“…In this Analytic Data Storage in Hadoop training course, expert author Ryan Blue will teach you about typical storage and ingest patterns in Hadoop. This course is designed for users that are already familiar with Hadoop. …”

“…You'll also learn a variety of ways to ingest data from various sources and orchestrate the data using transformation techniques offered by Azure Synapse. …”

“…You'll work with use cases and practical examples to be able to ingest, process, analyze, and stream real-time data in no time…”

Tabla de Contenidos: “…Cover -- Half-Title Page -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- Acknowledgment -- 1 Deep Dive Into Blockchain Technology: Characteristics, Security and Privacy Issues, Challenges, and Future Research Directions -- 1.1 Introduction -- 1.2 Blockchain Preliminaries -- 1.2.1 Functioning of Blockchain -- 1.2.2 Design of Blockchain -- 1.2.3 Blockchain Elements -- 1.3 Key Technologies of Blockchain -- 1.3.1 Distributed Ledger -- 1.3.2 Cryptography -- 1.3.3 Consensus -- 1.3.4 Smart Contracts -- 1.3.5 Benchmarks -- 1.4 Consensus Algorithms of Blockchain -- 1.4.1 Proof of Work (PoW) -- 1.4.2 Proof of Stake (PoS) -- 1.4.3 BFT-Based Consensus Algorithms -- 1.4.4 Practical Byzantine Fault Tolerance (PBFT) -- 1.4.5 Sleepy Consensus -- 1.4.6 Proof of Elapsed Time (PoET) -- 1.4.7 Proof of Authority (PoA) -- 1.4.8 Proof of Reputation (PoR) -- 1.4.9 Deputized Proof of Stake (DPoS) -- 1.4.10 SCP Design -- 1.5 Internet of Things and Blockchain -- 1.5.1 Internet of Things -- 1.5.2 IoT Blockchain -- 1.5.3 Up-to-Date Tendency in IoT Blockchain Progress -- 1.6 Applications of Blockchain in Smart City -- 1.6.1 Digital Identity -- 1.6.2 Security of Private Information -- 1.6.3 Data Storing, Energy Ingesting, Hybrid Development -- 1.6.4 Citizens Plus Government Frame -- 1.6.5 Vehicle-Oriented Blockchain Appliances in Smart Cities -- 1.6.6 Financial Applications -- 1.7 Security and Privacy Properties of Blockchain -- 1.7.1 Security and Privacy Necessities of Online Business Transaction -- 1.7.2 Secrecy of Connections and Data Privacy -- 1.8 Privacy and Security Practices Employed in Blockchain -- 1.8.1 Mixing -- 1.8.2 Anonymous Signatures -- 1.8.3 Homomorphic Encryption (HE) -- 1.8.4 Attribute-Based Encryption (ABE) -- 1.8.5 Secure Multi-Party Computation (MPC) -- 1.8.6 Non-Interactive Zero-Knowledge (NIZK)…”

Tabla de Contenidos: “…4.2.4.9 The Need For Prescriptive Analytics in Maintenance: A Case Study -- 4.3 Big Data Analytics Methods -- 4.3.1 Defining Big Data Analytics -- 4.3.2 Defining Big Data Via the Three Vs -- 4.3.2.1 Data Volume as a Defining Attribute of Big Data -- 4.3.2.2 Data Type Variety as a Defining Attribute of Big Data -- 4.3.2.3 Data Feed Velocity as a Defining Attribute of Big Data -- 4.3.3 Text Analytics -- 4.3.4 Audio Analytics -- 4.3.5 Video Analytics -- 4.3.6 Social Media Analytics -- 4.4 Maintenance Strategies with Big Data Analytics -- 4.5 Data-Driven and Model-Driven Approaches -- 4.5.1 Data Mining and Knowledge Discovery -- 4.6 Maintenance Descriptive Analytics -- 4.7 Maintenance Diagnostic Analytics -- 4.8 Maintenance Predictive Analytics -- 4.9 Maintenance Prescriptive Analytics -- 4.10 Big Data Analytics Methods -- 4.10.1 Text Analytics -- 4.10.2 Audio Analytics -- 4.10.3 Video Analytics -- 4.10.4 Social Media Analytics -- 4.11 Big Data Management and Governance -- 4.12 Big Data Access and Analysis -- 4.13 Big Data Visualisation -- 4.14 Big Data Ingestion -- 4.15 Big Data Cluster Management -- 4.16 Big Data Distributions -- 4.17 Data Governance -- 4.18 Data Access -- 4.19 Data Analysis -- 4.20 Bid Data File System -- 4.20.1 Quantcast File System -- 4.20.2 Hadoop Distributed File System -- 4.20.3 Cassandra File System (CFS) -- 4.20.4 GlusterFS -- 4.20.5 Lustre -- 4.20.6 Parallel Virtual File System -- 4.20.7 Orange File System (OrangeFS) -- 4.20.8 BeeGFS -- 4.20.9 MapR-FS -- 4.20.9.1 Kudu -- References -- Chapter 5 Data-Driven Decision-Making -- 5.1 Data for Decision-Making -- 5.1.1 Data-Driven Decision-Making -- 5.1.2 The Process of Data-Driven Decision-Making -- 5.1.3 The Context of Data-Driven Decision-Making -- 5.1.4 The Importance of Data-Driven Decision-Making -- 5.1.5 Common Challenges of Data-Driven Decision-Making…”

“…In ancient times, people benefited from ingesting different parts of various weeds (root, stem, shoot, leaf, flower, fruit, seed, etc.) to maintain a healthy life. …”

“…You’ll also learn how the ODH helps you leverage the investment in your data lake (or swamp), so that the data trapped there can finally be ingested, processed, and provisioned. With this ebook, you’ll learn how an ODH: Allows you to focus on categorizing data for easy and fast retrieval Provides flexible storage models, indexing support, query capabilities, security, and a governance framework Delivers flexible storage models; support for indexing, scripting, and automation; query capabilities; transactional integrity; and security Includes a governance model to help you access, ingest, harmonize, materialize, provision, and consume data…”

“…Cumulus, a free and open source framework, supports this vision via configurable workflows to ingest, process, archive, manage, and distribute NASA's Earth imagery. …”

Tabla de Contenidos: “…El centauro de la hipálage doble / Comicidad y parodia en la comedia burlesca del Siglo de Oro: El Hamete de Toledo, de tres ingenios / Amor y subjetividad en La Diana de Montemayor / Acerca de la Diana de Montemayor / Sor Juana, Pfandl, y la mujer masculina / Divulgación de un género literario en el Barroco / De los Hospitales de amor al Hospital de negios (de Boscán a Hurtado de Toledo) / El teatro barroco como juego La villana de Vallecas de Tirso de Molina / La yedra en la poesía de Francisco de la Torre: simbologia y autorepresentación / Sobre poética y retórica: la relación entre imitación, género y estilo en la obra de Francisco de Trillo y Figueroa / Abre el ojo y El amor al uso: marcas de género y configuración del «enredo» en Rojas Zorrilla y Antonio de Solís / Ingestión y expulsión - el problema de la identidad en el Lazarillo de Tormes / Vicente de la Rosa (Quijote I, 51) y el problema epistemológico de la verosimilitud / Agudeza emblemática en H. …”

“…You'll learn how to scale your processing capabilities across multiple machines while ingesting data from any source--whether that's Hadoop clusters, cloud data storage, or local data files. …”

“…TensorFlow has evolved to be an ecosystem that supports a machine learning workflow through ingesting and transforming data, building models, monitoring, and productionization. …”

“…You develop a working knowledge with nineteen tutorials that cover each and every essential, including: * setting up your system and understanding the interface * ingesting and organizing your material including drive-based and disc-based camera data* slicing, dicing, and organizing clips * editing to build and trim a sequence of shots <BR i…”

“…This report explains how operational databases enable IoT applications to fulfill four data management capabilities: Fast Ingest: In-memory performance and horizontal scalability that provide a single ingestion point for high-velocity data feeds Explore and Analyze: Real-time access to applications and querying engines Act: The ability to trigger events and make decisions based on inbound streams Export: The ability to export accumulated, filtered, enriched, or augmented data to downstream systems About the author Ryan Betts, VoltDB CTO, is one of the company’s founding developers. …”

“…"Everyday, Hulu ingests 100 terabytes of user level app interaction data. …”

“…Working through practice problems helps students better ingest and retain lesson content, creating a solid foundation to build on for future success. …”

“…By the end of this course, you will learn how to ingest, process, and export data using Databricks and HDInsight. …”

“…As you progress, you will learn how to ingest, cleanse, and transform your data into stunning visualizations, reports, and dashboards that speak to business decision-makers. …”

“…You want to effectively approach the data lifecycle from ingestion to labeling to solving problems with machine learning. …”

“…The book gradually takes you through operations such as data ingestion, data cleansing, transformation, and integrating data with other sources. …”