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.

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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

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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?

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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:
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Roadmap to Become DevOps Engineer 👨‍💻

📂 Linux Basics
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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
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• Caching with Azure Redis Cache

12. Cost Management
• Azure Cost Management and Billing
• Reserved Instances and Spot VMs
• Cost Optimization Strategies
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13. Networking
• Virtual Networks (VNets)
• VPN Gateway
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14. CI/CD in Azure
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• Infrastructure as Code (IaC) with ARM Templates
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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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