Data Engineer - course 89,000 rub. from Otus, training 4 months, date November 30, 2023.
Miscellaneous / / December 03, 2023
What will this course give you?
- Understanding the key ways to integrate, process, and store big data
- Ability to work with Hadoop ecosystem components, distributed storage and cloud solutions
- Practical skills in developing data services, storefronts and applications
- Knowledge of the principles of organizing monitoring, orchestration, testing
The course is addressed developers, DBMS administrators and everyone who seeks to improve their professional level, master new tools and engage in interesting tasks in the field of working with data.
After studying Data Engineering, you will become a sought-after specialist who:
- deploys, adjusts and optimizes data processing tools
- adapts datasets for further work and analytics
- creates services that use the results of processing large amounts of data
- responsible for the data architecture in the company
Real Case Studies: examples of implementations, tool usage, performance optimization, problems, errors and applied results
Highly practical orientation:
During the course we will incrementally create a working product, solving applied problems
A holistic picture of the challenges and tasks of modern business, and the role of the Data Engineer in solving them
Demand among employers
40 employers are already ready to call course graduates for an interview
6
coursesData engineer at Wildberries, DE Junior course speaker. More than 7 years in IT
Graduate of Voronezh State University with honors. Currently a student at the HSE master's program "System and Software Engineering". Professional experience - 2 years of work as a Data Analyst and Data Engineer. Now he works with 5 popular databases, develops in Python and is rapidly developing his skills. Ready to share my experience.
1
wellHe has been developing analytics in the company for more than 10 years. Among the achievements: - Construction of our own end-to-end web analytics system; - Construction of an analytical warehouse based on MPP Vertica; - Organization of data processing based on Spark, Kafka, HDFS; -...
He has been developing analytics in the company for more than 10 years. Among the achievements: - Construction of our own end-to-end web analytics system; - Construction of an analytical warehouse based on MPP Vertica; - Organization of data processing based on Spark, Kafka, HDFS;- Building processes for working with data, including data quality;- Creation of several internal tools for working and structuring metadata (Data Catalog);- Construction of a corporate reporting system, including realtime; - For more than 5 years, he has been increasing Data Literacy within the company, conducting various trainings on working with data, tools, SQL; He also developed several analytics leaders who now work in large companies. The main focus is on understanding business problems when working with data and solving them.
1
wellHead of department, Sberbank 8 years of experience in industrial development, including the creation and maintenance of web applications both in large companies and in startups. 3 years of development of distributed systems for large government...
Head of department, Sberbank 8 years of experience in industrial development, including the creation and maintenance of web applications both in large companies and in startups. 3 years of development of distributed systems for large government customers. Implemented three projects from scratch, from prototype to ready for industrial use. Currently engaged in full-stack development for internal customers at the bank, solving problems related to data analysis and engineering. Experience in programming in Java, Scala, Python, Javascript. A wide range of professional interests, ranging from building distributed systems to predictive analytics and intent analysis. Education: Bachelor's degree from UrFU named after. B.N. Yeltsin “Information Technologies”.
Data Architecture
-Topic 1.Data Engineer. Tasks, skills, tools, market needs
-Topic 2.Architecture of analytical applications: basic components and principles
-Topic 3.On premises / Cloud solutions
-Topic 4. Pipeline automation and orchestration – 1
-Topic 5. Pipeline automation and orchestration – 2
Data Lake
-Topic 6. Distributed file systems. HDFS/S3
-Topic 7.SQL access to Hadoop. Apache Hive/Presto
-Topic 8. Data storage formats and their features
-Topic 9. Analysis of remote control for 1 case
-Topic 10.Message queues. Kafka overview.
-Topic 11.Downloading data from external systems
-Topic 12.Apache Spark – 1
-Topic 13.Apache Spark – 2
DWH
-Topic 14.Analytical DBMS. MPP databases
-Topic 15.DWH Modeling – 1. dbt basics
-Topic 16.DWH Modeling – 2. Data Vault 2.0
-Topic 17.DevOps practices in Analytical applications. CI+CD
-Topic 18. Analysis of remote control for case 2
-Topic 19.Data Quality. Data Quality Management
-Topic 20. Deployment of a BI solution
-Topic 21.Monitoring / Metadata
NoSQL/NewSQL
-Topic 22.NoSQL Storage. Wide-column and key-value
-Topic 23.NoSQL Storage. Document-oriented
-Theme 24.ELK
-Theme 25.ClickHouse
-Topic 26. Analysis of remote control for case 3
MLOps
-Topic 27.Organization and Packaging of code
-Topic 28.Docker and REST architecture
-Theme 29.MLFlow + DVC
-Topic 30. Deployment of models
-Topic 31. Analysis of remote control for case 4
-Topic 32. Analysis of remote control for case 5
Graduation project
-Topic 33. Selection of topic and organization of project work
-Topic 34.Consultation
-Topic 35.Protection