What_Is_Data_Governance_Understanding_the_Business_Impact_by_Andy.epub
2.3 MB
What Is Data Governance? Understanding the Business Impact
Сравнение открытых OLAP-систем Big Data: ClickHouse, Druid и Pinot / Блог компании Конференции Олега Бунина (Онтико) / Хабр
https://habr.com/ru/company/oleg-bunin/blog/351308/
https://habr.com/ru/company/oleg-bunin/blog/351308/
Хабр
Сравнение открытых OLAP-систем Big Data: ClickHouse, Druid и Pinot
ClickHouse, Druid и Pinot — три открытых хранилища данных, которые позволяют выполнять аналитические запросы на больших объемах данных с интерактивными задержкам...
How Airbnb Achieved Metric Consistency at Scale | by Robert Chang | Airbnb Engineering & Data Science | Apr, 2021 | Medium
https://medium.com/airbnb-engineering/how-airbnb-achieved-metric-consistency-at-scale-f23cc53dea70
https://medium.com/airbnb-engineering/how-airbnb-achieved-metric-consistency-at-scale-f23cc53dea70
Medium
How Airbnb Achieved Metric Consistency at Scale
Part-I: Introducing Minerva — Airbnb’s Metric Platform
Building Power BI custom visuals with React and D3.js | Pt. One | Welcome, Developer
https://www.welcomedeveloper.com/building-power-bi-custom-visuals-with-react-and-d-3-js-pt-one
https://www.welcomedeveloper.com/building-power-bi-custom-visuals-with-react-and-d-3-js-pt-one
Peritos_Solutions_Case_Study_Power_BI_Custom_Visualization_v1_0.pdf
367.1 KB
PeritosSolutionsCase-Study-Power-BI-Custom-Visualization-v1.0.pdf
Docker_на_практике_by_Иан_Милл,_Эйдан_Хобсон_Сейерс.pdf
8.8 MB
Docker на практике by Иан Милл, Эйдан Хобсон Сейерс.pdf
Простая идея Docker – упаковка приложения и его зависимостей в единый развертываемый контейнер – породило ажиотаж в индустрии программного обеспечения. Теперь контейнеры являются крайне необходимыми для корпоративной инфраструктуры, а Docker представляет собой бесспорный отраслевой стандарт.
Простая идея Docker – упаковка приложения и его зависимостей в единый развертываемый контейнер – породило ажиотаж в индустрии программного обеспечения. Теперь контейнеры являются крайне необходимыми для корпоративной инфраструктуры, а Docker представляет собой бесспорный отраслевой стандарт.
Руководство по Docker Compose для начинающих / Блог компании RUVDS.com / Хабр
https://habr.com/ru/company/ruvds/blog/450312/
https://habr.com/ru/company/ruvds/blog/450312/
Хабр
Руководство по Docker Compose для начинающих
Автор статьи, перевод которой мы сегодня публикуем, говорит, что она предназначена для тех разработчиков, которые хотят изучить Docker Compose и идут к тому, чтобы создать своё первое клиент-серверное...
Orchestration Frameworks for Big Data | by Javier Ramos | ITNEXT
https://itnext.io/orchestration-frameworks-for-big-data-cfb9d3af6e7e
https://itnext.io/orchestration-frameworks-for-big-data-cfb9d3af6e7e
Medium
Orchestration Frameworks for Big Data
Introduction
ETL_with_Azure_Cookbook_Practical_recipes_for_building_modern_ETL.pdf
14.2 MB
ETL with Azure Cookbook Practical recipes for building modern ETL solutions to load and transform data from any source
- Explore ETL and how it is different from ELT
- Move and transform various data sources with Azure ETL and ELT services
- Use SSIS 2019 with Azure HDInsight clusters
- Discover how to query SQL Server 2019 Big Data Clusters hosted in Azure
- Migrate SSIS solutions to Azure and solve key challenges associated with it
- Understand why data profiling is crucial and how to implement it in Azure Databricks
- Get to grips with BIML and learn how it applies to SSIS and Azure Data Factory solutions
- Explore ETL and how it is different from ELT
- Move and transform various data sources with Azure ETL and ELT services
- Use SSIS 2019 with Azure HDInsight clusters
- Discover how to query SQL Server 2019 Big Data Clusters hosted in Azure
- Migrate SSIS solutions to Azure and solve key challenges associated with it
- Understand why data profiling is crucial and how to implement it in Azure Databricks
- Get to grips with BIML and learn how it applies to SSIS and Azure Data Factory solutions
The Data Engineering Interview Study Guide | by SeattleDataGuy | Apr, 2021 | Better Programming
https://betterprogramming.pub/the-data-engineering-interview-study-guide-6f09420dd972
https://betterprogramming.pub/the-data-engineering-interview-study-guide-6f09420dd972
Medium
The Data Engineering Interview Study Guide
For your FAANG and other technical interviews
PowerBI-Advanced-Analytics-with-PowerBI-white-paper.pdf
1.4 MB
PowerBI-Advanced-Analytics-with-PowerBI-white-paper.pdf
KNIME Analytics Platform is the open source software for creating data science. Intuitive, open, and continuously integrating new developments, KNIME makes understanding data and designing data science workflows and reusable components accessible to everyone.
https://www.knime.com/knime-analytics-platform
https://www.knime.com/knime-analytics-platform
KNIME
KNIME Analytics Platform | KNIME
KNIME Analytics Platform is free and open source, which ensures users remain on the bleeding edge of data science, 300+ connectors to data sources, and integrations to all popular machine learning libraries.
KNIME Analytics Platform is the “killer app” for machine learning and statistics | by SJ Porter | Towards Data Science
https://towardsdatascience.com/knime-desktop-the-killer-app-for-machine-learning-cb07dbef1375
https://towardsdatascience.com/knime-desktop-the-killer-app-for-machine-learning-cb07dbef1375
Medium
KNIME Analytics Platform is the “killer app” for machine learning and statistics
A free, easy, and open-source tool for all things data? Yes, please!
Data Engineering with Python 2020.epub
29.6 MB
Data Engineering with Python, Paul Crickard, 2020
What you will learn
- Understand how data engineering supports data science workflows
- Discover how to extract data from files and databases and then clean, transform, and enrich it
- Configure processors for handling different file formats as well as both relational and NoSQL databases
- Find out how to implement a data pipeline and dashboard to visualize results
- Use staging and validation to check data before landing in the warehouse
- Build real-time pipelines with staging areas that perform validation and handle failures
- Get to grips with deploying pipelines in the production environment
What you will learn
- Understand how data engineering supports data science workflows
- Discover how to extract data from files and databases and then clean, transform, and enrich it
- Configure processors for handling different file formats as well as both relational and NoSQL databases
- Find out how to implement a data pipeline and dashboard to visualize results
- Use staging and validation to check data before landing in the warehouse
- Build real-time pipelines with staging areas that perform validation and handle failures
- Get to grips with deploying pipelines in the production environment