Data is changing everything. Many industries today are being fundamentally transformed through the accumulation and analysis of large quantities of data, stored in diversified but flexible repositories known as data lakes. Whether your company has just begun to think about big data or has already initiated a strategy for handling it, this practical ebook shows you how to plan a successful data lake migration. You’ll learn the value of data lakes, their structure, and the problems they attempt to solve. Using Zaloni’s data lake maturity model, you’ll then explore your organization’s readiness for putting a data lake into action. Do you have the tools and data architectures to support big data analysis? Are your people and processes prepared? The data lake maturity model will help you rate your organization’s readiness. This report includes: The structure and purpose of a data lake Descriptive, predictive, and prescriptive analytics Data lake curation, self-service, and the use of data lake zones How to rate your organization using the data lake maturity model A complete checklist to help you determine your strategic path forward

The data lake is a daring new approach for harnessing the power of big data technology and providing convenient self-service capabilities. But is it right for your company? This book is based on discussions with practitioners and executives from more than a hundred organizations, ranging from data-driven companies such as Google, LinkedIn, and Facebook, to governments and traditional corporate enterprises. You’ll learn what a data lake is, why enterprises need one, and how to build one successfully with the best practices in this book. Alex Gorelik, CTO and founder of Waterline Data, explains why old systems and processes can no longer support data needs in the enterprise. Then, in a collection of essays about data lake implementation, you’ll examine data lake initiatives, analytic projects, experiences, and best practices from data experts working in various industries. Get a succinct introduction to data warehousing, big data, and data science Learn various paths enterprises take to build a data lake Explore how to build a self-service model and best practices for providing analysts access to the data Use different methods for architecting your data lake Discover ways to implement a data lake from experts in different industries

Many organizations have begun to rethink the strategy of allowing regional teams to maintain independent databases that are periodically consolidated with the head office. As businesses extend their reach globally, these hierarchical approaches no longer work. Instead, an enterprise’s entire data infrastructure—including multiple types of data persistence—needs to be shared and updated everywhere at the same time with fine-grained control over who has access. This practical report examines the requirements and challenges of constructing a geo-distributed data platform, including examples of specific technologies designed to meet them. Authors Ted Dunning and Ellen Friedman also provide real-world use cases that show how low-latency geo-distribution of very large-scale data and computation provide a competitive edge. With this report, you’ll explore: How replication and mirroring methods for data movement provide the large scale, low latency, and low cost that systems demand The importance of multimaster replication of data streams and databases Advantages (and disadvantages) of cloud neutrality, cloud bursting, and hybrid cloud architecture for transferring data Why effective data governance is a complex process that requires the right tools for controlling and monitoring geo-distributed data How to make containers work for geo-distributed data at scale, even where stateful applications are involved Use cases that demonstrate how telecoms and online advertisers distribute large quantities of data

There are many benefits to becoming a data-driven organization, including the ability to accelerate and improve business decision accuracy through the real-time processing of transactions, social media streams, and IoT data. But those benefits require significant changes to your infrastructure. You need flexible architectures that can copy data to analytics platforms at near-zero latency while maintaining 100% production uptime. Fortunately, a solution already exists. This ebook demonstrates how change data capture (CDC) can meet the scalability, efficiency, real-time, and zero-impact requirements of modern data architectures. Kevin Petrie, Itamar Ankorion, and Dan Potter—technology marketing leaders at Attunity—explain how CDC enables faster and more accurate decisions based on current data and reduces or eliminates full reloads that disrupt production and efficiency. The book examines: How CDC evolved from a niche feature of database replication software to a critical data architecture building block Architectures where data workflow and analysis take place, and their integration points with CDC How CDC identifies and captures source data updates to assist high-speed replication to one or more targets Case studies on cloud-based streaming and streaming to a data lake and related architectures Guiding principles for effectively implementing CDC in cloud, data lake, and streaming environments The Attunity Replicate platform for efficiently loading data across all major database, data warehouse, cloud, streaming, and Hadoop platforms

Use this practical guide to successfully handle the challenges encountered when designing an enterprise data lake and learn industry best practices to resolve issues. When designing an enterprise data lake you often hit a roadblock when you must leave the comfort of the relational world and learn the nuances of handling non-relational data. Starting from sourcing data into the Hadoop ecosystem, you will go through stages that can bring up tough questions such as data processing, data querying, and security. Concepts such as change data capture and data streaming are covered. The book takes an end-to-end solution approach in a data lake environment that includes data security, high availability, data processing, data streaming, and more. Each chapter includes application of a concept, code snippets, and use case demonstrations to provide you with a practical approach. You will learn the concept, scope, application, and starting point. What You'll Learn Get to know data lake architecture and design principles Implement data capture and streaming strategies Implement data processing strategies in Hadoop Understand the data lake security framework and availability model Who This Book Is For Big data architects and solution architects

Many organizations today are succeeding with data lakes, not just as storage repositories but as places to organize, prepare, analyze, and secure a wide variety of data. Management and governance is critical for making your data lake work, yet hard to do without a roadmap. With this ebook, you’ll learn an approach that merges the flexibility of a data lake with the management and governance of a traditional data warehouse. Author Ben Sharma explains the steps necessary to deploy data lakes with robust, metadata-driven data management platforms. You’ll learn best practices for building, maintaining, and deriving value from a data lake in your production environment. Included is a detailed checklist to help you construct a data lake in a controlled yet flexible way. Managing and governing data in your lake cannot be an afterthought. This ebook explores how integrated data lake management solutions, such as the Zaloni Data Platform (ZDP), deliver necessary controls without making data lakes slow and inflexible. You’ll examine: A reference architecture for a production-ready data lake An overview of the data lake technology stack and deployment options Key data lake attributes, including ingestion, storage, processing, and access Why implementing management and governance is crucial for the success of your data lake How to curate data lakes through data governance, acquisition, organization, preparation, and provisioning Methods for providing secure self-service access for users across the enterprise How to build a future-proof data lake tech stack that includes storage, processing, data management, and reference architecture Emerging trends that will shape the future of data lakes

The data lake was once heralded as the answer to the flood of big data that arrived in a variety of structured and unstructured formats. But, due to the ease of integration and the lack of governance, data lakes in many companies have devolved into unusable data swamps. This short ebook shows you how to solve this problem using an Operational Data Hub (ODH) to collect, store, index, cleanse, harmonize, and master data of all shapes and formats. Gerhard Ungerer—CTO and co-founder of Random Bit LLC—explains how the ODH supports transactional integrity so that the hub can serve as integration point for enterprise applications. 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

"Data Lake for Enterprises" is a comprehensive guide to building data lakes using the Lambda Architecture. It introduces big data technologies like Hadoop, Spark, and Flume, showing how to use them effectively to manage and leverage enterprise-scale data. You'll gain the skills to design and implement data systems that handle complex data challenges. What this Book will help me do Master the use of Lambda Architecture to create scalable and effective data management systems. Understand and implement technologies like Hadoop, Spark, Kafka, and Flume in an enterprise data lake. Integrate batch and stream processing techniques using big data tools for comprehensive data analysis. Optimize data lakes for performance and reliability with practical insights and techniques. Implement real-world use cases of data lakes and machine learning for predictive data insights. Author(s) None Mishra, None John, and Pankaj Misra are recognized experts in big data systems with a strong background in designing and deploying data solutions. With a clear and methodical teaching style, they bring years of experience to this book, providing readers with the tools and knowledge required to excel in enterprise big data initiatives. Who is it for? This book is ideal for software developers, data architects, and IT professionals looking to integrate a data lake strategy into their enterprises. It caters to readers with a foundational understanding of Java and big data concepts, aiming to advance their practical knowledge of building scalable data systems. If you're eager to delve into cutting-edge technologies and transform enterprise data management, this book is for you.

Many organizations use Hadoop-driven data lakes as an adjunct staging area for their enterprise data warehouses (EDW). But for those companies ready to take the plunge, a data lake is far more useful as a one-stop-shop for extracting insights from their vast collection of data. With this eBook, you’ll learn best practices for building, maintaining, and deriving value from a Hadoop data lake in production environments. Authors Alice LaPlante and Ben Sharma explain how a data lake will enable your organization to manage an increasing volume of datasets—from blog postings and product reviews to streaming data—and to discover important relationships between them. Whether you want to control administrative costs in healthcare or reduce risk in financial services, this ebook addresses the architectural considerations and required capabilities you need to build your own data lake. With this report, you’ll learn: The key attributes of a data lake, including its ability to store information in native formats for later processing Why implementing data management and governance in your data lake is crucial How to address various challenges for building and managing a data lake Self-service options that enable different users to access the data lake without help from IT Emerging trends that will shape the future of data lakes

Organizations today are swimming in data, but most of them manage to analyze only a fraction of what they collect. To help build a stronger data-driven culture, many organizations are adopting a new approach called self-service analytics. This O’Reilly report examines how this approach provides data access to more people across a company, allowing business users to work with data themselves and create their own customized analyses. The result? More eyes looking at more data in more ways. Along with the perceived benefits, author Sandra Swanson also delves into the potential pitfalls of self-service analytics: balancing greater data access with concerns about security, data governance, and siloed data stores. Read this report and gain insights from enterprise tech (Yahoo), government (the City of Chicago), and disruptive retail (Warby Parker and Talend). Learn how these organizations are handling self-service analytics in practice. Sandra Swanson is a Chicago-based writer who’s covered technology, science, and business for dozens of publications, including ScientificAmerican.com. Connect with her on Twitter (@saswanson) or at www.saswanson.com.

In "Data Lake Development with Big Data," you will explore the fundamental principles and techniques for constructing and managing a Data Lake tailored for your organization's big data challenges. This book provides practical advice and architectural strategies for ingesting, managing, and analyzing large-scale data efficiently and effectively. What this Book will help me do Learn how to architect a Data Lake from scratch tailored to your organizational needs. Master techniques for ingesting data using real-time and batch processing frameworks efficiently. Understand data governance, quality, and security considerations essential for scalable Data Lakes. Discover strategies for enabling users to explore data within the Data Lake effectively. Gain insights into integrating Data Lakes with Big Data analytic applications for high performance. Author(s) None Pasupuleti and Beulah Salome Purra bring their extensive expertise in big data and enterprise data management to this book. With years of hands-on experience designing and managing large-scale data architectures, their insights are rooted in practical knowledge and proven techniques. Who is it for? This book is ideal for data architects and senior managers tasked with adapting or creating scalable data solutions in enterprise contexts. Readers should have foundational knowledge of master data management and be familiar with Big Data technologies to derive maximum value from the content presented.

Organizations across many industries have recently created fast-growing repositories to deal with an influx of new data from many sources and often in multiple formats. To manage these data lakes, companies have begun to leave the familiar confines of relational databases and data warehouses for Hadoop and various big data solutions. But adopting new technology alone won’t solve the problem. Based on interviews with several experts in data management, author Andy Oram provides an in-depth look at common issues you’re likely to encounter as you consider how to manage business data. You’ll explore five key topic areas, including: Acquisition and ingestion: how to solve these problems with a degree of automation. Metadata: how to keep track of when data came in and how it was formatted, and how to make it available at later stages of processing. Data preparation and cleaning: what you need to know before you prepare and clean your data, and what needs to be cleaned up and how. Organizing workflows: what you should do to combine your tasks—ingestion, cataloging, and data preparation—into an end-to-end workflow. Access control: how to address security and access controls at all stages of data handling. Andy Oram, an editor at O’Reilly Media since 1992, currently specializes in programming. His work for O'Reilly includes the first books on Linux ever published commercially in the United States.

To discover the shape and structure of the big data market, the San Francisco-based startup Relato took a unique approach to market research and created the first fully data-driven market report. Company CEO Russell Jurney and his team collected and analyzed raw data from a variety of sources to reveal a boatload of business insights about the big data space. This exceptional report is now available for free download. Using data analytic techniques such as social network analysis (SNA), Relato exposed the vast and complex partnership network that exists among tens of thousands of unique big data vendors. The dataset Relato collected is centered around Cloudera, Hortonworks, and MapR, the major platform vendors of Hadoop, the primary force behind this market. From this snowball sample, a 2-hop network, the Relato team was able to answer several questions, including: Who are the major players in the big data market? Which is the leading Hadoop vendor? What sectors are included in this market and how do they relate? Which among the thousands of partnerships are most important? Who’s doing business with whom? Metrics used in this report are also visible in Relato’s interactive web application, via a link in the report, which walks you through the insights step-by-step.

Companies of all sizes are considering data lakes as a way to deal with terabytes of security data that can help them conduct forensic investigations and serve as an early indicator to identify bad or relevant behavior. Many think about replacing their existing SIEM (security information and event management) systems with Hadoop running on commodity hardware. Before your company jumps into the deep end, you first need to weigh several critical factors. This O'Reilly report takes you through technological and design options for implementing a data lake. Each option not only supports your data analytics use cases, but is also accessible by processes, workflows, third-party tools, and teams across your organization. Within this report, you'll explore: Five questions to ask before choosing architecture for your backend data store How data lakes can overcome scalability and data duplication issues Different options for storing context and unstructured log data Data access use cases covering both search and analytical queries via SQL Processes necessary for ingesting data into a data lake, including parsing, enrichment, and aggregation Four methods for embedding your SIEM into a data lake

Big data--the enormous amount of data that is created as virtually every movement, transaction, and choice we make becomes digitized--is revolutionizing business. Offering real-world insight and explanations, this book provides a roadmap for organizations looking to develop a profitable big data strategy...and reveals why it's not something they can leave to the I.T. department.

Sharing best practices from companies that have implemented a big data strategy including Walmart, InterContinental Hotel Group, Walt Disney, and Shell, Think Bigger covers the most important big data trends affecting organizations, as well as key technologies like Hadoop and MapReduce, and several crucial types of analyses. In addition, the book offers guidance on how to ensure security, and respect the privacy rights of consumers. It also examines in detail how big data is impacting specific industries--and where opportunities can be found.

Big data is changing the way businesses--and even governments--are operated and managed. Think Bigger is an essential resource for anyone who wants to ensure that their company isn't left in the dust.