𝗕𝗲𝗰𝗼𝗺𝗲 𝗮 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗣𝗿𝗼𝗳𝗲𝘀𝘀𝗶𝗼𝗻𝗮𝗹 𝘄𝗶𝘁𝗵 𝗧𝗵𝗶𝘀 𝗙𝗿𝗲𝗲 𝗢𝗿𝗮𝗰𝗹𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗣𝗮𝘁𝗵!😍

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𝐋𝐢𝐧𝐤👇:-

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Data Engineering free courses   

Linked Data Engineering
🎬 Video Lessons
Rating ⭐️: 5 out of 5     
Students 👨‍🎓: 9,973
Duration ⏰:  8 weeks long
Source: openHPI
🔗 Course Link  

Data Engineering
Credits ⏳: 15
Duration ⏰: 4 hours
🏃‍♂️ Self paced       
Source:  Google cloud
🔗 Course Link

Data Engineering Essentials using Spark, Python and SQL  
🎬 402 video lesson
🏃‍♂️ Self paced
Teacher: itversity
Resource: Youtube
🔗 Course Link  
 
Data engineering with Azure Databricks      
Modules ⏳: 5
Duration ⏰:  4-5 hours worth of material
🏃‍♂️ Self paced       
Source:  Microsoft ignite
🔗 Course Link

Perform data engineering with Azure Synapse Apache Spark Pools      
Modules ⏳: 5
Duration ⏰:  2-3 hours worth of material
🏃‍♂️ Self paced       
Source:  Microsoft Learn
🔗 Course Link

Books
Data Engineering
The Data Engineers Guide to Apache Spark

All the best 👍👍

𝗙𝗿𝗲𝗲 𝗧𝗖𝗦 𝗶𝗢𝗡 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗨𝗽𝗴𝗿𝗮𝗱𝗲 𝗬𝗼𝘂𝗿 𝗦𝗸𝗶𝗹𝗹𝘀!😍

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Roadmap for becoming an Azure Data Engineer in 2025:

- SQL
- Basic python
- Cloud Fundamental
- ADF
- Databricks/Spark/Pyspark
- Azure Synapse
- Azure Functions, Logic Apps
- Azure Storage, Key Vault
- Dimensional Modelling
- Azure Fabric
- End-to-End Project
- Resume Preparation
- Interview Prep

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

All the best 👍👍

𝗧𝗼𝗽 𝟱 𝗙𝗿𝗲𝗲 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗬𝗼𝘂 𝗖𝗮𝗻 𝗘𝗻𝗿𝗼𝗹𝗹 𝗜𝗻 𝗧𝗼𝗱𝗮𝘆!😍

In today’s fast-paced tech industry, staying ahead requires continuous learning and upskilling✨️

Fortunately, 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 is offering 𝗳𝗿𝗲𝗲 𝗰𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗰𝗼𝘂𝗿𝘀𝗲𝘀 that can help beginners and professionals enhance their 𝗲𝘅𝗽𝗲𝗿𝘁𝗶𝘀𝗲 𝗶𝗻 𝗱𝗮𝘁𝗮, 𝗔𝗜, 𝗦𝗤𝗟, 𝗮𝗻𝗱 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 without spending a dime!⬇️

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Spark Must-Know Differences:

➤ RDD vs DataFrame:
- RDD: Low-level API, unstructured data, more control.
- DataFrame: High-level API, optimized, structured data.

➤ DataFrame vs Dataset:
- DataFrame: Untyped API, ease of use, suitable for Python.
- Dataset: Typed API, compile-time safety, best with Scala/Java.

➤ map() vs flatMap():
- map(): Transforms each element, returns a new RDD with the same number of elements.
- flatMap(): Transforms each element and flattens the result, can return a different number of elements.

➤ filter() vs where():
- filter(): Filters rows based on a condition, commonly used in RDDs.
- where(): SQL-like filtering, more intuitive in DataFrames.

➤ collect() vs take():
- collect(): Retrieves the entire dataset to the driver.
- take(): Retrieves a specified number of rows, safer for large datasets.

➤ cache() vs persist():
- cache(): Stores data in memory only.
- persist(): Stores data with a specified storage level (memory, disk, etc.).

➤ select() vs selectExpr():
- select(): Selects columns with standard column expressions.
- selectExpr(): Selects columns using SQL expressions.

➤ join() vs union():
- join(): Combines rows from different DataFrames based on keys.
- union(): Combines rows from DataFrames with the same schema.

➤ withColumn() vs withColumnRenamed():
- withColumn(): Creates or replaces a column.
- withColumnRenamed(): Renames an existing column.

➤ groupBy() vs agg():
- groupBy(): Groups rows by a column or columns.
- agg(): Performs aggregate functions on grouped data.

➤repartition() vs coalesce():
- repartition(): Increases or decreases the number of partitions, performs a full shuffle.
- coalesce(): Reduces the number of partitions without a full shuffle, more efficient for reducing partitions.

➤ orderBy() vs sort():
- orderBy(): Returns a new DataFrame sorted by specified columns, supports both ascending and descending.
- sort(): Alias for orderBy(), identical in functionality.

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

All the best 👍👍

𝗙𝗥𝗘𝗘 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀! 📊🚀

Want to master data analytics? Here are top free courses, books, and certifications to help you get started with Power BI, Tableau, Python, and Excel.

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All The Best 💥

10 Pyspark questions to clear your interviews.

1. How do you deploy PySpark applications in a production environment?
2. What are some best practices for monitoring and logging PySpark jobs?
3. How do you manage resources and scheduling in a PySpark application?
4. Write a PySpark job to perform a specific data processing task (e.g., filtering data, aggregating results).
5. You have a dataset containing user activity logs with missing values and inconsistent data types. Describe how you would clean and standardize this dataset using PySpark.
6. Given a dataset with nested JSON structures, how would you flatten it into a tabular format using PySpark?
8. Your PySpark job is running slower than expected due to data skew. Explain how you would identify and address this issue.
9. You need to join two large datasets, but the join operation is causing out-of-memory errors. What strategies would you use to optimize this join?
10. Describe how you would set up a real-time data pipeline using PySpark and Kafka to process streaming data

Remember: Don’t just mug up these questions, practice them on your own to build problem-solving skills and clear interviews easily

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

All the best 👍👍

𝗪𝗮𝗻𝘁 𝘁𝗼 𝗺𝗮𝘀𝘁𝗲𝗿 𝗘𝘅𝗰𝗲𝗹 𝗶𝗻 𝗷𝘂𝘀𝘁 𝟳 𝗱𝗮𝘆𝘀?

📊 Here's a structured roadmap to help you go from beginner to pro in a week!

Whether you're learning formulas, functions, or data visualization, this guide covers everything step by step.

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All The Best 💥

Apache Airflow Interview Questions: Basic, Intermediate and Advanced Levels

𝗕𝗮𝘀𝗶𝗰 𝗟𝗲𝘃𝗲𝗹:

• What is Apache Airflow, and why is it used?
• Explain the concept of Directed Acyclic Graphs (DAGs) in Airflow.
• How do you define tasks in Airflow?
• What are the different types of operators in Airflow?
• How can you schedule a DAG in Airflow?

𝗜𝗻𝘁𝗲𝗿𝗺𝗲𝗱𝗶𝗮𝘁𝗲 𝗟𝗲𝘃𝗲𝗹:

• How do you monitor and manage workflows in Airflow?
• Explain the difference between Airflow Sensors and Operators.
• What are XComs in Airflow, and how do you use them?
• How do you handle dependencies between tasks in a DAG?
• Explain the process of scaling Airflow for large-scale workflows.

𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗟𝗲𝘃𝗲𝗹:

• How do you implement retry logic and error handling in Airflow tasks?
• Describe how you would set up and manage Airflow in a production environment.
• How can you customize and extend Airflow with plugins?
• Explain the process of dynamically generating DAGs in Airflow.
• Discuss best practices for optimizing Airflow performance and resource utilization.
• How do you manage and secure sensitive data within Airflow workflows?

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

All the best 👍👍

Data-engineer-handbook

This is a repo with links to everything you'd ever want to learn about data engineering

Creator: DataExpert-io
Stars ⭐️: 24.9k
Forked by: 4.9k

Github Repo:
https://github.com/DataExpert-io/data-engineer-handbook

#github

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

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📢 Start watching today and stay ahead in the AI revolution! 🚀

Roadmap to Become DevOps Engineer 👨‍💻

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

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

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💡 Whether you’re a student, working professional, or just eager to learn—

This is your golden opportunity!✅️

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

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

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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Soft skills are just as important as technical expertise! 🌟

Here are 3 FREE courses to help you communicate, negotiate, and present with confidence

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