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The patterns of success that you acquire from reading this book will help you hone your skills to build high-performing and scalable ACID-compliant lakehouses using flexible and cost-efficient decoupled storage and compute capabilities. Extensive coverage of Delta Lake ensures that you are aware of and can benefit from all that this new, open source storage layer can offer. In addition to the deep examples on Databricks in the book, there is coverage of alternative platforms such as Synapse Analytics and Snowflake so that you can make the right platform choice for your needs.
Benefit from the new Delta Lake open-source storage layer for data lakehouses
Take advantage of schema evolution, change feeds, live tables, and more
Writefunctional PySpark code for data lakehouse ELT jobs
Optimize Apache Spark performance through partitioning, indexing, and other tuning options
Choose between alternatives such as Databricks, Synapse Analytics, and Snowflake
The patterns of success that you acquire from reading this book will help you hone your skills to build high-performing and scalable ACID-compliant lakehouses using flexible and cost-efficient decoupled storage and compute capabilities. Extensive coverage of Delta Lake ensures that you are aware of and can benefit from all that this new, open source storage layer can offer. In addition to the deep examples on Databricks in the book, there is coverage of alternative platforms such as Synapse Analytics and Snowflake so that you can make the right platform choice for your needs.
Benefit from the new Delta Lake open-source storage layer for data lakehouses
Take advantage of schema evolution, change feeds, live tables, and more
Writefunctional PySpark code for data lakehouse ELT jobs
Optimize Apache Spark performance through partitioning, indexing, and other tuning options
Choose between alternatives such as Databricks, Synapse Analytics, and Snowflake
Having several Azure Data, AI, and Lakehouse certifications under his belt, Ron has been a go-to technical advisor for some of the largest and most impactful Azure implementation projects on the planet. He has been responsible for scaling key data architectures, defining the road map and strategy for the future of data and business intelligence needs, and challenging customers to grow by thoroughly understanding the fluid business opportunities and enabling change by translating them into high-quality and sustainable technical solutionsthat solve the most complex challenges and promote digital innovation and transformation.
Ron is a gifted presenter and trainer, known for his innate ability to clearly articulate and explain complex topics to audiences of all skill levels. He applies a practical and business-oriented approach by taking transformational ideas from concept to scale. He is a true enabler of positive and impactful change by championing a growth mindset.
Shows data lakehouse design using Apache Spark on Azure
Teaches performance optimization techniques for Spark queries
Provides hands-on PySpark and Delta Lake examples for lakehouse ELT jobs
Part I: Getting Started.- Chapter 1: The Data Lakehouse Paradigm.- Part II: Data Platforms.- Chapter 2: Snowflake.- Chapter 3: Databricks.- Chapter 4: Synapse Analytics.- Part III: Apache Spark ELT.- Chapter 5: Pipelines and Jobs.- Chapter 6: Notebook Code.- Part IV: Delta Lake.-Chapter 7: Schema Evolution.- Chapter 8: Change Feed.- Chapter 9: Clones.- Chapter 10: Live Tables.- Chapter 11: Sharing.- Part V: Optimizing Performance.- Chapter 12: Dynamic Partition Pruning for Querying Star Schemas.- Chapter 13: Z-Ordering & Data Skipping.- Chapter 14: Adaptive Query Execution.- Chapter 15: ¿Bloom Filter Index.- Chapter 16: Hyperspace.- Part VI: Advanced Capabilities.- Chapter 17: Auto Loader.- Chapter 18: Python Wheels.- Chapter 19: Security & Controls.
| Erscheinungsjahr: | 2022 |
|---|---|
| Genre: | Informatik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Taschenbuch |
| Seiten: | 488 |
| Titelzusatz: | Building and Scaling Data Lakehouses on Azure with Delta Lake, Apache Spark, Databricks, Synapse Analytics, and Snowflake |
| Inhalt: |
xxii
465 S. 365 s/w Illustr. 465 p. 365 illus. |
| ISBN-13: | 9781484282328 |
| ISBN-10: | 1484282329 |
| Sprache: | Englisch |
| Ausstattung / Beilage: | Paperback |
| Einband: | Kartoniert / Broschiert |
| Autor: | L'Esteve, Ron |
| Auflage: | 1st ed. |
| Hersteller: |
Apress
Apress L.P. |
| Maße: | 254 x 178 x 27 mm |
| Von/Mit: | Ron L'Esteve |
| Erscheinungsdatum: | 14.07.2022 |
| Gewicht: | 0,909 kg |
Having several Azure Data, AI, and Lakehouse certifications under his belt, Ron has been a go-to technical advisor for some of the largest and most impactful Azure implementation projects on the planet. He has been responsible for scaling key data architectures, defining the road map and strategy for the future of data and business intelligence needs, and challenging customers to grow by thoroughly understanding the fluid business opportunities and enabling change by translating them into high-quality and sustainable technical solutionsthat solve the most complex challenges and promote digital innovation and transformation.
Ron is a gifted presenter and trainer, known for his innate ability to clearly articulate and explain complex topics to audiences of all skill levels. He applies a practical and business-oriented approach by taking transformational ideas from concept to scale. He is a true enabler of positive and impactful change by championing a growth mindset.
Shows data lakehouse design using Apache Spark on Azure
Teaches performance optimization techniques for Spark queries
Provides hands-on PySpark and Delta Lake examples for lakehouse ELT jobs
Part I: Getting Started.- Chapter 1: The Data Lakehouse Paradigm.- Part II: Data Platforms.- Chapter 2: Snowflake.- Chapter 3: Databricks.- Chapter 4: Synapse Analytics.- Part III: Apache Spark ELT.- Chapter 5: Pipelines and Jobs.- Chapter 6: Notebook Code.- Part IV: Delta Lake.-Chapter 7: Schema Evolution.- Chapter 8: Change Feed.- Chapter 9: Clones.- Chapter 10: Live Tables.- Chapter 11: Sharing.- Part V: Optimizing Performance.- Chapter 12: Dynamic Partition Pruning for Querying Star Schemas.- Chapter 13: Z-Ordering & Data Skipping.- Chapter 14: Adaptive Query Execution.- Chapter 15: ¿Bloom Filter Index.- Chapter 16: Hyperspace.- Part VI: Advanced Capabilities.- Chapter 17: Auto Loader.- Chapter 18: Python Wheels.- Chapter 19: Security & Controls.
| Erscheinungsjahr: | 2022 |
|---|---|
| Genre: | Informatik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Taschenbuch |
| Seiten: | 488 |
| Titelzusatz: | Building and Scaling Data Lakehouses on Azure with Delta Lake, Apache Spark, Databricks, Synapse Analytics, and Snowflake |
| Inhalt: |
xxii
465 S. 365 s/w Illustr. 465 p. 365 illus. |
| ISBN-13: | 9781484282328 |
| ISBN-10: | 1484282329 |
| Sprache: | Englisch |
| Ausstattung / Beilage: | Paperback |
| Einband: | Kartoniert / Broschiert |
| Autor: | L'Esteve, Ron |
| Auflage: | 1st ed. |
| Hersteller: |
Apress
Apress L.P. |
| Maße: | 254 x 178 x 27 mm |
| Von/Mit: | Ron L'Esteve |
| Erscheinungsdatum: | 14.07.2022 |
| Gewicht: | 0,909 kg |
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