Exploring Graphs With Elixir

Data is everywhere - it's just not very well connected, which makes it super hard to relate dataset to dataset. Using graphs as the underlying glue, you can readily join data together and create navigation paths across diverse sets of data. Add Elixir, with its awesome power of concurrency, and you'll soon be mastering data networks. Learn how different graph models can be accessed and used from within Elixir and how you can build a robust semantics overlay on top of graph data structures. We'll start from the basics and examine the main graph paradigms. Get ready to embrace the world of connected data! Graphs provide an intuitive and highly flexible means for organizing and querying huge amounts of loosely coupled data items. These data networks, or graphs in math speak, are typically stored and queried using graph databases. Elixir, with its noted support for fault tolerance and concurrency, stands out as a language eminently suited to processing sparsely connected and distributed datasets. Using Elixir and graph-aware packages in the Elixir ecosystem, you'll easily be able to fit your data to graphs and networks, and gain new information insights. Build a testbed app for comparing native graph data with external graph databases. Develop a set of applications under a single umbrella app to drill down into graph structures. Build graph models in Elixir, and query graph databases of various stripes - using Cypher and Gremlin with property graphs and SPARQL with RDF graphs. Transform data from one graph modeling regime to another. Understand why property graphs are especially good at graph traversal problems, while RDF graphs shine at integrating different semantic models and can scale up to web proportions. Harness the outstanding power of concurrent processing in Elixir to work with distributed graph datasets and manage data at scale. What You Need: To follow along with the book, you should have Elixir 1.10+ installed. The book will guide you through setting up an umbrella application for a graph testbed using a variety of graph databases for which Java SDK 8+ is generally required. Instructions for installing the graph databases are given in an appendix.

Data is everywhere - it's just not very well connected, which makes it super hard to relate dataset to dataset. Using graphs as the underlying glue, you can readily join data together and create navigation paths across diverse sets of data. Add Elixir, with its awesome power of concurrency, and you'll soon be mastering data networks. Learn how different graph models can be accessed and used from within Elixir and how you can build a robust semantics overlay on top of graph data structures. We'll start from the basics and examine the main graph paradigms. Get ready to embrace the world of connected data! Graphs provide an intuitive and highly flexible means for organizing and querying huge amounts of loosely coupled data items. These data networks, or graphs in math speak, are typically stored and queried using graph databases. Elixir, with its noted support for fault tolerance and concurrency, stands out as a language eminently suited to processing sparsely connected and distributed datasets. Using Elixir and graph-aware packages in the Elixir ecosystem, you'll easily be able to fit your data to graphs and networks, and gain new information insights. Build a testbed app for comparing native graph data with external graph databases. Develop a set of applications under a single umbrella app to drill down into graph structures. Build graph models in Elixir, and query graph databases of various stripes - using Cypher and Gremlin with property graphs and SPARQL with RDF graphs. Transform data from one graph modeling regime to another. Understand why property graphs are especially good at graph traversal problems, while RDF graphs shine at integrating different semantic models and can scale up to web proportions. Harness the outstanding power of concurrent processing in Elixir to work with distributed graph datasets and manage data at scale. What You Need: To follow along with the book, you should have Elixir 1.10+ installed. The book will guide you through setting up an umbrella application for a graph testbed using a variety of graph databases for which Java SDK 8+ is generally required. Instructions for installing the graph databases are given in an appendix.

Imagine writing Java code that reads like the problem statement, code that's highly expressive, concise, easy to read and modify, and has reduced complexity. With the functional programming capabilities in Java, that's not a fantasy. This book will guide you from the familiar imperative style through the practical aspects of functional programming, using plenty of examples. Apply the techniques you learn to turn highly complex imperative code into elegant and easy-to-understand functional-style code. Updated to the latest version of Java, this edition has four new chapters on error handling, refactoring to functional style, transforming data, and idioms of functional programming. Don't struggle with the limitations of the imperative style; instead learn to combine object-oriented programming with the functional style to reduce the accidental complexity. Harness the functional programming capabilities of Java to create applications where the program reveals its intentions and your team can quickly understand and modify code to align with changing business requirements. Unlock the power of lambda expressions and the Streams API to turn the oft-written spaghetti code into highly concise, expressive, elegant, and maintainable code. See how Streams make the arduous task of parallelizing code as easy as flipping a switch when superior speed is necessary. Apply design patterns built around lambda expressions, safely manage resource allocations, use memoization, and learn to transform data into different forms, all while honoring immutability, and providing thread safety to leverage lazy evaluation for efficiency and parallel execution for performance. Move beyond the basics, explore the idioms for writing functional programs. Learn to think functionally by refactoring legacy code into the functional style. And, if your code runs aground due to failures, learn to properly handle errors the functional way. Don't drown in theory; instead learn the practical functional programming techniques to create superior Java code. What You Need: Java version 8 or newer.

Put the power of Haskell to work in your programs, learning from an engineer who uses Haskell daily to get practical work done efficiently. Leverage powerful features like Monad Transformers and Type Families to build useful applications. Realize the benefits of a pure functional language, like protecting your code from side effects. Manage concurrent processes fearlessly. Apply functional techniques to working with databases and building RESTful services. Don't get bogged down in theory, but learn to employ advanced programming concepts to solve real-world problems. Don't just learn the syntax, but dive deeply into Haskell as you build efficient, well-tested programs. Haskell is a pure functional programming language with a rich ecosystem of tools and libraries. Designed to push the boundaries of programming, it offers unparalleled power for building reliable and maintainable systems. But to unleash that power, you need a guide. Effective Haskell is that guide. Written by an engineer who understands how to apply Haskell to the real world and uses it daily to get practical work done, it is your ticket to Haskell mastery. Gain deep understanding of how Haskell deals with IO and the outside world by writing a complete Haskell application that does several different kinds of IO. Reinforce your learnings with practice exercises in every chapter. Write stable and performant code using Haskell's type system, code that is easier to grow and refactor. Leverage the power of pure functional programming to improve collaboration, make concurrency safe and easy, and make large code bases manageable. Implement type-safe web services, write generative tests, design strongly typed embedded domain-specific languages, and build applications that exploit parallelism and concurrency without fear of deadlocks and race conditions. Create and deploy cloud-native Haskell applications. Master the performance characteristics of functional applications to make them run faster and use less memory. Write Haskell programs that solve real-world business problems. What You Need: Intel based Mac, M1 Macs, Linux PC, or Windows with WSL2 ghcup (http://www. Haskell.org/ghcup/) An active internet connection will be required for some projects.

Build applications quicker and with less effort using functional programming and Kotlin. Learn by building a complete application, from gathering requirements to delivering a microservice architecture following functional programming principles. Learn how to implement CQRS and EventSourcing in a functional way to map the domain into code better and to keep the cost of change low for the whole application life cycle. If you're curious about functional programming or you are struggling with how to put it into practice, this guide will help you increase your productivity composing small functions together instead of creating fat objects. Switching to the functional paradigm isn't easy when you're used to object-oriented programming. You need more than just lambdas and mapping over collections to get a declarative style and disentangle the state from the computations. Use transformations and compositions to help you write less code with better results. Boost your productivity and harness the power of functional programming by creating real-world applications rather than focusing on theoretical concepts. Work through a series of short exercises to find and compose pure functions, and create data structures that work like algebra. Get rid of mutable state in your software to eliminate the main source of bugs. Apply CQRS and EventSourcing patterns to translate stakeholder requirements into functional design and then into code. See how Kotlin's easy-to-learn syntax and functional-friendly approach make it a great option for a pragmatic language that integrates well with existing Java code and libraries. Leverage functional programming to build and deliver robust applications in less time and with fewer defects. What You Need: The code in this book is designed to allow you to build your application from scratch on Windows, Mac and Linux. You will need a recent IDE, we recommend IntelliJ Community Edition, and Kotlin 1.3.x or later.

Diagrams communicate relationships more directly and clearly than words ever can. Using only text-based markup, create meaningful and attractive diagrams to document your domain, visualize user flows, reveal system architecture at any desired level, or refactor your code. With the tools and techniques this book will give you, you'll create a wide variety of diagrams in minutes, share them with others, and revise and update them immediately on the basis of feedback. Adding diagrams to your professional vocabulary will enable you to work through your ideas quickly when working on your own code or discussing a proposal with colleagues. Expand your professional vocabulary by learning to communicate with diagrams as easily and naturally as speaking or writing. This book will provide you with the skills and tools to turn ideas into clear, meaningful, and attractive diagrams in mere minutes, using nothing more complicated than text-based markup. You'll learn what kinds of diagrams are suited to each of a variety of use cases, from documenting your domain to understanding how complex code pieces together. Model your software's architecture, creating diagrams focused broadly or narrowly, depending on the audience. Visualize application and user flows, design database schemas, and use diagrams iteratively to design and refactor your application. You'll be able to use technical diagramming to improve your day-to-day workflow. You will better understand the codebase you work in, communicate ideas more effectively and immediately with others, and more clearly document the architecture with C4 diagrams. Manually creating diagrams is cumbersome and time-consuming. You'll learn how to use text-based tools like Mermaid to rapidly turn ideas into diagrams. And You'll learn how to keep your diagrams up to date and seamlessly integrated into your engineering workflow. You'll be better at visualizing and communicating when you add diagrams to your standard vocabulary. What You Need: A Mac or PC A GitHub account

Businesses own more data than ever before, but it's of no value if you don't know how to use it. Data governance manages the people, processes, and strategy needed for deploying data projects to production. But doing it well is far from easy: Less than one fourth of business leaders say their organizations are data driven. In Designing Data Governance from the Ground Up, you'll build a cross-functional strategy to create roadmaps and stewardship for data-focused projects, embed data governance into your engineering practice, and put processes in place to monitor data after deployment. In the last decade, the amount of data people produced grew 3,000 percent. Most organizations lack the strategy to clean, collect, organize, and automate data for production-ready projects. Without effective data governance, most businesses will keep failing to gain value from the mountain of data that's available to them. There's a plethora of content intended to help DataOps and DevOps teams reach production, but 90 percent of projects trained with big data fail to reach production because they lack governance. This book shares six steps you can take to build a data governance strategy from scratch. You'll find a data framework, pull together a team of data stewards, build a data governance team, define your roadmap, weave data governance into your development process, and monitor your data in production Whether you're a chief data officer or individual contributor, this book will show you how to manage up, get the buy-in you need to build data governance, find the right colleagues to co-create data governance, and keep them engaged for the long haul.

From tea guru Sebastian Beckwith and New York Times bestsellers Caroline Paul and Wendy MacNaughton comes the essential guide to exploring and enjoying the vast world of tea. Tea, the most popular beverage in the world after water, has brought nations to war, defined cultures, bankrupted coffers, and toppled kings. And yet in many ways this fragrantly comforting and storied brew remains elusive, even to its devotees. As down-to-earth yet stylishly refined as the drink itself, A Little Tea Book submerges readers into tea, exploring its varieties, subtleties, and pleasures right down to the process of selecting and brewing the perfect cup. From orange pekoe to pu-erh, tea expert Sebastian Beckwith provides surprising tips, fun facts, and flavorful recipes to launch dabblers and connoisseurs alike on a journey of taste and appreciation. Along with writer and fellow tea-enthusiast Caroline Paul, Beckwith walks us through the cultural and political history of the elixir that has touched every corner of the world. Featuring featuring charming, colorful charts, graphs, and illustrations by bestselling illustrator Wendy MacNaughton and Beckwith's sumptuous photographs, A Little Tea Book is a friendly, handsome, and illuminating primer with a dash of sass and sophistication. Cheers!