts of data modelling, star schema, and snowflake schema. Students will learn how to create ER diagrams and implement their own data warehouse. Modern tools and technologies such as BigQuery, Redshift, and Snowflake will be covered as hands-on labs so students get to experience how it works in practice. We will also cover different scenarios where each approach is a better fit for the problem.
  • Learn the basics of NoSQL databases
  • Understand the key features of NoSQL databases
  • Learn how to work with NoSQL databases

Resources

Watch our recorded webinar and learn more about Data Science career and industry insights.
Data Engineering Program Info Session
Data Engineer Career Panel Discussion
Meet Your Faculty: Zheng Xu
Meet Your Faculty: David Tian

Instructors & Guest Speakers

Online Learning Platform

Learn anywhere, anytime

Track your learning journey
Watch lecture recordings, work on coding challenges, ask for TA help, and get resume and job support. The learning portal allows you to track your entire learning journey with ease.
Sharpen your coding skills
Leverage our online coding tool to test your knowledge, identify your weaknesses, and improve your Python and SQL coding skills. The LeetCode style live coding challenges will help you get prepared for technical job interviews.

DATA ENGINEERING CAPSTONE PROJECT

Build portfolio projects that impress hiring managers
Learn how to build end-to-end data engineering pipelines and showcase that to hiring managers. Your resume will shine with the projects you work on in this course.
What you will work on
  • Build complex data ingestion pipelines on AWS/GCP
  • Implement Change Data Capture (CDC) and message brokers
  • Implement data lake and Delta Lake using Apache Hudi and Kafka
  • Streaming data analytics using Spark Streaming and Flink
  • Apply data quality assurance best practices
  • Build reusable and testable pipeline code
STUDENT PROJECT DEMO
WeCloudData Data Engineer Student Project Demo 5
WeCloudData Data Engineer Student Project Demo 4

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Frequently Asked Questions

WeCloudData is the only bootcamp and accredited career college that focuses on the data field in Canada. We offer five distinctive programs - Data Science, Data Analytics, Data Engineering, Artificial Intelligence, and Cloud Computing to prepare you for your dream career. The programs are instructor-led and project-based. You will learn advanced data techniques at a fraction of the cost from other institutes.
Our programs cover the most up-to-date skills that are required by employers across Canada. In addition to offering personal training, we also work with a wide range of well-known data-driven companies in North America to help them up-skill their employees for cutting-edge technologies. That being said, we know what qualifications employers are looking for in future candidates.
You can communicate with the instructors and teaching assistants (TA's) regularly through our online platform and communication app (Slack). We also provide one-on-one support by our professional TA's.
Every live-online lecture is recorded so you can watch them anytime. You can always book a one-on-one meeting with our TA’s to help you catch up with the session you missed.
WeCloudData is registered as a private career college under the Ontario Ministry of Colleges and Universities with the operating name of “Toronto Institute of Data Science and Technology”. As an Ontario registered private career college, you can apply for a student line of credit from BMO with a lower interest rate. We also work with PayBright (https://paybright.com) to offer payment options through installments.

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Data Engineer Client Project (Career Mentorship)

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