𝗛𝗮𝗿𝘃𝗮𝗿𝗱 𝗶𝘀 𝗢𝗳𝗳𝗲𝗿𝗶𝗻𝗴 𝗙𝗥𝗘𝗘 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 – 𝗗𝗼𝗻’𝘁 𝗠𝗶𝘀𝘀 𝗢𝘂𝘁!😍

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You will be 18x better at Azure Data Engineering

If you cover these topics:

1. Azure Fundamentals
• Cloud Computing Basics
• Azure Global Infrastructure
• Azure Regions and Availability Zones
• Resource Groups and Management

2. Azure Storage Solutions
• Azure Blob Storage
• Azure Data Lake Storage (ADLS)
• Azure SQL Database
• Cosmos DB

3. Data Ingestion and Integration
• Azure Data Factory
• Azure Event Hubs
• Azure Stream Analytics
• Azure Logic Apps

4. Big Data Processing
• Azure Databricks
• Azure HDInsight
• Azure Synapse Analytics
• Spark on Azure

5. Serverless Compute
• Azure Functions
• Azure Logic Apps
• Azure App Services
• Durable Functions

6. Data Warehousing
• Azure Synapse Analytics (formerly SQL Data Warehouse)
• Dedicated SQL Pool vs. Serverless SQL Pool
• Data Marts
• PolyBase

7. Data Modeling
• Star Schema
• Snowflake Schema
• Slowly Changing Dimensions
• Data Partitioning Strategies

8. ETL and ELT Pipelines
• Extract, Transform, Load (ETL) Patterns
• Extract, Load, Transform (ELT) Patterns
• Azure Data Factory Pipelines
• Data Flow Activities

9. Data Security
• Azure Key Vault
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• Data Encryption (At Rest, In Transit)
• Managed Identities

10. Monitoring and Logging
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11. Scalability and Performance
• Vertical vs. Horizontal Scaling
• Load Balancers
• Autoscaling
• Caching with Azure Redis Cache

12. Cost Management
• Azure Cost Management and Billing
• Reserved Instances and Spot VMs
• Cost Optimization Strategies
• Pricing Calculators

13. Networking
• Virtual Networks (VNets)
• VPN Gateway
• ExpressRoute
• Azure Firewall and NSGs

14. CI/CD in Azure
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• Infrastructure as Code (IaC) with ARM Templates
• GitHub Actions
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20 recently asked 𝗞𝗔𝗙𝗞𝗔 interview questions.

- How do you create a topic in Kafka using the Confluent CLI?
- Explain the role of the Schema Registry in Kafka.
- How do you register a new schema in the Schema Registry?
- What is the importance of key-value messages in Kafka?
- Describe a scenario where using a random key for messages is beneficial.
- Provide an example where using a constant key for messages is necessary.
- Write a simple Kafka producer code that sends JSON messages to a topic.
- How do you serialize a custom object before sending it to a Kafka topic?
- Describe how you can handle serialization errors in Kafka producers.
- Write a Kafka consumer code that reads messages from a topic and deserializes them from JSON.
- How do you handle deserialization errors in Kafka consumers?
- Explain the process of deserializing messages into custom objects.
- What is a consumer group in Kafka, and why is it important?
- Describe a scenario where multiple consumer groups are used for a single topic.
- How does Kafka ensure load balancing among consumers in a group?
- How do you send JSON data to a Kafka topic and ensure it is properly serialized?
- Describe the process of consuming JSON data from a Kafka topic and converting it to a usable format.
- Explain how you can work with CSV data in Kafka, including serialization and deserialization.
- Write a Kafka producer code snippet that sends CSV data to a topic.
- Write a Kafka consumer code snippet that reads and processes CSV data from a topic.

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ETL vs ELT

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Python for Data Engineering role 👇

➊ List Comprehensions and Dict Comprehensions
↳ Optimize iteration with one-liners
↳ Fast filtering and transformations
↳ O(n) time complexity

➋ Lambda Functions
↳ Anonymous functions for concise operations
↳ Used in map(), filter(), and sort()
↳ Key for functional programming

➌ Functional Programming (map, filter, reduce)
↳ Apply transformations efficiently
↳ Reduce dataset size dynamically
↳ Avoid unnecessary loops

➍ Iterators and Generators
↳ Efficient memory handling with yield
↳ Streaming large datasets
↳ Lazy evaluation for performance

➎ Error Handling with Try-Except
↳ Graceful failure handling
↳ Preventing crashes in pipelines
↳ Custom exception classes

➏ Regex for Data Cleaning
↳ Extract structured data from unstructured text
↳ Pattern matching for text processing
↳ Optimized with re.compile()

➐ File Handling (CSV, JSON, Parquet)
↳ Read and write structured data efficiently
↳ pandas.read_csv(), json.load(), pyarrow
↳ Handling large files in chunks

➑ Handling Missing Data
↳ .fillna(), .dropna(), .interpolate()
↳ Imputing missing values
↳ Reducing nulls for better analytics

➒ Pandas Operations
↳ DataFrame filtering and aggregations
↳ .groupby(), .pivot_table(), .merge()
↳ Handling large structured datasets

➓ SQL Queries in Python
↳ Using sqlalchemy and pandas.read_sql()
↳ Writing optimized queries
↳ Connecting to databases

⓫ Working with APIs
↳ Fetching data with requests and httpx
↳ Handling rate limits and retries
↳ Parsing JSON/XML responses

⓬ Cloud Data Handling (AWS S3, Google Cloud, Azure)
↳ Upload/download data from cloud storage
↳ boto3, gcsfs, azure-storage
↳ Handling large-scale data ingestion

𝐓𝐡𝐞 𝐛𝐞𝐬𝐭 𝐰𝐚𝐲 𝐭𝐨 𝐥𝐞𝐚𝐫𝐧 𝐏𝐲𝐭𝐡𝐨𝐧 𝐢𝐬 𝐧𝐨𝐭 𝐣𝐮𝐬𝐭 𝐛𝐲 𝐬𝐭𝐮𝐝𝐲𝐢𝐧𝐠, 𝐛𝐮𝐭 𝐛𝐲 𝐢𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭𝐢𝐧𝐠 𝐢𝐭

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5 frequently Asked SQL Interview Questions with Answers in Data Engineering interviews:
𝐃𝐢𝐟𝐟𝐢𝐜𝐮𝐥𝐭𝐲 - 𝐌𝐞𝐝𝐢𝐮𝐦

⚫️Determine the Top 5 Products with the Highest Revenue in Each Category.
Schema: Products (ProductID, Name, CategoryID), Sales (SaleID, ProductID, Amount)

WITH ProductRevenue AS (
SELECT p.ProductID,
p.Name,
p.CategoryID,
SUM(s.Amount) AS TotalRevenue,
RANK() OVER (PARTITION BY p.CategoryID ORDER BY SUM(s.Amount) DESC) AS RevenueRank
FROM Products p
JOIN Sales s ON p.ProductID = s.ProductID
GROUP BY p.ProductID, p.Name, p.CategoryID
)
SELECT ProductID, Name, CategoryID, TotalRevenue
FROM ProductRevenue
WHERE RevenueRank <= 5;

⚫️ Identify Employees with Increasing Sales for Four Consecutive Quarters.
Schema: Sales (EmployeeID, SaleDate, Amount)

WITH QuarterlySales AS (
SELECT EmployeeID,
DATE_TRUNC('quarter', SaleDate) AS Quarter,
SUM(Amount) AS QuarterlyAmount
FROM Sales
GROUP BY EmployeeID, DATE_TRUNC('quarter', SaleDate)
),
SalesTrend AS (
SELECT EmployeeID,
Quarter,
QuarterlyAmount,
LAG(QuarterlyAmount, 1) OVER (PARTITION BY EmployeeID ORDER BY Quarter) AS PrevQuarter1,
LAG(QuarterlyAmount, 2) OVER (PARTITION BY EmployeeID ORDER BY Quarter) AS PrevQuarter2,
LAG(QuarterlyAmount, 3) OVER (PARTITION BY EmployeeID ORDER BY Quarter) AS PrevQuarter3
FROM QuarterlySales
)
SELECT EmployeeID, Quarter, QuarterlyAmount
FROM SalesTrend
WHERE QuarterlyAmount > PrevQuarter1 AND PrevQuarter1 > PrevQuarter2 AND PrevQuarter2 > PrevQuarter3;

⚫️ List Customers Who Made Purchases in Each of the Last Three Years.
Schema: Orders (OrderID, CustomerID, OrderDate)

WITH YearlyOrders AS (
SELECT CustomerID,
EXTRACT(YEAR FROM OrderDate) AS OrderYear
FROM Orders
GROUP BY CustomerID, EXTRACT(YEAR FROM OrderDate)
),
RecentYears AS (
SELECT DISTINCT OrderYear
FROM Orders
WHERE OrderDate >= CURRENT_DATE - INTERVAL '3 years'
),
CustomerYearlyOrders AS (
SELECT CustomerID,
COUNT(DISTINCT OrderYear) AS YearCount
FROM YearlyOrders
WHERE OrderYear IN (SELECT OrderYear FROM RecentYears)
GROUP BY CustomerID
)
SELECT CustomerID
FROM CustomerYearlyOrders
WHERE YearCount = 3;


⚫️ Find the Third Lowest Price for Each Product Category.
Schema: Products (ProductID, Name, CategoryID, Price)

WITH RankedPrices AS (
SELECT CategoryID,
Price,
DENSE_RANK() OVER (PARTITION BY CategoryID ORDER BY Price ASC) AS PriceRank
FROM Products
)
SELECT CategoryID, Price
FROM RankedPrices
WHERE PriceRank = 3;

⚫️ Identify Products with Total Sales Exceeding a Specified Threshold Over the Last 30 Days.
Schema: Sales (SaleID, ProductID, SaleDate, Amount)

WITH RecentSales AS (
SELECT ProductID,
SUM(Amount) AS TotalSales
FROM Sales
WHERE SaleDate >= CURRENT_DATE - INTERVAL '30 days'
GROUP BY ProductID
)
SELECT ProductID, TotalSales
FROM RecentSales
WHERE TotalSales > 200;

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