Скачать или смотреть Data Engineering System Design Interview at Microsoft | Surfalytics

Join Dmitry and Sergii in this comprehensive Azure Data Engineering mock interview, where they collaboratively design a scalable data platform for a startup mobile application. This session delves into real-world scenarios, showcasing best practices in building a modern lakehouse architecture on Azure. Whether you're preparing for a data engineering interview or aiming to enhance your Azure skills, this video offers valuable insights into designing robust data solutions.

🎯 Key Takeaways:

Lakehouse Architecture Implementation: Understand the application of bronze, silver, and gold layers using Azure Data Lake Storage Gen2 and Delta Lake.

Data Ingestion Strategies: Explore the use of Azure Data Factory for ingesting data from diverse sources like Cosmos DB, Azure Postgres, and marketing APIs.

Incremental Loading Techniques: Learn methods for handling incremental data loads using Change Data Capture (CDC) and metadata-driven pipelines.

Data Processing with Azure Databricks: Discover how to leverage Delta Live Tables (DLT) and Autoloader for efficient data processing.

Security and Secret Management: Gain insights into securing data pipelines using Azure Key Vault and managed identities.

Data Quality and Observability: Understand the implementation of data quality checks using tools like DQX for PySpark and monitoring with Azure Monitor.

Infrastructure as Code: Learn about managing environments and deployments using Terraform and Azure DevOps.

BI Tool Integration: Discuss considerations for selecting BI tools, including the use of native Databricks dashboards and semantic layers.

🏄‍♂️ Topics Covered:

Project Overview: Designing a data engineering system for a startup mobile application.

Data Sources: Integrating data from Cosmos DB, Azure Postgres, and various marketing APIs.

Data Storage: Utilizing Azure Data Lake Storage Gen2 for storing raw and processed data.
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Data Processing: Implementing data transformation and enrichment using Azure Databricks and Delta Lake.

Data Modeling: Structuring data into bronze (raw), silver (cleaned), and gold (aggregated) layers.

Incremental Data Loading: Strategies for efficiently loading new and updated data.

Security Measures: Ensuring secure access and secret management across services.

Data Quality Assurance: Implementing checks and monitoring to maintain data integrity.

Infrastructure Management: Deploying and managing resources using Terraform and CI/CD pipelines.

Business Intelligence Integration: Approaches for integrating BI tools and creating dashboards for end-users.

⏰ Timecodes:
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Inspired by West Coast surfing spots 🏖️ and Pacific Ocean vibes 🌊. Created to help you start a new career in the data analytics space, and develop data engineering and analytics skills through coaching. It will teach you not just dry skills, but will keep your focus on delivering significant value to businesses in the analytics realm as well as help to get fair compensation 💰 for the work you’re passionate about ❤️‍🔥.

The goal of Surfalytics is to assist you in achieving one of the following:

🏄‍♂️ Land your first job in the data industry with literally zero experience. I have accomplished this many times across the globe.

🏄 Advance from a middle-level role to a senior position (as an Analyst or Engineer).

🏄‍♀️ Transition from a non-technical Analyst role to a technical Engineer role.

Moreover, we will focus on creating a highly competitive CV and securing top job offers. We will not consider any lowball offers, focusing only on top-tier companies and well-paid opportunities.

Finally, Surfalytics is a results-driven community with a very narrow focus, resulting in a high return on investment (ROI). Here, ‘investment’ does not mean money but your time. I am literally fighting for your attention to encourage you to study and work hard, instead of watching Netflix or playing video games.

This is the best YouTube channel for Data Analytics and Engineering. You will patch up a lack of knowledge and get new experience and tips to build a Data Analyst roadmap or Data Engineer roadmap for yourself.

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