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Crack Your AWS Interview: Key Questions on Lambda Scalability, Glue Jobs, and IAM
Prepare for AWS interviews with top questions on Lambda, Glue, S3, IAM, and PySpark. Real-world answers with SQL and troubleshooting examples. Read More ⇢
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Master Data Engineering Vocabulary for Client Interviews
This Data Engineering Vocabulary Guide emphasizes the importance of clear communication in data engineering roles. It covers essential terms and phrases related to data pipelines, processing, governance, and performance optimization, as well as common challenges and best practices for tools like AWS Glue and Kafka, ensuring engineers can effectively articulate… Read More ⇢
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Complete Data Engineer Roadmap: Tools and Skills Explained
Discover the complete path to become a Data Engineer in 2025. Learn the essential tools, technologies, and platforms—like Spark, Kafka, Airflow, and cloud services—that power modern data pipelines and help you build a successful career in data engineering.” Read More ⇢
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Top Databricks PySpark and AWS Questions for Senior Data Engineers
Explore advanced Databricks PySpark and AWS interview questions with real-world answers. Learn about SCD Type 2, Medallion Architecture, Dynamic Partition Pruning, performance tuning, Delta Lake, and complex pipeline design to prepare for senior data engineering roles Read More ⇢
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Agentic AI Use Cases: How Businesses Are Using Autonomous AI Agents
Discover how Agentic AI is applied in real-world domains like healthcare, finance, retail, education, and manufacturing. Learn its benefits, real use cases, and why businesses should adopt Agentic AI in 2025. Read More ⇢
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Step-by-Step Azure Data Factory Project for Data Engineers
Build a mini project in Azure Data Factory (ADF) with this step-by-step tutorial. Learn key ADF terms—pipelines, datasets, linked services, activities, triggers, and integration runtimes—while creating a real-world ETL workflow. Read More ⇢
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How Databricks Uses Cores and Memory for Efficient Big Data Processing
Learn how Databricks clusters use memory, cores, and nodes to process big data. Includes a step-by-step 100GB data partitioning example for clarity. Read More ⇢
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Master the New PySpark Features and Functions in Spark 3.5
Discover the latest PySpark functions and features in Spark 3.4 and 3.5, including Arrow-optimized UDFs, Python UDTFs, new array helpers, HyperLogLog aggregations, and enhanced streaming. Learn how to use them with practical examples. Read More ⇢
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Top Strategy to Revise All Data Engineer Interview Questions Fast
Discover a powerful method to recap all key Data Engineering interview questions in one go—covering SQL, Python, Spark, AWS, and more. Perfect guide for last-minute revision. Read More ⇢
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