This practical guide bridges the gap between general cloud computing architecture in Microsoft Azure and scientific computing for bioinformatics and genomics. You'll get a solid understanding of the architecture patterns and services that are offered in Azure and how they might be used in your bioinformatics practice. You'll get code examples that you can reuse for your specific needs. And you'll get plenty of concrete examples to illustrate how a given service is used in a bioinformatics context. You'll also get valuable advice on how to: Use enterprise platform services to easily scale your bioinformatics workloads Organize, query, and analyze genomic data at scale Build a genomics data lake and accompanying data warehouse Use Azure Machine Learning to scale your model training, track model performance, and deploy winning models Orchestrate and automate processing pipelines using Azure Data Factory and Databricks Cloudify your organization's existing bioinformatics pipelines by moving your workflows to Azure high-performance compute services And more

This practical guide bridges the gap between general cloud computing architecture in Microsoft Azure and scientific computing for bioinformatics and genomics. You'll get a solid understanding of the architecture patterns and services that are offered in Azure and how they might be used in your bioinformatics practice. You'll get code examples that you can reuse for your specific needs. And you'll get plenty of concrete examples to illustrate how a given service is used in a bioinformatics context. You'll also get valuable advice on how to: Use enterprise platform services to easily scale your bioinformatics workloads Organize, query, and analyze genomic data at scale Build a genomics data lake and accompanying data warehouse Use Azure Machine Learning to scale your model training, track model performance, and deploy winning models Orchestrate and automate processing pipelines using Azure Data Factory and Databricks Cloudify your organization's existing bioinformatics pipelines by moving your workflows to Azure high-performance compute services And more

If your organization plans to modernize services and move to the cloud from legacy software or a private cloud on premises, this book is for you. Software developers, solution architects, cloud engineers, and anybody interested in cloud technologies will learn fundamental concepts for cloud computing, migration, transformation, and development using Microsoft Azure. Author and Microsoft MVP Jonah Carrio Andersson guides you through cloud computing concepts and deployment models, the wide range of modern cloud technologies, application development with Azure, team collaboration services, security services, and cloud migration options in Microsoft Azure. You'll gain insight into the Microsoft Azure cloud services that you can apply in different business use cases, software development projects, and modern solutions in the cloud. You'll also become fluent with Azure cloud migration services, serverless computing technologies that help your development team work productively, Azure IoT, and Azure cognitive services that make your application smarter. This book also provides real-world advice and best practices based on the author's own Azure migration experience. Gain insight into which Azure cloud service best suits your company's particular needs Understand how to use Azure for different use cases and specific technical requirements Start developing cloud services, applications, and solutions in the Azure environment Learn how to migrate existing legacy applications to Microsoft Azure

Explore and work with various Microsoft Azure services for real-time Data Analytics KEY FEATURESÊ Understanding what Azure can do with your data Understanding the analytics services offered by Azure Understand how data can be transformed to generate more data Understand what is done after a Machine Learning model is builtÊ Go through some Data Analytics real-world use cases ÊÊ DESCRIPTIONÊ Data is the key input for Analytics. Building and implementing data platforms such as Data Lakes, modern Data Marts, and Analytics at scale require the right cloud platform that Azure provides through its services. The book starts by sharing how analytics has evolved and continues to evolve. Following the introduction, you will deep dive into ingestion technologies. You will learn about Data processing services in Azure. You will next learn about what is meant by a Data Lake and understand how Azure Data Lake Storage is used for analytical workloads. You will then learn about critical services that will provide actual Machine Learning capabilities in Azure. The book also talks about Azure Data Catalog for cataloging, Azure AD for Access Management, Web Apps and PowerApps for cloud web applications, Cognitive services for Speech, Vision, Search and Language, Azure VM for computing and Data Science VMs, Functions as serverless computing, Kubernetes and Containers as deployment options. Towards the end, the book discusses two use cases on Analytics. WHAT WILL YOU LEARNÊÊ Explore and work with various Azure services Orchestrate and ingest data using Azure Data Factory Learn how to use Azure Stream Analytics Get to know more about Synapse Analytics and its features Learn how to use Azure Analysis Services and its functionalities Ê WHO THIS BOOK IS FORÊ This book is for anyone who has basic to intermediate knowledge of cloud and analytics concepts and wants to use Microsoft Azure for Data Analytics. This book will also benefit Data Scientists who want to use Azure for Machine Learning. Ê TABLE OF CONTENTSÊÊ 1. Ê Data and its power 2. Ê Evolution of Analytics and its Types 3. Ê Internet of Things 4. Ê AI and ML 5. Ê Why cloud 6. Ê What are a data lake and a modern datamart 7. Ê Introduction to Azure services 8. Ê Types of data 9. Ê Azure Data Factory 10. Stream Analytics 11. Azure Data Lake Store and Azure Storage 12. Cosmos DB 13.Ê Synapse Analytics 14.Ê Azure Databricks 15.Ê Azure Analysis Services 16.Ê Power BI 17.Ê Azure Machine Learning 18.Ê Sample Architectures and synergies - Real-Time and Batch 19.Ê Azure Data Catalog 20.Ê Azure Active Directory 21.Ê Azure Webapps 22.Ê Power apps 23.Ê Time Series Insights 24.Ê Azure Cognitive Services 25.Ê Azure Logicapps 26.Ê Azure VM 27.Ê Azure Functions 28.Ê Azure Containers 29.Ê Azure KubernetesÊ Service 30.Ê Use Case 1 31.Ê Use Case 2

Advances in high-throughput biological methods have led to the publication of a large number of genome-wide studies in human and animal models. In this context, recent tools from bioinformatics and computational biology have been fundamental for the analysis of these genomic studies. The book Bioinformatics and Human Genomics Research provides updated and comprehensive information about multiple approaches of the application of bioinformatic tools to research in human genomics. It covers strategies analysis of genome-wide association studies, genome-wide expression studies and genome-wide DNA methylation, among other topics. It provides interesting strategies for data mining in human genomics, network analysis, prediction of binding sites for miRNAs and transcription factors, among other themes. Experts from all around the world in bioinformatics and human genomics have contributed chapters in this book. Readers will find this book as quite useful for their in silico explorations, which would contribute to a better and deeper understanding of multiple biological processes and of pathophysiology of many human diseases.

Perform genome analysis and sequencing of data with Amazon Web Services Genomics in the AWS Cloud: Analyzing Genetic Code Using Amazon Web Services enables a person who has moderate familiarity with AWS Cloud to perform full genome analysis and research. Using the information in this book, you'll be able to take a FASTQ file containing raw data from a lab or a BAM file from a service provider and perform genome analysis on it. You'll also be able to identify potentially pathogenic gene sequences. Get an introduction to Whole Genome Sequencing (WGS) Make sense of WGS on AWS Master AWS services for genome analysis Some key advantages of using AWS for genomic analysis is to help researchers utilize a wide choice of compute services that can process diverse datasets in analysis pipelines. Genomic sequencers that generate raw data files are located in labs on premises and AWS provides solutions to make it easy for customers to transfer these files to AWS reliably and securely. Storing Genomics and Medical (e.g., imaging) data at different stages requires enormous storage in a cost-effective manner. Amazon Simple Storage Service (Amazon S3), Amazon Glacier, and Amazon Elastics Block Store (Amazon EBS) provide the necessary solutions to securely store, manage, and scale genomic file storage. Moreover, the storage services can interface with various compute services from AWS to process these files. Whether you're just getting started or have already been analyzing genomics data using the AWS Cloud, this book provides you with the information you need in order to use AWS services and features in the ways that will make the most sense for your genomic research.

This book presents an improved design for service provisioning and allocation models that are validated through running genome sequence assembly tasks in a hybrid cloud environment. It proposes approaches for addressing scheduling and performance issues in big data analytics and showcases new algorithms for hybrid cloud scheduling. Scientific sectors such as bioinformatics, astronomy, high-energy physics, and Earth science are generating a tremendous flow of data, commonly known as big data. In the context of growing demand for big data analytics, cloud computing offers an ideal platform for processing big data tasks due to its flexible scalability and adaptability. However, there are numerous problems associated with the current service provisioning and allocation models, such as inefficient scheduling algorithms, overloaded memory overheads, excessive node delays and improper error handling of tasks, all of which need to be addressed to enhance the performance of big data analytics.