Swapnil Mule 
Ultimate Azure Synapse Analytics [EPUB ebook] 
Unlock the Full Potential of Azure Synapse Analytics to Seamlessly Integrate, Analyze, and Optimize Complex Data for Enhanced Business Insights and Decision-Making (English Edition)

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Empower Your Data Insights with Azure Synapse Analytics
Key Features
● Leverage Azure Synapse Analytics for data warehousing, big data analytics, and machine learning in one environment.
● Integrate with Azure services like Azure Data Lake Storage and Azure Machine Learning to enhance analytics.
● Gain insights from real-world examples and best practices to solve complex data challenges.
Book Description
Unlock the full potential of Azure Synapse Analytics with 
Ultimate Azure Synapse Analytics  your definitive roadmap to mastering the art of data analytics in the cloud era. From the foundational concepts to advanced techniques, each chapter offers practical insights and hands-on tutorials to streamline your data workflows and drive actionable insights.
Discover how Azure Synapse Analytics revolutionizes data processing and integration, empowering you to harness the vast capabilities of the Azure ecosystem. Seamlessly transition from traditional data warehousing to cutting-edge big data analytics, leveraging serverless and dedicated resources for optimal performance. Dive deep into Synapse SQL, explore advanced data engineering with Apache Spark, and delve into machine learning and Dev Ops practices to stay ahead in today’s data-driven landscape.
Whether you’re seeking to optimize performance, ensure compliance, or facilitate seamless migration, this book provides the expertise needed to excel in your role. Gain valuable insights into industry best practices, enhance your data engineering skills, and drive innovation within your organization.
What you will learn
● Understand the significance of Azure Synapse Analytics in modern data analytics.
● Learn to set up and configure your Synapse workspace for efficient data processing.
● Dive into Synapse SQL and discover techniques for data exploration and analysis.
● Master advanced techniques for seamless data integration into Azure Synapse Analytics.
● Explore big data engineering concepts and leverage Apache Spark for scalable data processing.
● Discover how to implement machine learning models and algorithms using Synapse Analytics.
● Ensure data security and regulatory compliance with effective security measures in Azure Synapse Analytics.
● Optimize performance and efficiency through performance tuning strategies and optimization techniques.
Table of Contents
1. The World of Azure Synapse Analytics
2. Setting Up the Synapse Workspace
3. Synapse SQL and Data Exploration
4. Data Integration Technique
5. Big Data Engineering with Apache Spark
6. Machine Learning with Synapse
7. Implementing Security and Compliance
8. Performance Tuning and Optimization
9. Dev Ops for Data Engineering
10. Ensuring Implementation Success and Effective Migration
      
Index
About the Authors
Swapnil Mule  a distinguished data engineer, graduated as a top-ranking student from MIT, Pune in 2015. Throughout his academic journey, he consistently showcased exceptional prowess, earning accolades for his remarkable achievements in the field of engineering.
With over a decade of experience, Swapnil specializes in spearheading data engineering projects, collaborating with global banks and renowned brands such as BNY Mellon, Microsoft, and Verition Funds. His proficiency spans various technical domains including data modeling, data orchestration design, data analysis, and data warehousing. Equipped with certifications in SQL Server, AWS, and SAFe® Agile methodologies, Swapnil’s passion for data engineering and unwavering commitment to excellence position him as a driving force in innovation, delivering impactful solutions to clients worldwide.
 

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Sprache Englisch ● Format EPUB ● Seiten 592 ● ISBN 9788197256233 ● Dateigröße 80.8 MB ● Verlag Orange Education Pvt Ltd ● Erscheinungsjahr 2024 ● herunterladbar 24 Monate ● Währung EUR ● ID 9505321 ● Kopierschutz Adobe DRM
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