Modern Pyspark Databricks Data by Raj Rishu (6 results)

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

Taschenbuch. Condition: Neu. Neuware - Build Real-World Data Engineering Skills with PySpark and DatabricksModern data engineering is no longer just about writing SQL or moving files from one system to another. Today's data engineers need to build pipelines that are scalable, reliable, optimized, secure, observable, and ready for production.PySpark & Databricks Data Engineering provides a practical, structured path from foundational data engineering concepts to advanced enterprise-level data platforms.Starting with Python, Spark, and PySpark fundamentals, the book progressively takes you into advanced transformations, performance optimization, Delta Lake, incremental processing, CDC, Structured Streaming, Auto Loader, Databricks Workflows, Unity Catalog, security, governance, system design, and production troubleshooting.Inside this book, you will learn: - Data engineering fundamentals and architecture- Python concepts required for data engineering- PySpark DataFrames and Spark SQL- Transformations, actions, joins, aggregations, and window functions- Complex and nested data processing- Spark partitioning and shuffle- Broadcast joins and data-skew handling- Spark performance optimization- Databricks Lakehouse architecture- Delta Lake and ACID transactions- MERGE, UPDATE, DELETE, and time travel- Incremental data processing- Change Data Capture (CDC)- Structured Streaming- Auto Loader- Checkpointing and watermarks- Databricks Workflows and orchestration- Data quality and production reliability- Unity Catalog- Security and access control- Data lineage and governance- Data products and enterprise architecture- Monitoring, observability, and cost optimization- CI/CD and testing- Disaster recovery concepts- End-to-end data engineering projects- Production troubleshooting- Databricks system-design interviews- PySpark coding challenges. …

Taschenbuch. Condition: Neu. Neuware - Databricks in Action: A Practical Guide to Data EngineeringModern data engineering is no longer just about moving data from one system to another. It is about building reliable, scalable, secure, and production-ready data platforms.Databricks in Action: A Practical Guide to Data Engineering provides a practical journey through the technologies and concepts that power modern data engineering. From Apache Spark and PySpark to SQL, Delta Lake, streaming, data quality, optimization, governance, and production deployment, this book focuses on the skills needed to build real-world data pipelines.Inside, readers will explore: - Data engineering fundamentals and modern lakehouse architecture- Apache Spark architecture and distributed processing- PySpark programming and DataFrame operations- Advanced SQL and data transformation techniques- Data ingestion and incremental processing- Bronze, Silver, and Gold data architectures- Delta Lake, MERGE, time travel, optimization, and maintenance- Batch and streaming data pipelines- CDC and slowly changing dimensions- Data quality, validation, and monitoring- Spark performance optimization and troubleshooting- Databricks CLI and REST API automation- Security, governance, and production deployment- End-to-end data engineering project practices- Interview questions and practical preparationThe book also includes detailed appendices covering SQL, PySpark, Spark functions, Delta Lake commands, Databricks CLI and REST API, troubleshooting, interview questions, and a complete project checklist.Whether you are learning data engineering, preparing for a Databricks-focused role, or looking for a practical reference while building modern data pipelines, this book is designed to help you develop the knowledge and confidence to work with Databricks in real-world environments.Learn the concepts. Build the pipelines. Troubleshoot the failures. Optimize the system. Think like a production data engineer.…

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Condition: New. Print on Demand.

Paperback. Condition: new. Paperback. Build Real-World Data Engineering Skills with PySpark and DatabricksModern data engineering is no longer just about writing SQL or moving files from one system to another. Today's data engineers need to build pipelines that are scalable, reliable, optimized, secure, observable, and ready for production.PySpark & Databricks Data Engineering provides a practical, structured path from foundational data engineering concepts to advanced enterprise-level data platforms.Starting with Python, Spark, and PySpark fundamentals, the book progressively takes you into advanced transformations, performance optimization, Delta Lake, incremental processing, CDC, Structured Streaming, Auto Loader, Databricks Workflows, Unity Catalog, security, governance, system design, and production troubleshooting.Inside this book, you will learn: Data engineering fundamentals and architecturePython concepts required for data engineeringPySpark DataFrames and Spark SQLTransformations, actions, joins, aggregations, and window functionsComplex and nested data processingSpark partitioning and shuffleBroadcast joins and data-skew handlingSpark performance optimizationDatabricks Lakehouse architectureDelta Lake and ACID transactionsMERGE, UPDATE, DELETE, and time travelIncremental data processingChange Data Capture (CDC)Structured StreamingAuto LoaderCheckpointing and watermarksDatabricks Workflows and orchestrationData quality and production reliabilityUnity CatalogSecurity and access controlData lineage and governanceData products and enterprise architectureMonitoring, observability, and cost optimizationCI/CD and testingDisaster recovery conceptsEnd-to-end data engineering projectsProduction troubleshootingDatabricks system-design interviewsPySpark coding challenges This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. …