Cogniforge Global, Inc

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Top 30 Databricks Interview Questions for Big Tech Companies Preparing for a Data Engineer or Analyst interview? If Databricks is part of the job description, this post will guide you through the most important Databricks interview questions frequently asked by top tech companies. Here’s a refined list of 30 essential questions to help you ace your next interview: 1. Databricks Fundamentals 1. What is Databricks, and how is it different from Apache Spark? 2. Explain the architecture of Databricks. 3. What are Databricks clusters, and what are the different types? 4. How does Databricks Workspace enhance collaboration? 5. What are the benefits of using Databricks on cloud platforms like AWS, Azure, or GCP? 2. Delta Lake and Data Processing 6. What is Delta Lake, and how does it ensure reliability in data lakes? 7. Explain ACID transactions in Delta Lake. 8. What is the purpose of Delta Lake’s time travel feature? 9. How do you optimize Delta Tables in Databricks? 10. Describe the Bronze, Silver, and Gold architecture in Databricks for data pipelines. 3. Databricks SQL and Query Optimization 11. What is Databricks SQL, and when should you use it? 12. How do you create and manage SQL dashboards in Databricks? 13. What is the difference between Spark SQL and Databricks SQL? 14. How do you optimize queries in Databricks? 15. What are some common query performance bottlenecks, and how do you address them? 4. Spark-Specific Questions 16. How do DataFrames differ from RDDs in Spark? 17. What are wide and narrow transformations in Spark? 18. How do you handle skewed data in Spark applications? 19. What is the role of caching in Spark jobs? 20. Explain the partitioning concept in Spark and its importance. 5. Machine Learning and Advanced Analytics 21. How does Databricks support the machine learning lifecycle? 22. What is MLflow, and how is it used in Databricks? 23. How do you perform feature engineering in Databricks? 24. What are some best practices for managing machine learning experiments in Databricks? 6. Security and Governance in Databricks 25. How does Databricks ensure data security? 26. What is Unity Catalog, and how does it improve data governance? 27. How do you implement role-based access control in Databricks? 7. Integration and Troubleshooting 28. How do you integrate Databricks with external BI tools like Power BI or Tableau? 29. What are some common challenges when managing Databricks clusters? 30. How do you monitor and debug Spark jobs in Databricks? #Databricks #BigData #DataEngineering #DataAnalytics #SQL #DeltaLake #ApacheSpark #TechInterviews #MachineLearning #CareerGrowth

𝑨𝒓𝒆 𝒚𝒐𝒖 𝒑𝒓𝒆𝒑𝒂𝒓𝒊𝒏𝒈 𝒇𝒐𝒓 𝒂 𝒅𝒂𝒕𝒂 𝒆𝒏𝒈𝒊𝒏𝒆𝒆𝒓𝒊𝒏𝒈 𝒊𝒏𝒕𝒆𝒓𝒗𝒊𝒆𝒘?  In this video, I'll walk you through everything you need to know to ace your next data engineering interview, from the technical skills to the problem-solving strategies! We'll cover: Key skills: The essential tools, technologies, and concepts every data engineer should master (SQL, Python, ETL, data warehousing, cloud platforms, etc.). Interview process: A breakdown of the typical data engineering interview stages, from phone screens to onsite interviews. Common interview questions: Learn how to tackle common technical and behavioral questions, with example answers and approaches. Data engineering problems: I'll guide you through solving typical coding challenges and system design questions that you might face. Tips for success: How to build confidence, manage your time, and leave a lasting impression during interviews. Whether you're new to data engineering or looking to level up your interview game, this video has everything you need to succeed. Make sure to like, subscribe, and hit the notification bell so you don’t miss any of our upcoming interview preparation content! Good luck, and let's crack that interview together! Video Link: https://lnkd.in/dY6S7-gQ #interview #dataengineer

Ready to Crack Your Next Azure Data Engineer Interview??🌟  If you're aiming for success in Azure Data Engineer interviews, it's all about mastering real-world scenarios and practical challenges. To help you out, here are a few must-know questions to test your knowledge:   🔑 Sample Questions 1️⃣ How would you design a scalable ETL pipeline using Azure Data Factory to process terabytes of data daily?   2️⃣ Explain how you would handle schema drift in Azure Data Factory for dynamic datasets.   3️⃣ What are the best practices for optimizing Spark jobs in Azure Databricks for large-scale data processing?? 💡 These are just a glimpse of the 25 most important questions that cover:   - Azure Data Factory   - Azure Databricks   - Azure Synapse Analytics   - PySpark and More  📄 Want the complete list with detailed answers to ace your interview?   👉 Check out the PDF now and take a step closer to success!  Let me know your thoughts or which topic you'd like to deep dive into next!! 🚀 Want to ace this and more?? 📥 Grab the PDF with all the steps and insights here:   👉 [Attach your PDF link via Tompate with 25% discount code: Nitya21]  https://lnkd.in/d9gaEB_H ✨ What’s inside:   ✅ Step-by-step guidance for this and other must-know Azure scenarios.   ✅ Structured insights to help you tackle even the toughest interview questions.  Proven Success: Hundreds of professionals have cracked their interviews using this guide. Don’t miss out—this could be your key to success!  ✅ Repost if you found this useful 🚀 Follow Aditya Chandak #Azure #DataEngineering #DataFactory #Databricks #Synapse #PySpark #CareerGrowth

A quick summary that I wrote on Medium about Week 3 from Zach Wilson Data Engineering BootCamp - Infrastructure track. This week was all about Databricks and Apache Spark, all the basic stuff delivered by Yared Gudeta from Databricks. While next week we go more advanced, here´s the summary of all the week of content that you probably are following here. ➡️Day 1: Databricks Platform Overview ➡️Day 2: Introduction to Apache Spark ➡️Day 3: Spark SQL and DataFrame Operations Read the full article here 👇 https://lnkd.in/dmqb_qGF #bigdata #databricks #spark #dataengineering

Data Engineering can feel overwhelming at first But once you break it down into core concepts, it starts making a lot more sense. Here's a guide to help demystify it: 1. Data Pipelines   - ETL vs ELT: Learn the difference between Extract-Transform-Load (ETL) and Extract-Load-Transform (ELT).   - Tools: Apache Airflow, Prefect, Luigi.   - Concept: Automate data movement from sources (APIs, databases) to destinations (data warehouses, lakes). 2. Storage   - Structured Data: Relational Databases (SQL).   - Unstructured Data: Data lakes (e.g , ADLS, GCS, S3, HDFS).   - Columnar Storage: Parquet, ORC.   - Learn about partitioning and indexing for efficient querying. 3. Distributed Systems   - Hadoop, Spark, Kafka.   - Concepts like distributed file systems, parallel processing, and message queues. 4. Data Warehousing   - Tools: Snowflake, BigQuery, Redshift.   - Dimensional modeling, star schema, fact tables, and dimensions. 5. Batch vs Streaming   - Batch: Processing data in chunks (daily ,weekly, monthly jobs).   - Streaming: Processing data in real-time (e.g., Kafka, Spark Streaming). 6. Data Integration   - APIs, webhooks, connectors (e.g. Fivetran, Stitch).   - Understand how to clean, validate, and deduplicate data. 7. Orchestration   - Managing dependencies, retries, and monitoring workflows.   - Tools like Dagster, Apache Airflow. 8. Monitoring and Logging   - Monitor pipeline performance, data quality, and job failures.   - Tools: Prometheus, Grafana, OpenTelemetry. 9. Data Governance   - Data security, lineage, compliance (GDPR, HIPAA).   - Tools: Apache Atlas, Collibra. 10. Programming   - Python (Pandas, PySpark), SQL, and Bash scripting.   - Basics of APIs (REST, GraphQL) for data extraction. 11. Data Quality and Validation   - Implement checks for nulls, duplicates, and outliers.   - Tools like Great Expectations. 12. Cloud Platforms   - AWS, Azure, GCP.   - Focus on cloud-native tools for storage, computation, and pipelines. 🤝 Join my Data Community : https://lnkd.in/gy4R55Tj . . 📌 Follow Abhisek Sahu for more. 𝑹𝒆𝒑𝒐𝒔𝒕 𝒊𝒇 𝒚𝒐𝒖 𝒇𝒊𝒏𝒅 𝒊𝒕 𝒖𝒔𝒆𝒇𝒖𝒍✅ #dataengineering #dataengineer #bigdata #cloud #weekend

Pyspark - Dynamic Partition Pruning

🔍𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 𝐃𝐚𝐭𝐚𝐛𝐫𝐢𝐜𝐤𝐬 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞: 𝐀 𝐇𝐢𝐠𝐡-𝐋𝐞𝐯𝐞𝐥 𝐎𝐯𝐞𝐫𝐯𝐢𝐞𝐰 Databricks provides a powerful platform for managing and processing data, combining flexibility and scalability. Here’s a quick overview of its architecture in the context of AWS: 💡𝐊𝐞𝐲 𝐂𝐨𝐦𝐩𝐨𝐧𝐞𝐧𝐭𝐬: 1️⃣ Control Plane: Managed by Databricks in your Databricks account. Includes backend services and the web application for managing workflows. 2️⃣ Compute Plane: Serverless Compute: Runs in a dedicated serverless compute plane within your Databricks account. Classic Compute: Resources are hosted in your AWS account within the classic compute plane, leveraging your network and AWS resources. 🔹 Storage: Every Databricks workspace is linked to a workspace storage bucket in your AWS account, ensuring seamless storage and processing of your data. For more insights, check out the differences between classic compute and serverless compute, and explore which option aligns with your business needs. Databricks empowers organizations to unlock the full potential of their data while ensuring scalability and control. What’s your experience working with Databricks? Let’s discuss below! #Databricks #DataEngineering #CloudComputing #AWS #BigData

I'm happy to share that I have earned Databricks Certified Data Engineer Associate certification