Database design and theory

Data is at the center of many challenges in system design today.

Master the skills necessary to hire and manage a team of highly skilled individuals to design, build, and implement applications and systems based on advanced analytics and AIKey FeaturesLearn to create an operationally effective advanced analytics team in a corporate environmentSelect and undertake projects that have a high probability of success and deliver the improved top and bottom-line resultsUnderstand how to create relationships with executives, senior managers, peers, and subject matter experts that lead to team collaboration, increased funding, and long-term success for you and your teamBook DescriptionIn Building Analytics Teams, John K.

Databases for Data-Centric Geotechnics forms a definitive reference and guide to databases in geotechnical and rock engineering, to enhance decision-making in geotechnical practice using data-driven methods.

The immense increase on the size and type of real time data generated across various edge computing platform results in unstructured databases and data silos.

Describes the underlying theory as well as algorithms, optimisation ideas and implementation details.

Big data involves the use of sophisticated analytics to make decisions based on large-scale data inputs.

Applied Analysis of Composite Media: Analytical and Computational Approaches presents formulas and techniques that can used to study 2D and 3D problems in composites and random porous media.

Data fabric, data lakehouse, and data mesh have recently appeared as viable alternatives to the modern data warehouse.

Supercharge your skills for developing powerful deep learning models and distributing them at scale efficiently using cloud servicesKey FeaturesUnderstand how to execute a deep learning project effectively using various tools availableLearn how to develop PyTorch and TensorFlow models at scale using Amazon Web ServicesExplore effective solutions to various difficulties that arise from model deploymentBook DescriptionMachine learning engineers, deep learning specialists, and data engineers encounter various problems when moving deep learning models to a production environment.

Perform Interactive Data Analysis with Smarter Visualizations and Support your Enterprise-wide Analytical Needs Key Features Get a practical demonstration of discovering data for sales, human resources, and more using Qlik Sense Create dynamic dashboards for business intelligence and predictive analytics Create and collaborate comprehensive analytical solutions using Rattle and Qlik Sense Book Description Qlik Sense is powerful and creative visual analytics software that allows users to discover data, explore it, and dig out meaningful insights in order to make a profit and make decisions for your business.

While tools for analyzing streaming and real-time data are gaining adoption, the ability to visualize these data types has yet to catch up.

A practical, step-by-step guide to using Microsoft's AutoML technology on the Azure Machine Learning service for developers and data scientists working with the Python programming languageKey FeaturesCreate, deploy, productionalize, and scale automated machine learning solutions on Microsoft AzureImprove the accuracy of your ML models through automatic data featurization and model trainingIncrease productivity in your organization by using artificial intelligence to solve common problemsBook DescriptionAutomated Machine Learning with Microsoft Azure will teach you how to build high-performing, accurate machine learning models in record time.

Get up and running with Oracle's premium cloud blockchain services and build distributed blockchain apps with easeKey FeaturesDiscover Hyperledger Fabric and its components, features, qualifiers, and architectureGet familiar with the Oracle Blockchain Platform and its unique featuresBuild Hyperledger Fabric-based business networks with Oracle's premium blockchain cloud serviceBook DescriptionHyperledger Fabric empowers enterprises to scale out in an unprecedented way, allowing organizations to build and manage blockchain business networks.

CouchDB is a new breed of database for the Internet, geared to meet the needs of todays dynamic web applications.

The field of artificial intelligence, data science, and analytics is crippling itself.

Learn quick and effective techniques to get up and running with building blockchain including Ethereum and Hyperledger Fabric.

Python is a first-class tool for many researchers, primarily because of its libraries for storing, manipulating, and gaining insight from data.

Pulling aside the curtain of 'Big Data' buzz, this book introduces C-suite and other non-technical senior leaders to the essentials of obtaining and maintaining accurate, reliable data, especially for decision-making purposes.

For the past decade, e-mail has been the preferred method of internal communication in libraries.

Biomedical Robots and Devices in Healthcare: Opportunities and Challenges for Future Applications explores recent advances and challenges involved in using these techniques in healthcare and biomedical engineering, offering insights and guidance to researchers, professionals, and graduate students interested in this area.

Present energy systems are undergoing a radical transformation, driven by the urgent need to address the climate change crisis.

Understand how blockchain works and explore a variety of strategies to implement it in your organization effectivelyKey FeaturesBecome familiar with business challenges faced by companies when using blockchainDiscover how companies implement blockchain to monetize and secure their dataStudy real-world examples to understand blockchain and its use in organizationsBook DescriptionIn addition to cryptocurrencies, blockchain-based apps are being developed in different industries such as banking, supply chain, and healthcare to achieve digital transformation and enhance user experience.

Data mining techniques and algorithms are extensively used to build real-world applications.

Explore the different data mining techniques using the libraries and packages offered by PythonKey FeaturesGrasp the basics of data loading, cleaning, analysis, and visualizationUse the popular Python libraries such as NumPy, pandas, matplotlib, and scikit-learn for data miningYour one-stop guide to build efficient data mining pipelines without going into too much theoryBook DescriptionData mining is a necessary and predictable response to the dawn of the information age.

Discover the power of location data to build effective, intelligent data models with Geospatial ecosystemsKey FeaturesManipulate location-based data and create intelligent geospatial data modelsBuild effective location recommendation systems used by popular companies such as UberA hands-on guide to help you consume spatial data and parallelize GIS operations effectivelyBook DescriptionData scientists, who have access to vast data streams, are a bit myopic when it comes to intrinsic and extrinsic location-based data and are missing out on the intelligence it can provide to their models.

Predictive Filtering for Microsatellite Control Systems introduces technological design, modeling, stability analysis, predictive filtering, state estimation problem and real-time operation of spacecraft control systems in aerospace engineering.

Theoretical and Applied Solutions in Multi Scale MappingUsers have come to expect instant access to up-to-date geographical information, with global coverage--presented at widely varying levels of detail, as digital and paper products; customisable data that can readily combined with other geographic information.

Build efficient data flow and machine learning programs with this flexible, multi-functional open-source cluster-computing frameworkKey FeaturesMaster the art of real-time big data processing and machine learning Explore a wide range of use-cases to analyze large data Discover ways to optimize your work by using many features of Spark 2.

Data modeling is the single most overlooked feature in Power BI Desktop, yet it's what sets Power BI apart from other tools on the market.

Solve real-world data problems and create data-driven workflows for easy data movement and processing at scale with Azure Data FactoryKey FeaturesLearn how to load and transform data from various sources, both on-premises and on cloudUse Azure Data Factory's visual environment to build and manage hybrid ETL pipelinesDiscover how to prepare, transform, process, and enrich data to generate key insightsBook DescriptionAzure Data Factory (ADF) is a modern data integration tool available on Microsoft Azure.

Create dynamic dashboards to perform interactive analytics for business intelligence operationsKey FeaturesExplore newly added features in Qlik SenseDiscover best practices to work with data using Qlik SenseLearn to implement advanced functions for better data insightBook DescriptionQlik Sense allows you to explore simple and complex data to reveal hidden insight and data relationships that help you make quality decisions for overall productivity.

Use PySpark to easily crush messy data at-scale and discover proven techniques to create testable, immutable, and easily parallelizable Spark jobsKey FeaturesWork with large amounts of agile data using distributed datasets and in-memory cachingSource data from all popular data hosting platforms, such as HDFS, Hive, JSON, and S3Employ the easy-to-use PySpark API to deploy big data Analytics for productionBook DescriptionApache Spark is an open source parallel-processing framework that has been around for quite some time now.

Mine Ether, deploy smart contracts, tokens, and ICOs, and manage security vulnerabilities of EthereumKey FeaturesBuild end-to-end decentralized Ethereum apps using Truffle, Web3, and SolidityExplore various solution-based recipes to build smart contracts and foolproof decentralized applicationsDevelop decentralized marketplaces from scratch, build wallets, and manage transactionsBook DescriptionEthereum and Blockchain will change the way software is built for business transactions.

Process Algebra is a formal description technique for complex computer systems, especially those involving communicating, concurrently executing components.

The widespread adoption of AI and machine learning is revolutionizing many industries today.

Supervised and unsupervised machine learning made easy in Scala with this quick-start guide.

Solve all big data problems by learning how to create efficient data modelsKey FeaturesCreate effective models that get the most out of big dataApply your knowledge to datasets from Twitter and weather data to learn big dataTackle different data modeling challenges with expert techniques presented in this bookBook DescriptionModeling and managing data is a central focus of all big data projects.

Over 75 practical recipes to confidently design an efficient virtual datacenter with VMware vSphere 6.

Get unique insights from your data by combining the power of SQL Server, R and PythonKey FeaturesUse the features of SQL Server 2017 to implement the data science project life cycleLeverage the power of R and Python to design and develop efficient data modelsfind unique insights from your data with powerful techniques for data preprocessing and analysisBook DescriptionSQL Server only started to fully support data science with its two most recent editions.

Over the last decade, advances in the semiconductor fabrication process have led to the realization of true system-on-a-chip devices.

Combine popular machine learning techniques to create ensemble models using PythonKey FeaturesImplement ensemble models using algorithms such as random forests and AdaBoostApply boosting, bagging, and stacking ensemble methods to improve the prediction accuracy of your model Explore real-world data sets and practical examples coded in scikit-learn and KerasBook DescriptionEnsembling is a technique of combining two or more similar or dissimilar machine learning algorithms to create a model that delivers superior predictive power.

What makes this book different from others on database design?

Discover the power of location data to build effective, intelligent data models with Geospatial ecosystemsKey FeaturesManipulate location-based data and create intelligent geospatial data modelsBuild effective location recommendation systems used by popular companies such as UberA hands-on guide to help you consume spatial data and parallelize GIS operations effectivelyBook DescriptionData scientists, who have access to vast data streams, are a bit myopic when it comes to intrinsic and extrinsic location-based data and are missing out on the intelligence it can provide to their models.

A Tour of Data Science: Learn R and Python in Parallel covers the fundamentals of data science, including programming, statistics, optimization, and machine learning in a single short book.

Learn through hands-on exercises covering a variety of topics including data connections, analytics, and dashboards to effectively prepare for the Tableau Desktop Certified Associate examKey FeaturesPrepare for the Tableau Desktop Certified Associate exam with the help of tips and techniques shared by expertsImplement Tableau's advanced analytical capabilities such as forecastingDelve into advanced Tableau features and explore best practices for building dashboardsBook DescriptionThe Tableau Desktop Certified Associate exam measures your knowledge of Tableau Desktop and your ability to work with data and data visualization techniques.

Databases have become an integral part of modern-day life.

Vom kleinen mittelständischen Unternehmen bis hin zum Großkonzern: Wer eine App sucht, über die man auf unterschiedlichen Plattformen ausgesuchte Datenbestände abrufen kann, der stößt unweigerlich auf "FileMaker".

Think about your data intelligently and ask the right questionsKey FeaturesMaster data cleaning techniques necessary to perform real-world data science and machine learning tasksSpot common problems with dirty data and develop flexible solutions from first principlesTest and refine your newly acquired skills through detailed exercises at the end of each chapterBook DescriptionData cleaning is the all-important first step to successful data science, data analysis, and machine learning.

Data projects are an intrinsic part of an organizations technical ecosystem, but data engineers in many companies continue to work on problems that others have already solved.