Data Warehouse Analyst - free course from Otus, training 5 months, date November 30, 2023.

Data 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.

More than 6 years of experience in the development of data warehouses, ELT pipelines, data analysis and visualization. Experience in the field of state security, creation and implementation of QCD LLC "Group of Companies "SBSV-Klyuchavto", currently developing QCD for the Delo group of companies I am confident that data is the second oil, a kind of property that you need to be able to manage and dispose of. The presence of organized data, its proper storage, use, sale, anonymization indicate a high level of digital maturity. Teacher

Alexandra has been working in the field of analytics and BI since 2019. By this time, she received a bachelor's degree in Software Engineering from St. Petersburg State University of Aviation Administration, and then a master's degree. The first steps in his career were taken at the American company Intermedia Cloud Communications as a junior data analyst, and by 2021 he managed to become the head of the analytics team. This whole year was devoted to a new cross-team project for international financial management on the Microsoft stack (MS SQL Server, SSRS, SSIS, Power BI). Since March 2022, he has been working in the Tinkoff Bank group of companies as a warehouse analyst data. Provides support to top management of the financial department in building prototypes of ETL processes using Greenplum, ad-hoc analytics in Python, reporting and visualization in Tableau. In 2020, she received additional education in the direction of Project Management Manager in IT. He is a staunch supporter of flexible development methodologies. Believes that the most profitable investments are investments in one's own development. Stack: SQL, SAS DIS, SSIS, Tableau, Power BI, Python

ELT: Structure and types of data sources
-Topic 1. Data sources: classification and features
-Topic 2.Tools for downloading data – 1
-Topic 3.Tools for downloading data – 2

DWH Basics
-Topic 4. Analytical engines (DBMS) for working with data
-Topic 5.Principles of DWH construction
-Topic 6.DZ analysis – Uploading web counter data
-Topic 7.Introduction to Data Build Tool
-Topic 8.DBT: Analytics Engineering

DWH Intermediate
-Topic 9.Orchestration of scripts and tasks – 1
-Topic 10. Orchestration of scripts and tasks – 2
-Topic 11.DZ analysis – Configuring and launching the dbt project
-Topic 12.Data Quality
-Topic 13. Performance optimization issues
-Topic 14.Data Vault – 1
-Topic 15.Data Vault – 2
-Topic 16.DZ analysis – Preparing and setting up a DAG schedule for downloading data from sources

Business Intelligence
-Topic 17.BI: Overview
-Topic 18.BI: Deployment
-Topic 19.BI: Modeling & Delivering
-Topic 20.DZ analysis – Organization of a detailed DWH layer using the Data Vault methodology
-Topic 21.Analytics: Basic analytical showcases
-Topic 22.BI: In-Depth Questions
-Topic 23. DZ Razor – Configuration and deployment of a BI solution
-Topic 24.Analytics: Advanced analytics showcases

DWH Advanced topics
-Topic 25.DWH: Advanced topics
-Topic 26.DBT: Extending with modules
-Topic 27.DWH: Monitoring + Workload management
-Topic 28.DZ analysis – Visualization and dashboarding for analytical showcases
-Topic 29.DWH: External + Semi-structured data
-Topic 30.DWH: Reverse-ETL
-Topic 31.DWH: Machine Learning capabilities

Recap
-Topic 32. Case analysis: end-to-end solution
-Topic 33.DZ analysis – Advanced DWH: Configuring CI, dbt modules, External tables
-Topic 34. Further development of skills

Project work
-Topic 35. Selection of topic and organization of project work
-Topic 36.Protection of design work