Learn how to gain insights from your data as well as machine learning and become a presentation pro who can create interactive dashboards Key Features Enhance your presentation skills by implementing engaging data storytelling and visualization techniques Learn the basics of machine learning and easily apply machine learning models to your data Improve productivity by automating your data processes Book DescriptionData Analytics Made Easy is an accessible beginner's guide for anyone working with data. The book interweaves four key elements: Data visualizations and storytelling - Tired of people not listening to you and ignoring your results? Don't worry; chapters 7 and 8 show you how to enhance your presentations and engage with your managers and co-workers. Learn to create focused content with a well-structured story behind it to captivate your audience. Automating your data workflows - Improve your productivity by automating your data analysis. This book introduces you to the open-source platform, KNIME Analytics Platform. You'll see how to use this no-code and free-to-use software to create a KNIME workflow of your data processes just by clicking and dragging components. Machine learning - Data Analytics Made Easy describes popular machine learning approaches in a simplified and visual way before implementing these machine learning models using KNIME. You'll not only be able to understand data scientists' machine learning models; you'll be able to challenge them and build your own. Creating interactive dashboards - Follow the book's simple methodology to create professional-looking dashboards using Microsoft Power BI, giving users the capability to slice and dice data and drill down into the results. What you will learn Understand the potential of data and its impact on your business Import, clean, transform, combine data feeds, and automate your processes Influence business decisions by learning to create engaging presentations Build real-world models to improve profitability, create customer segmentation, automate and improve data reporting, and more Create professional-looking and business-centric visuals and dashboards Open the lid on the black box of AI and learn about and implement supervised and unsupervised machine learning models Who this book is forThis book is for beginners who work with data and those who need to know how to interpret their business/customer data. The book also covers the high-level concepts of data workflows, machine learning, data storytelling, and visualizations, which are useful for managers. No previous math, statistics, or computer science knowledge is required.

An expert guide to implementing fast, secure, and scalable decentralized applications that work with thousands of users in real time Key Features Implement advanced features of the Ethereum network to build powerful decentralized applications Build smart contracts on different domains using the programming techniques of Solidity and Vyper Explore the architecture of Ethereum network to understand advanced use cases of blockchain development Book DescriptionEthereum is one of the commonly used platforms for building blockchain applications. It's a decentralized platform for applications that can run exactly as programmed without being affected by fraud, censorship, or third-party interference. This book will give you a deep understanding of how blockchain works so that you can discover the entire ecosystem, core components, and its implementations. You will get started by understanding how to configure and work with various Ethereum protocols for developing dApps. Next, you will learn to code and create powerful smart contracts that scale with Solidity and Vyper. You will then explore the building blocks of the dApps architecture, and gain insights on how to create your own dApp through a variety of real-world examples. The book will even guide you on how to deploy your dApps on multiple Ethereum instances with the required best practices and techniques. The next few chapters will delve into advanced topics such as, building advanced smart contracts and multi-page frontends using Ethereum blockchain. You will also focus on implementing machine learning techniques to build decentralized autonomous applications, in addition to covering several use cases across a variety of domains such as, social media and e-commerce. By the end of this book, you will have the expertise you need to build decentralized autonomous applications confidently. What you will learn Apply scalability solutions on dApps with Plasma and state channels Understand the important metrics of blockchain for analyzing and determining its state Develop a decentralized web application using React.js and Node.js Create oracles with Node.js to provide external data to smart contracts Get to grips with using Etherscan and block explorers for various transactions Explore web3.js, Solidity, and Vyper for dApps communication Deploy apps with multiple Ethereum instances including TestRPC, private chain, test chain, and mainnet Who this book is forThis book is for anyone who wants to build fast, highly secure, and transactional decentralized applications. If you are an Ethereum developer looking to perfect your existing skills in building powerful blockchain applications, then this book is for you. Basic knowledge of Ethereum and blockchain is necessary to understand the concepts covered in this book.

Understand, design, and create cognitive applications using Watson's suite of APIs. Key Features Develop your skills and work with IBM Watson APIs to build efficient and powerful cognitive apps Learn how to build smart apps to carry out different sets of activities using real-world use cases Get well versed with the best practices of IBM Watson and implement them in your daily work Book DescriptionCognitive computing is rapidly infusing every aspect of our lives riding on three important fields: data science, machine learning (ML), and artificial intelligence (AI). It allows computing systems to learn and keep on improving as the amount of data in the system grows. This book introduces readers to a whole new paradigm of computing - a paradigm that is totally different from the conventional computing of the Information Age. You will learn the concepts of ML, deep learning (DL), neural networks, and AI through the set of APIs provided by IBM Watson. This book will help you build your own applications to understand, plan, and solve problems, and analyze them as per your needs. You will learn about various domains of cognitive computing, such as NLP, voice processing, computer vision, emotion analytics, and conversational systems, using different IBM Watson APIs. From this, the reader will learn what ML is, and what goes on in the background to make computers "do their magic," as well as where these concepts have been applied. Having achieved this, the readers will then be able to embark on their journey of learning, researching, and applying the concept in their respective fields. What you will learn Get well versed with the APIs provided by IBM Watson on IBM Cloud Learn ML, AI, cognitive computing, and neural network principles Implement smart applications in fields such as healthcare, entertainment, security, and more Understand unstructured content using cognitive metadata with the help of Natural Language Understanding Use Watson's APIs to create real-life applications to realize their capabilities Delve into various domains of cognitive computing, such as media analytics, embedded deep learning, computer vision, and more Who this book is forThis book is for beginners and novices; having some knowledge about artificial intelligence and deep learning is an advantage, but not a prerequisite to benefit from this book. We explain the concept of deep learning and artificial intelligence through the set of tools IBM Watson provides.

Get to grips with solving real-world NLP problems, such as dependency parsing, information extraction, topic modeling, and text data visualization Key Features Analyze varying complexities of text using popular Python packages such as NLTK, spaCy, sklearn, and gensim Implement common and not-so-common linguistic processing tasks using Python libraries Overcome the common challenges faced while implementing NLP pipelines Book DescriptionPython is the most widely used language for natural language processing (NLP) thanks to its extensive tools and libraries for analyzing text and extracting computer-usable data. This book will take you through a range of techniques for text processing, from basics such as parsing the parts of speech to complex topics such as topic modeling, text classification, and visualization. Starting with an overview of NLP, the book presents recipes for dividing text into sentences, stemming and lemmatization, removing stopwords, and parts of speech tagging to help you to prepare your data. You'll then learn ways of extracting and representing grammatical information, such as dependency parsing and anaphora resolution, discover different ways of representing the semantics using bag-of-words, TF-IDF, word embeddings, and BERT, and develop skills for text classification using keywords, SVMs, LSTMs, and other techniques. As you advance, you'll also see how to extract information from text, implement unsupervised and supervised techniques for topic modeling, and perform topic modeling of short texts, such as tweets. Additionally, the book shows you how to develop chatbots using NLTK and Rasa and visualize text data. By the end of this NLP book, you'll have developed the skills to use a powerful set of tools for text processing. What you will learn Become well-versed with basic and advanced NLP techniques in Python Represent grammatical information in text using spaCy, and semantic information using bag-of-words, TF-IDF, and word embeddings Perform text classification using different methods, including SVMs and LSTMs Explore different techniques for topic modeling such as K-means, LDA, NMF, and BERT Work with visualization techniques such as NER and word clouds for different NLP tools Build a basic chatbot using NLTK and Rasa Extract information from text using regular expression techniques and statistical and deep learning tools Who this book is forThis book is for data scientists and professionals who want to learn how to work with text. Intermediate knowledge of Python will help you to make the most out of this book. If you are an NLP practitioner, this book will serve as a code reference when working on your projects.

Quickly build and deploy machine learning models without managing infrastructure, and improve productivity using Amazon SageMaker's capabilities such as Amazon SageMaker Studio, Autopilot, Experiments, Debugger, and Model Monitor Key Features Build, train, and deploy machine learning models quickly using Amazon SageMaker Analyze, detect, and receive alerts relating to various business problems using machine learning algorithms and techniques Improve productivity by training and fine-tuning machine learning models in production Book DescriptionAmazon SageMaker enables you to quickly build, train, and deploy machine learning (ML) models at scale, without managing any infrastructure. It helps you focus on the ML problem at hand and deploy high-quality models by removing the heavy lifting typically involved in each step of the ML process. This book is a comprehensive guide for data scientists and ML developers who want to learn the ins and outs of Amazon SageMaker. You'll understand how to use various modules of SageMaker as a single toolset to solve the challenges faced in ML. As you progress, you'll cover features such as AutoML, built-in algorithms and frameworks, and the option for writing your own code and algorithms to build ML models. Later, the book will show you how to integrate Amazon SageMaker with popular deep learning libraries such as TensorFlow and PyTorch to increase the capabilities of existing models. You'll also learn to get the models to production faster with minimum effort and at a lower cost. Finally, you'll explore how to use Amazon SageMaker Debugger to analyze, detect, and highlight problems to understand the current model state and improve model accuracy. By the end of this Amazon book, you'll be able to use Amazon SageMaker on the full spectrum of ML workflows, from experimentation, training, and monitoring to scaling, deployment, and automation. What you will learn Create and automate end-to-end machine learning workflows on Amazon Web Services (AWS) Become well-versed with data annotation and preparation techniques Use AutoML features to build and train machine learning models with AutoPilot Create models using built-in algorithms and frameworks and your own code Train computer vision and NLP models using real-world examples Cover training techniques for scaling, model optimization, model debugging, and cost optimization Automate deployment tasks in a variety of configurations using SDK and several automation tools Who this book is forThis book is for software engineers, machine learning developers, data scientists, and AWS users who are new to using Amazon SageMaker and want to build high-quality machine learning models without worrying about infrastructure. Knowledge of AWS basics is required to grasp the concepts covered in this book more effectively. Some understanding of machine learning concepts and the Python programming language will also be beneficial.

Get to grips with pandas by working with real datasets and master data discovery, data manipulation, data preparation, and handling data for analytical tasks Key Features Perform efficient data analysis and manipulation tasks using pandas 1.x Apply pandas to different real-world domains with the help of step-by-step examples Make the most of pandas as an effective data exploration tool Book DescriptionExtracting valuable business insights is no longer a 'nice-to-have', but an essential skill for anyone who handles data in their enterprise. Hands-On Data Analysis with Pandas is here to help beginners and those who are migrating their skills into data science get up to speed in no time. This book will show you how to analyze your data, get started with machine learning, and work effectively with the Python libraries often used for data science, such as pandas, NumPy, matplotlib, seaborn, and scikit-learn. Using real-world datasets, you will learn how to use the pandas library to perform data wrangling to reshape, clean, and aggregate your data. Then, you will learn how to conduct exploratory data analysis by calculating summary statistics and visualizing the data to find patterns. In the concluding chapters, you will explore some applications of anomaly detection, regression, clustering, and classification using scikit-learn to make predictions based on past data. This updated edition will equip you with the skills you need to use pandas 1.x to efficiently perform various data manipulation tasks, reliably reproduce analyses, and visualize your data for effective decision making - valuable knowledge that can be applied across multiple domains. What you will learn Understand how data analysts and scientists gather and analyze data Perform data analysis and data wrangling using Python Combine, group, and aggregate data from multiple sources Create data visualizations with pandas, matplotlib, and seaborn Apply machine learning algorithms to identify patterns and make predictions Use Python data science libraries to analyze real-world datasets Solve common data representation and analysis problems using pandas Build Python scripts, modules, and packages for reusable analysis code Who this book is forThis book is for data science beginners, data analysts, and Python developers who want to explore each stage of data analysis and scientific computing using a wide range of datasets. Data scientists looking to implement pandas in their machine learning workflow will also find plenty of valuable know-how as they progress. You'll find it easier to follow along with this book if you have a working knowledge of the Python programming language, but a Python crash-course tutorial is provided in the code bundle for anyone who needs a refresher.

Quickly build and deploy massive data pipelines and improve productivity using Azure Databricks Key Features Get to grips with the distributed training and deployment of machine learning and deep learning models Learn how ETLs are integrated with Azure Data Factory and Delta Lake Explore deep learning and machine learning models in a distributed computing infrastructure Book DescriptionMicrosoft Azure Databricks helps you to harness the power of distributed computing and apply it to create robust data pipelines, along with training and deploying machine learning and deep learning models. Databricks' advanced features enable developers to process, transform, and explore data. Distributed Data Systems with Azure Databricks will help you to put your knowledge of Databricks to work to create big data pipelines. The book provides a hands-on approach to implementing Azure Databricks and its associated methodologies that will make you productive in no time. Complete with detailed explanations of essential concepts, practical examples, and self-assessment questions, you'll begin with a quick introduction to Databricks core functionalities, before performing distributed model training and inference using TensorFlow and Spark MLlib. As you advance, you'll explore MLflow Model Serving on Azure Databricks and implement distributed training pipelines using HorovodRunner in Databricks. Finally, you'll discover how to transform, use, and obtain insights from massive amounts of data to train predictive models and create entire fully working data pipelines. By the end of this MS Azure book, you'll have gained a solid understanding of how to work with Databricks to create and manage an entire big data pipeline. What you will learn Create ETLs for big data in Azure Databricks Train, manage, and deploy machine learning and deep learning models Integrate Databricks with Azure Data Factory for extract, transform, load (ETL) pipeline creation Discover how to use Horovod for distributed deep learning Find out how to use Delta Engine to query and process data from Delta Lake Understand how to use Data Factory in combination with Databricks Use Structured Streaming in a production-like environment Who this book is forThis book is for software engineers, machine learning engineers, data scientists, and data engineers who are new to Azure Databricks and want to build high-quality data pipelines without worrying about infrastructure. Knowledge of Azure Databricks basics is required to learn the concepts covered in this book more effectively. A basic understanding of machine learning concepts and beginner-level Python programming knowledge is also recommended.

Work through practical recipes to learn how to solve complex machine learning and deep learning problems using Python Key Features Get up and running with artificial intelligence in no time using hands-on problem-solving recipes Explore popular Python libraries and tools to build AI solutions for images, text, sounds, and images Implement NLP, reinforcement learning, deep learning, GANs, Monte-Carlo tree search, and much more Book DescriptionArtificial intelligence (AI) plays an integral role in automating problem-solving. This involves predicting and classifying data and training agents to execute tasks successfully. This book will teach you how to solve complex problems with the help of independent and insightful recipes ranging from the essentials to advanced methods that have just come out of research. Artificial Intelligence with Python Cookbook starts by showing you how to set up your Python environment and taking you through the fundamentals of data exploration. Moving ahead, you'll be able to implement heuristic search techniques and genetic algorithms. In addition to this, you'll apply probabilistic models, constraint optimization, and reinforcement learning. As you advance through the book, you'll build deep learning models for text, images, video, and audio, and then delve into algorithmic bias, style transfer, music generation, and AI use cases in the healthcare and insurance industries. Throughout the book, you'll learn about a variety of tools for problem-solving and gain the knowledge needed to effectively approach complex problems. By the end of this book on AI, you will have the skills you need to write AI and machine learning algorithms, test them, and deploy them for production. What you will learn Implement data preprocessing steps and optimize model hyperparameters Delve into representational learning with adversarial autoencoders Use active learning, recommenders, knowledge embedding, and SAT solvers Get to grips with probabilistic modeling with TensorFlow probability Run object detection, text-to-speech conversion, and text and music generation Apply swarm algorithms, multi-agent systems, and graph networks Go from proof of concept to production by deploying models as microservices Understand how to use modern AI in practice Who this book is forThis AI machine learning book is for Python developers, data scientists, machine learning engineers, and deep learning practitioners who want to learn how to build artificial intelligence solutions with easy-to-follow recipes. You'll also find this book useful if you're looking for state-of-the-art solutions to perform different machine learning tasks in various use cases. Basic working knowledge of the Python programming language and machine learning concepts will help you to work with code effectively in this book.

Understand data analysis pipelines using machine learning algorithms and techniques with this practical guide Key Features Prepare and clean your data to use it for exploratory analysis, data manipulation, and data wrangling Discover supervised, unsupervised, probabilistic, and Bayesian machine learning methods Get to grips with graph processing and sentiment analysis Book DescriptionData analysis enables you to generate value from small and big data by discovering new patterns and trends, and Python is one of the most popular tools for analyzing a wide variety of data. With this book, you'll get up and running using Python for data analysis by exploring the different phases and methodologies used in data analysis and learning how to use modern libraries from the Python ecosystem to create efficient data pipelines. Starting with the essential statistical and data analysis fundamentals using Python, you'll perform complex data analysis and modeling, data manipulation, data cleaning, and data visualization using easy-to-follow examples. You'll then understand how to conduct time series analysis and signal processing using ARMA models. As you advance, you'll get to grips with smart processing and data analytics using machine learning algorithms such as regression, classification, Principal Component Analysis (PCA), and clustering. In the concluding chapters, you'll work on real-world examples to analyze textual and image data using natural language processing (NLP) and image analytics techniques, respectively. Finally, the book will demonstrate parallel computing using Dask. By the end of this data analysis book, you'll be equipped with the skills you need to prepare data for analysis and create meaningful data visualizations for forecasting values from data. What you will learn Explore data science and its various process models Perform data manipulation using NumPy and pandas for aggregating, cleaning, and handling missing values Create interactive visualizations using Matplotlib, Seaborn, and Bokeh Retrieve, process, and store data in a wide range of formats Understand data preprocessing and feature engineering using pandas and scikit-learn Perform time series analysis and signal processing using sunspot cycle data Analyze textual data and image data to perform advanced analysis Get up to speed with parallel computing using Dask Who this book is forThis book is for data analysts, business analysts, statisticians, and data scientists looking to learn how to use Python for data analysis. Students and academic faculties will also find this book useful for learning and teaching Python data analysis using a hands-on approach. A basic understanding of math and working knowledge of the Python programming language will help you get started with this book.

Informationsgesellschaft, Information als Wettbewerbsfaktor, Informationsflut: Diese Stichworte verdeutlichen die unternehmerische und gesellschaftliche Bedeutung von Informationen. Doch nicht nur Information allein, sondern auch die Systeme, die Informationen verarbeiten, speichern und ubertragen sowie die Technologien, auf denen sie beruhen, verdienen Aufmerksamkeit. Informationsmanagement hat die Aufgabe, den im Hinblick auf das Unternehmensziel bestmoeglichen Einsatz der Ressource Information zu gewahrleisten. Es zahlt zu den wesentlichen Bestandteilen heutiger Unternehmensfuhrung. Das Lehrbuch vermittelt in 13 Einheiten die Grundlagen des Informationsmanagements. Dabei werden neben den Managementaufgaben der Informationswirtschaft, der Systeme und der Technologien auch ausgewahlte Fuhrungsaufgaben des Informationsmanagementsbehandelt. Jede Lehreinheit beginnt mit einem UEberblick uber die behandelten Themen und schliesst mit einer Zusammenfassung sowie Aufgaben zur Wiederholung ab. So richtet sich dieses Buch insbesondere an Bachelorstudenten in den Fachern Wirtschaftsinformatik, BWL und Informatik.

Understand data analysis concepts to make accurate decisions based on data using Python programming and Jupyter Notebook Key Features Find out how to use Python code to extract insights from data using real-world examples Work with structured data and free text sources to answer questions and add value using data Perform data analysis from scratch with the help of clear explanations for cleaning, transforming, and visualizing data Book DescriptionData literacy is the ability to read, analyze, work with, and argue using data. Data analysis is the process of cleaning and modeling your data to discover useful information. This book combines these two concepts by sharing proven techniques and hands-on examples so that you can learn how to communicate effectively using data. After introducing you to the basics of data analysis using Jupyter Notebook and Python, the book will take you through the fundamentals of data. Packed with practical examples, this guide will teach you how to clean, wrangle, analyze, and visualize data to gain useful insights, and you'll discover how to answer questions using data with easy-to-follow steps. Later chapters teach you about storytelling with data using charts, such as histograms and scatter plots. As you advance, you'll understand how to work with unstructured data using natural language processing (NLP) techniques to perform sentiment analysis. All the knowledge you gain will help you discover key patterns and trends in data using real-world examples. In addition to this, you will learn how to handle data of varying complexity to perform efficient data analysis using modern Python libraries. By the end of this book, you'll have gained the practical skills you need to analyze data with confidence. What you will learn Understand the importance of data literacy and how to communicate effectively using data Find out how to use Python packages such as NumPy, pandas, Matplotlib, and the Natural Language Toolkit (NLTK) for data analysis Wrangle data and create DataFrames using pandas Produce charts and data visualizations using time-series datasets Discover relationships and how to join data together using SQL Use NLP techniques to work with unstructured data to create sentiment analysis models Discover patterns in real-world datasets that provide accurate insights Who this book is forThis book is for aspiring data analysts and data scientists looking for hands-on tutorials and real-world examples to understand data analysis concepts using SQL, Python, and Jupyter Notebook. Anyone looking to evolve their skills to become data-driven personally and professionally will also find this book useful. No prior knowledge of data analysis or programming is required to get started with this book.

People have described nature since the beginning of human history. They do it for various purposes, including to communicate about economic, social, governmental, meteorological, sustainability-related, strategic, military, and survival issues as well as artistic expression. As a part of the whole world of living beings, we use various types of senses, known and unknown, labeled and not identified, to both communicate and create. Describing Nature Through Visual Data is a collection of impactful research that discusses issues related to the visualization of scientific concepts, picturing processes, and products, as well as the role of computing in advancing visual literacy skills. Organized into four sections, the book contains descriptions, theories, and examples of visual and music-based solutions concerning the selected natural or technological events that are shaping present-day reality. The chapters pertain to selected scientific fields, digital art, computer graphics, and new media and confer the possible ways that visuals, visualization, simulation, and interactive knowledge presentation can help us to understand and share the content of scientific thought, research, artistic works, and practice. Featuring coverage on topics that include mathematical thinking, music theory, and visual communication, this reference is ideal for instructors, professionals, researchers, and students keen on comprehending and enhancing the role of knowledge visualization in computing, sciences, design, media communication, film, advertising, and marketing.

Learn quick and effective techniques to get up and running with building blockchain including Ethereum and Hyperledger Fabric. Key Features Understand the key concepts of decentralized applications and consensus algorithms Learn key concepts of Ethereum and Solidity programming Practical guide to get started with build efficient Blockchain applications with Ethereum and Hyperledger Book DescriptionBlockchain is a technology that powers the development of decentralized applications.This technology allows the construction of a network with no single control that enables participants to make contributions to and receive benefits from the network directly. This book will give you a thorough overview of blockchain and explain how a blockchain works.You will begin by going through various blockchain consensus mechanisms and cryptographic hash functions. You will then learn the fundamentals of programming in Solidity - the defacto language for developing decentralize, applications in Ethereum. After that, you will set up an Ethereum development environment and develop, package, build, and test campaign-decentralized applications.The book also shows you how to set up Hyperledger composer tools, analyze business scenarios, design business models, and write a chain code. Finally, you will get a glimpse of how blockchain is actually used in different real-world domains. By the end of this guide, you will be comfortable working with basic blockchain frameworks, and develop secure, decentralized applications in a hassle-free manner. What you will learn Understand how blockchain hashing works Write and test a smart contract using Solidity Develop and test a decentralized application Build and test your application using Hyperledger Fabric Implement business network using Hyperledger Composer Test and interact with business network applications Who this book is forThe book is for developers, analysts, or anyone looking to learn about Blockchain in a quick and easy manner.

A practical blockchain handbook designed to take you through implementing and re-engineering banking and financial solutions and workflows using eight step-by-step projects Key Features Implement various end-to-end blockchain projects and learn to enhance present-day financial solutions Use Ethereum, Hyperledger, and Stellar to build public and private decentralized applications Address complex challenges faced in the BFSI domain using different blockchain platform services Book DescriptionBlockchain technology will continue to play an integral role in the banking and finance sector in the coming years. It will enable enterprises to build transparent and secure business processes. Experts estimate annual savings of up to 20 billion dollars from this technology. This book will help you build financial apps using blockchain, guiding you through enhancing popular products and services in the banking and finance sector. The book starts by explaining the essential concepts of blockchain, and the impact of blockchain technology on the BFSI sector. Next, you'll delve into re-designing existing banking processes and building new financial apps using blockchain. To accomplish this, you'll work through eight blockchain projects. By demonstrating the entire process, the book helps you understand everything from setting up the environment and building frontend portals to system integration and testing apps. You will gain hands-on experience with the Ethereum, Hyperledger Fabric, and Stellar to develop private and public decentralized apps. Finally, you'll learn how to use ancillary platforms and frameworks such as IPFS, Truffle OpenZeppelin, and MetaMask. By the end of this blockchain book, you'll have an in-depth understanding of how to leverage distributed ledgers and smart contracts for financial use cases. What you will learn Design and implement blockchain solutions in a BFSI organization Explore common architectures and implementation models for enterprise blockchain Design blockchain wallets for multi-purpose applications using Ethereum Build secure and fast decentralized trading ecosystems with Blockchain Implement smart contracts to build secure process workflows in Ethereum and Hyperledger Fabric Use the Stellar platform to build KYC and AML-compliant remittance workflows Map complex business workflows and automate backend processes in a blockchain architecture Who this book is forThis book is for blockchain and Dapps developers, or anyone looking for a guide to building innovative and highly secure solutions in the fintech domain using real-world use cases. Developers working in financial enterprises and banks, and solution architects looking to build brand new process flows using blockchain technology will also find the book useful. Experience with Solidity programming and prior knowledge of finance and trade are required to get the most out of this book.

Learn how to architect, implement, and administer a complex Splunk Enterprise environment and extract valuable insights from business data. Key Features Understand the various components of Splunk and how they work together to provide a powerful Big Data analytics solution. Collect and index data from a wide variety of common machine data sources Design searches, reports, and dashboard visualizations to provide business data insights Book DescriptionSplunk is a leading platform and solution for collecting, searching, and extracting value from ever increasing amounts of big data - and big data is eating the world! This book covers all the crucial Splunk topics and gives you the information and examples to get the immediate job done. You will find enough insights to support further research and use Splunk to suit any business environment or situation. Splunk 7.x Quick Start Guide gives you a thorough understanding of how Splunk works. You will learn about all the critical tasks for architecting, implementing, administering, and utilizing Splunk Enterprise to collect, store, retrieve, format, analyze, and visualize machine data. You will find step-by-step examples based on real-world experience and practical use cases that are applicable to all Splunk environments. There is a careful balance between adequate coverage of all the critical topics with short but relevant deep-dives into the configuration options and steps to carry out the day-to-day tasks that matter. By the end of the book, you will be a confident and proficient Splunk architect and administrator. What you will learn Design and implement a complex Splunk Enterprise solution Configure your Splunk environment to get machine data in and indexed Build searches to get and format data for analysis and visualization Build reports, dashboards, and alerts to deliver critical insights Create knowledge objects to enhance the value of your data Install Splunk apps to provide focused views into key technologies Monitor, troubleshoot, and manage your Splunk environment Who this book is forThis book is intended for experienced IT personnel who are just getting started working with Splunk and want to quickly become proficient with its usage. Data analysts who need to leverage Splunk to extract critical business insights from application logs and other machine data sources will also benefit from this book.

Extend PostgreSQL using PostgreSQL server programming to create, test, debug, and optimize a range of user-defined functions in your favorite programming language Key Features Learn the concepts of PostgreSQL 11 with lots of real-world datasets and examples Learn queries, data replication, and database performance Extend the functionalities of your PostgreSQL instance to suit your organizational needs Book DescriptionPostgreSQL is a rock-solid, scalable, and safe enterprise-level relational database. With a broad range of features and stability, it is ever increasing in popularity.This book shows you how to take advantage of PostgreSQL 11 features for server-side programming. Server-side programming enables strong data encapsulation and coherence. The book begins with the importance of server-side programming and explains the risks of leaving all the checks outside the database. To build your capabilities further, you will learn how to write stored procedures, both functions and the new PostgreSQL 11 procedures, and create triggers to perform encapsulation and maintain data consistency. You will also learn how to produce extensions, the easiest way to package your programs for easy and solid deployment on different PostgreSQL installations. What you will learn Explore data encapsulation Write stored procedures in different languages Interact with transactions from within a function Get to grips with triggers and rules Create and manage custom data types Create extensions to package code and data Implement background workers and Inter-Process Communication (IPC) How to deal with foreign languages, in particular Java and Perl Who this book is forThis book is for database administrators, data engineers, and database engineers who want to implement advanced functionalities and master complex administrative tasks with PostgreSQL 11.