AWS Cloud Data Engineering VideoCamp
Two different trends have emerged in recent years.
First; cloud usage has been increasing worldwide due to its many different advantages. In the cloud market, Amazon Web Services (AWS), the first and pioneer of cloud service providers, is still the market leader by far.
The second; is the continuous increase in the amount and importance of data. Here, too, the role of Data Engineer has great responsibilities and is therefore in high demand. Now, the industry is well aware that without a well-functioning data engineering infrastructure healthy ML/AI/BI/Analysis products are impossible.
Considering these two important trends, we have prepared incredible content for you to learn about the most popular profession in the data world on the world’s number one cloud. We are thrilled to have you embark on the AWS Cloud Data Engineering journey with us.
So what does a data engineer do? A data engineer designs and builds systems that collect, transform, and appropriately deliver data that business units or customers need.
For your questions and detailed information: satis@veribilimiokulu.com
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VideoCamp
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Bootcamp İçeriği
Tüm eğitimlerimiz, konunun tüm yönlerini ele alarak kapsamlı bir şekilde hazırlanmıştır.
Summary
As a data engineer candidate, you are at the right place if you want to build and serve something useful, see that the data coming out of the data pipelines you have designed and installed is consumed by internal or external customers, and enjoy it. Because AWS Cloud Data Engineering Bootcamp is designed for you to acquire the most demanded skills with up-to-date content prepared by an instructor with extensive industry experience in Turkey, Europe, and America.
This course will help you to get AWS Certified Data Analytics – Specialty Certification which is highly recognized by employers.
The Cloud Data Engineering course consists of 3 stages;
1. Preparation: Starts 4 weeks before the live training, 2 weeks
At this stage, training that will facilitate the understanding of online lessons is taken through videos with the support and guidance of mentors and trainers. For example, if docker-compose or SQL join will be used in the live lesson, it is also aimed not to waste time on what they mean.
2. AWS Cloud Data Engineering VideoCamp, 16 chapter.
It is the main part of the training. All lessons have been prepared by selecting from previous live AWS Cloud Data Engineering Bootcamp recordings.
VideoCamp consists of 16 separate sections. Each section has its own downloadable code, script and presentations.
You can follow the training according to your own work speed. You can get help in a very short time when you get stuck. We have a Discord channel to get help. You can contact mentors (instructor assistants) through this channel and send your questions.
In this course, we will find answers to the following questions.
- What’s in the world of data engineering? How do we become good data engineers?
- What are file formats and compression algorithms? Where and how do we use which one?
- How to execute distributed queries over many different data sources?
- How to prepare our PC to work with AWS?
- How to create an AWS EC2 virtual machine? How do we set the security settings?
- How do we write data to AWS S3 with Python? How do we read?
- What are the data engineering use cases on AWS?
- How to query datasets on S3 with Athena?
- How to set up Amazon EMR Cluster? How to submit the Spark applications on EMR?
- What services are available for streaming applications? How do we use it?
- How to create charts and dashboards?
- How to clean and transform data with AWS Lambda?
- Many other exciting topics.
Important notes:
AWS Cloud Data Engineering VideoCamp can be made from previous AWS Cloud Data Engineering Bootcamp live class recordings.
You will have access to training for 1 year.
Resources to be shared with students: Virtual machine, presentations and sample codes used in lectures.
Cloud: Cloud costs belong to the rights of the courses used by Cloud. A very high cloud cost is not expected (Max 20 USD)
Eğitmen
Eğitmenlerimiz yılların vermiş olduğu tecrübeyle sizlere bilgi aktarımı sağlamaktadır.
Erkan Şirin
Senior Data/ML Engineer, AWS Solution Architect
Verdiği Eğitimler
Mentörler
Any computer connected to the internet and running docker is sufficient.
- Linux Basic, Bash Script (preparation stage)
- Postgresql & SQL Basics (preparation stage)
- Docker (preparation stage)
- AWS Account Setup (preparation stage)
1. AWS Cloud Basics
– Cloud and AWS Intro
– AWS Account
– IAM Basic
– AWS CLI, SDK, Cloudshell
– Amazon EC2
– Amazon S3
2. Introduction to Data Engineering
– Fundamentals of Data Engineering
– Data related roles
– Benefits of Cloud in terms of Data Engineering
3. Data Management Architectures for Analytics
– RDBMS, DWH, Data Mart, Data Lake, Lakehouse
– Dimensional Modelling in Warehouse
– Columnar storage formats
– ETL
– Data Lake architecture
– Lakehouse on AWS
– CLI setup and basic operations
4. Data Ingestion
– Database Migration
– Apache kafka
– Kinesis (Firehouse, Agent and DataStreams)
5. Data transformation
– AWS Lambda
– Apache Spark
– AWS Glue
– Amazon EMR
– Apache Flink
– Amazon Kinesis Data Analytics
5. Workflow Orchestration
– Architecting Data Engineering Pipelines
– AWS Glue Workflows
– AWS Step Functions
– Amazon Airflow
6. Data Serving and Consuming
– Amazon Athena
– Amazon Redshift
– Amazon QuickSight
7. Data Management and Governance
– Catalog
– Security
8. Provisioning Infrastructure
– Infrastructure as Code (IaC)
– Terraform
– Cloud Formation
It is for those who are in the roles of Data Engineer, ML Engineer and Data Scientist in order of priority and candidates for these roles. Team leaders who have these roles in their team can also follow.
In addition, if you want to find your direction in the sector and decide where you want to go in a healthier way, we think that these issues will become clear after this training.
So, can I follow this program?
If you know basic Python programming and SQL, you can follow this course if you are familiar with basic computer science concepts.
Although it is possible to spread your learning process over a year, the recommended pace is as follows.
Preliminary preparation phase: 2-4 weeks
AWS Cloud Data Engineering VideCamp 14 weeks
Final projects 2 weeks
The above periods are assumed to be at least four hours of work per day.
- Basic Docker knowledge (available in the preparation stage)
- Basic SQL and database knowledge (available in the preparation stage)
- Bootcamp is given online / remotely by an experienced trainer and team.
- During the bootcamp, you will learn professional data engineering at a level that you can easily use in business life.
- You will experience a real-life process with content that is not available in any online or classroom training in Turkish and is specific to bootcamp participants only.
- You will have the opportunity to learn together and benefit from the power of the community.
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