𝗪𝗮𝗻𝘁 𝘁𝗼 𝗠𝗮𝘀𝘁𝗲𝗿 𝗔𝗜 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘? 𝗛𝗲𝗿𝗲’𝘀 𝗛𝗼𝘄!😍

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Roadmap to Become DevOps Engineer 👨‍💻

📂 Linux Basics
 ∟📂 Scripting Skills
  ∟📂 CI/CD Tools
   ∟📂 Containerization
    ∟📂 Cloud Platforms
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      ∟ ✅ Apply For Job

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
• Role-Based Access Control (RBAC)
• Data Encryption (At Rest, In Transit)
• Managed Identities

10. Monitoring and Logging
• Azure Monitor
• Azure Log Analytics
• Azure Application Insights
• Metrics and Alerts

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
• Azure DevOps Pipelines
• Infrastructure as Code (IaC) with ARM Templates
• GitHub Actions
• Terraform on Azure

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All the best 👍👍

𝟲 𝗙𝗥𝗘𝗘 𝗬𝗼𝘂𝗧𝘂𝗯𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗞𝗶𝗰𝗸𝘀𝘁𝗮𝗿𝘁 𝗬𝗼𝘂𝗿 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗖𝗮𝗿𝗲𝗲𝗿!😍

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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.

Here, you can find Data Engineering Resources 👇
https://whatsapp.com/channel/0029Vaovs0ZKbYMKXvKRYi3C

All the best 👍👍

ETL vs ELT

𝗠𝗮𝘀𝘁𝗲𝗿 𝗦𝗼𝗳𝘁 𝗦𝗸𝗶𝗹𝗹𝘀 𝗳𝗼𝗿 𝗖𝗮𝗿𝗲𝗲𝗿 𝗦𝘂𝗰𝗰𝗲𝘀𝘀!😍

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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

Join for more data engineering resources: https://t.me/sql_engineer