Get started with an accelerated introduction to the R ecosystem, programming language, and tools including R script and RStudio. Utilizing many examples and projects, this book teaches you how to get data into R and how to work with that data using R. Once grounded in the fundamentals, the rest of Practical R 4 dives into specific projects and examples starting with running and analyzing a survey using R and LimeSurvey. Next, you'll carry out advanced statistical analysis using R and MouselabWeb. Then, you’ll see how R can work for you without statistics, including how R can be used to automate data formatting, manipulation, reporting, and custom functions. The final part of this book discusses using R on a server; you’ll build a script with R that can run an RStudio Server and monitor a report source for changes to alert the user when something has changed. This project includes both regular email alerting and push notification. And, finally, you’ll use R to create a customized daily rundown report of a person's most important information such as a weather report, daily calendar, to-do's and more. This demonstrates how to automate such a process so that every morning, the user navigates to the same web page and gets the updated report. What You Will Learn Set up and run an R script, including installation on a new machine and downloading and configuring R Turn any machine into a powerful data analytics platform accessible from anywhere with RStudio Server Write basic R scripts and modify existing scripts to suit your own needs Create basic HTML reports in R, inserting information as needed Build a basic R package and distribute it Who This Book Is For Some prior exposure to statistics, programming, and maybe SAS is recommended but not required.

Free Data Storytelling Training Register before it sells out again! Our BI Data Storytelling Mastery Accelerator 3-Day Live Workshop new dates are finally available. Many BI teams are still struggling to deliver consistent, high-engaging analytics their users love. At the end of the workshop, you'll leave with a clear BI delivery action plan. Register today! In this episode, you'll learn: [00:40] Transformational Stories and Lessons Learned: 3 ways to use data storytelling in data science [19:50] Storytelling as a means of visualization. [26:52] How to think about answering the questions with data storytelling. For full show notes, and the links mentioned visit: https://bibrainz.com/podcast/56  

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"Learn Grafana 7.0" is the ultimate beginner's guide to leveraging Grafana's capabilities for analytics and interactive dashboards. You'll master real-time data monitoring, visualization, and learn how to query and explore metrics with a hands-on approach to Grafana 7.0's new features. What this Book will help me do Learn to install and configure Grafana from scratch, preparing you for real-world data analysis tasks. Navigate and utilize the Graph panel in Grafana effectively, ensuring clear and actionable visual insights. Incorporate advanced dashboard features such as annotations, templates, and links to enhance data monitoring. Integrate Grafana with major cloud providers like AWS and Azure for robust monitoring solutions. Implement secure user authentication and fine-tuned permissions for managing teams and sharing insights safely. Author(s) None Salituro, the author of "Learn Grafana 7.0," is an experienced data visualization expert with years of experience in software development and analytics. Salituro focuses on creating understandable and accessible resources for developers and analysts of all skill levels, bringing a hands-on practical approach to technical learning. Who is it for? This book is perfect for data analysts, business intelligence developers, and administrators looking to build skills in data visualization and monitoring with Grafana 7.0. If you're eager to create interactive dashboards and learn practical applications of Grafana's features, this book is for you. Beginners to Grafana are fully accommodated, though familiarity with data visualization principles is beneficial. For those seeking to monitor cloud services like AWS with Grafana, this book is indispensable.

Summary The majority of analytics platforms are focused on use internal to an organization by business stakeholders. As the availability of data increases and overall literacy in how to interpret it and take action improves there is a growing need to bring business intelligence use cases to a broader audience. GoodData is a platform focused on simplifying the work of bringing data to employees and end users. In this episode Sheila Jung and Philip Farr discuss how the GoodData platform is being used, how it is architected to provide scalable and performant analytics, and how it integrates into customer’s data platforms. This was an interesting conversation about a different approach to business intelligence and the importance of expanded access to data.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management What are the pieces of advice that you wish you had received early in your career of data engineering? If you hand a book to a new data engineer, what wisdom would you add to it? I’m working with O’Reilly on a project to collect the 97 things that every data engineer should know, and I need your help. Go to dataengineeringpodcast.com/97things to add your voice and share your hard-earned expertise. When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! GoodData is revolutionizing the way in which companies provide analytics to their customers and partners. Start now with GoodData Free that makes our self-service analytics platform available to you at no cost. Register today at dataengineeringpodcast.com/gooddata Your host is Tobias Macey and today I’m interviewing Sheila Jung and Philip Farr about how GoodData is building a platform that lets you share your analytics outside the boundaries of your organization

Interview

Introduction How did you get involved in the area of data management? Can you start by describing what you are building at GoodData and some of its origin story? The business intelligence market has been around for decades now and there are dozens of options with different areas of focus. What are the factors that might motivate me to choose GoodData over the other contenders in the space? What are the use cases and industries that you focus on supporting with GoodData? How has the market of business intelligence tools evolved in recent years?

What are the contributing trends in technology and business use cases that are driving that change?

What are some of the ways that your customers are embedding analytics into their own products? What are the differences in processing and serving capabilities between an internally used business intelligence tool, and one that is used for embedding into externally used systems?

What unique challenges are posed by the embedded analytics use case? How do you approach topics such as security, access control, and latency in a multitenant analytics platform?

What guidelines have you found to be most useful when addressing the concerns of accuracy and interpretability of the data being presented? How is the GoodData platform architected?

What are the complexities that you have had to design around in order to provide performant access to your customers’ data sources in an interactive use case? What are the off-the-shelf components that you have been able to integrate into the platform,

No one has ever been disappointed by a sequel, right? Especially when the original was well-received both by the critics and at the box office. Well, Episode #134: "These Are a Few of Our Favorite (Analytics) Tips" scored an 83% Tomatometer with an audience score of 91% on Rotten Tomatoes. As it happened, those are the same scores that The Sound of Music achieved, and they're pretty impressive. Unlike The Sound of Music, we decided we'd give our fans what they clearly wanted and release another episode of our (just as favorite) analytics tips! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Free Data Storytelling Training Register before it sells out again! Our BI Data Storytelling Mastery Accelerator 3-Day Live Workshop new dates are finally available. Many BI teams are still struggling to deliver consistent, high-engaging analytics their users love. At the end of the workshop, you'll leave with a clear BI delivery action plan. Register today! In this episode, you'll learn: [01:50] Transformational Stories and Lessons Learned: Yves's 3 things you can do to become irreplaceable. [06:39] User Expectations: The importance of making yourself stick in your role during the pandemic and lockdowns. [22:47]  - How consulting allows Yves to bypass age discrimination. For full show notes, and the links mentioned visit: https://bibrainz.com/podcast/54  

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Analyze vast amounts of data in record time using Apache Spark with Databricks in the Cloud. Learn the fundamentals, and more, of running analytics on large clusters in Azure and AWS, using Apache Spark with Databricks on top. Discover how to squeeze the most value out of your data at a mere fraction of what classical analytics solutions cost, while at the same time getting the results you need, incrementally faster. This book explains how the confluence of these pivotal technologies gives you enormous power, and cheaply, when it comes to huge datasets. You will begin by learning how cloud infrastructure makes it possible to scale your code to large amounts of processing units, without having to pay for the machinery in advance. From there you will learn how Apache Spark, an open source framework, can enable all those CPUs for data analytics use. Finally, you will see how services such as Databricks provide the power of Apache Spark, without you having to know anything aboutconfiguring hardware or software. By removing the need for expensive experts and hardware, your resources can instead be allocated to actually finding business value in the data. This book guides you through some advanced topics such as analytics in the cloud, data lakes, data ingestion, architecture, machine learning, and tools, including Apache Spark, Apache Hadoop, Apache Hive, Python, and SQL. Valuable exercises help reinforce what you have learned. What You Will Learn Discover the value of big data analytics that leverage the power of the cloud Get started with Databricks using SQL and Python in either Microsoft Azure or AWS Understand the underlying technology, and how the cloud and Apache Spark fit into the bigger picture See how these tools are used in the real world Run basic analytics, including machine learning, on billions of rows at a fraction of a cost or free Who This Book Is For Data engineers, data scientists, and cloud architects who want or need to run advanced analytics in the cloud. It is assumed that the reader has data experience, but perhaps minimal exposure to Apache Spark and Azure Databricks. The book is also recommended for people who want to get started in the analytics field, as it provides a strong foundation.

Deliver eye-catching and insightful business intelligence with Microsoft Power BI Desktop. This new edition has been updated to cover all the latest features of Microsoft’s continually evolving visualization product. New in this edition is help with storytelling—adapted to PCs, tablets, and smartphones—and the building of a data narrative. You will find coverage of templates and JSON style sheets, data model annotations, and the use of composite data sources. Also provided is an introduction to incorporating Python visuals and the much awaited Decomposition Tree visual. Pro Power BI Desktop shows you how to use source data to produce stunning dashboards and compelling reports that you mold into a data narrative to seize your audience’s attention. Slice and dice the data with remarkable ease and then add metrics and KPIs to project the insights that create your competitive advantage. Convert raw data into clear, accurate, and interactive information with Microsoft’s free self-service BI tool. This book shows you how to choose from a wide range of built-in and third-party visualization types so that your message is always enhanced. You will be able to deliver those results on PCs, tablets, and smartphones, as well as share results via the cloud. The book helps you save time by preparing the underlying data correctly without needing an IT department to prepare it for you. What You Will Learn Deliver attention-grabbing information, turning data into insight Find new insights as you chop and tweak your data as never before Build a data narrative through interactive reports with drill-through and cross-page slicing Mash up data from multiple sources into a cleansed and coherent data model Build interdependent charts, maps, and tables to deliver visually stunninginformation Create dashboards that help in monitoring key performance indicators of your business Adapt delivery to mobile devices such as phones and tablets Who This Book Is For Power users who are ready to step up to the big leagues by going beyond what Microsoft Excel by itself can offer. The book also is for line-of-business managers who are starved for actionable data needed to make decisions about their business. And the book is for BI analysts looking for an easy-to-use tool to analyze data and share results with C-suite colleagues they support.

This audio blog discusses cloud adoption and how data teams will migrate an increasing portion of their on-premises operational and analytics workloads to the cloud. They can best meet budget and project requirements by using data streaming technologies such as change data capture (CDC), which replicates real-time updates between data source and target.

Originally published at: https://www.eckerson.com/articles/the-next-wave-of-cloud-migrations-needs-data-streaming

The Spark distributed data processing platform provides an easy-to-implement tool for ingesting, streaming, and processing data from any source. In Spark in Action, Second Edition, you’ll learn to take advantage of Spark’s core features and incredible processing speed, with applications including real-time computation, delayed evaluation, and machine learning. Spark skills are a hot commodity in enterprises worldwide, and with Spark’s powerful and flexible Java APIs, you can reap all the benefits without first learning Scala or Hadoop. About the Technology Analyzing enterprise data starts by reading, filtering, and merging files and streams from many sources. The Spark data processing engine handles this varied volume like a champ, delivering speeds 100 times faster than Hadoop systems. Thanks to SQL support, an intuitive interface, and a straightforward multilanguage API, you can use Spark without learning a complex new ecosystem. About the Book Spark in Action, Second Edition, teaches you to create end-to-end analytics applications. In this entirely new book, you’ll learn from interesting Java-based examples, including a complete data pipeline for processing NASA satellite data. And you’ll discover Java, Python, and Scala code samples hosted on GitHub that you can explore and adapt, plus appendixes that give you a cheat sheet for installing tools and understanding Spark-specific terms. What's Inside Writing Spark applications in Java Spark application architecture Ingestion through files, databases, streaming, and Elasticsearch Querying distributed datasets with Spark SQL About the Reader This book does not assume previous experience with Spark, Scala, or Hadoop. About the Author Jean-Georges Perrin is an experienced data and software architect. He is France’s first IBM Champion and has been honored for 12 consecutive years. Quotes This book reveals the tools and secrets you need to drive innovation in your company or community. - Rob Thomas, IBM An indispensable, well-paced, and in-depth guide. A must-have for anyone into big data and real-time stream processing. - Anupam Sengupta, GuardHat Inc. This book will help spark a love affair with distributed processing. - Conor Redmond, InComm Product Control Currently the best book on the subject! - Markus Breuer, Materna IPS

A hallmark of the analytics community is the generosity with which ideas and wisdom are shared. One of the largest analytics conferences each year is Adobe Summit. One of the most followed Tims on the planet wrote a book called Tribe of Mentors: Short Life Advice from the Best in the World. Jen Yacenda and Eric Matisoff mixed all three of these truths together in preparation for an hour-long presentation chock full of excellent career advice. And then Adobe Summit went virtual, and their session got drastically shortened. On this episode, Jen joined the gang to talk through (some of) the 11 questions that they posed to 38 analysts, the responses they got, and how she and the hosts answered the questions themselves. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Summary Data lakes offer a great deal of flexibility and the potential for reduced cost for your analytics, but they also introduce a great deal of complexity. What used to be entirely managed by the database engine is now a composition of multiple systems that need to be properly configured to work in concert. In order to bring the DBA into the new era of data management the team at Upsolver added a SQL interface to their data lake platform. In this episode Upsolver CEO Ori Rafael and CTO Yoni Iny describe how they have grown their platform deliberately to allow for layering SQL on top of a robust foundation for creating and operating a data lake, how to bring more people on board to work with the data being collected, and the unique benefits that a data lake provides. This was an interesting look at the impact that the interface to your data can have on who is empowered to work with it.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management What are the pieces of advice that you wish you had received early in your career of data engineering? If you hand a book to a new data engineer, what wisdom would you add to it? I’m working with O’Reilly on a project to collect the 97 things that every data engineer should know, and I need your help. Go to dataengineeringpodcast.com/97things to add your voice and share your hard-earned expertise. When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! You listen to this show because you love working with data and want to keep your skills up to date. Machine learning is finding its way into every aspect of the data landscape. Springboard has partnered with us to help you take the next step in your career by offering a scholarship to their Machine Learning Engineering career track program. In this online, project-based course every student is paired with a Machine Learning expert who provides unlimited 1:1 mentorship support throughout the program via video conferences. You’ll build up your portfolio of machine learning projects and gain hands-on experience in writing machine learning algorithms, deploying models into production, and managing the lifecycle of a deep learning prototype. Springboard offers a job guarantee, meaning that you don’t have to pay for the program until you get a job in the space. The Data Engineering Podcast is exclusively offering listeners 20 scholarships of $500 to eligible applicants. It only takes 10 minutes and there’s no obligation. Go to dataengineeringpodcast.com/springboard and apply today! Make sure to use the code AISPRINGBOARD when you enroll. Your host is Tobias Macey and today I’m interviewing Ori Rafael and Yoni Iny about building a data lake for the DBA at Upsolver

Interview

Introduction How did you get involved in the area of data management? Can you start by sharing your definition of what a data lake is and what it is comprised of? We talked last in November of 2018. How has the landscape of data lake technologies and adoption changed in that time?

How has Upsolver changed or evolved since we last spoke?

How has the evolution of the underlying technologies impacted your implementation and overall product strategy?

What are some of the common challenges that accompany a data lake implementation? How do those challenges influence the adoption or viability of a data lake? How does the introduction of a universal SQL layer change the staffing requirements for building and maintaining a data lake?

What are the advantages of a data lake over a data warehouse if everything is being managed via SQL anyway?

What are some of the underlying realities of the data systems that power the lake which will eventually need to be understood by the operators of the platform? How is the SQL layer in Upsolver implemented?

What are the most challenging or complex aspects of managing the underlying technologies to provide automated partitioning, indexing, etc.?

What are the main concepts that you need to educate your customers on? What are some of the pitfalls that users should be aware of? What features of your platform are often overlooked or underutilized which you think should be more widely adopted? What have you found to be the most interesting, unexpected, or challenging lessons learned while building the technical and business elements of Upsolver? What do you have planned for the future?

Contact Info

Ori

LinkedIn

Yoni

yoniiny on GitHub LinkedIn

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

Upsolver

Podcast Episode

DBA == Database Administrator IDF == Israel Defense Forces Data Lake Eventual Consistency Apache Spark Redshift Spectrum Azure Synapse Analytics SnowflakeDB

Podcast Episode

BigQuery Presto

Podcast Episode

Apache Kafka Cartesian Product kSQLDB

Podcast Episode

Eventador

Podcast Episode

Materialize

Podcast Episode

Common Table Expressions Lambda Architecture Kappa Architecture Apache Flink

Podcast Episode

Reinforcement Learning Cloudformation GDPR

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Support Data Engineering Podcast

Master machine learning with SAS Viya! Machine learning can feel intimidating for new practitioners. Machine Learning with SAS Viya provides everything you need to know to get started with machine learning in SAS Viya, including decision trees, neural networks, and support vector machines. The analytics life cycle is covered from data preparation and discovery to deployment. Working with open-source code? Machine Learning with SAS Viya has you covered – step-by-step instructions are given on how to use SAS Model Manager tools with open source. SAS Model Studio features are highlighted to show how to carry out machine learning in SAS Viya. Demonstrations, practice tasks, and quizzes are included to help sharpen your skills. In this book, you will learn about: Supervised and unsupervised machine learning Data preparation and dealing with missing and unstructured data Model building and selection Improving and optimizing models Model deployment and monitoring performance

Free Data Storytelling Training Note: Upcoming BI Data Storytelling Mastery Accelerator 3-Day Live Workshops have been canceled due to the COVID-19 crisis. However, 2-Day Online Workshops (Livestream recordings) will be available. Many BI teams are still struggling to deliver consistent, high-engaging analytics their users love. At the end of the workshop, you'll leave with a clear BI delivery action plan. Register today! In this episode, you'll learn: [08:07] User Expectations: Why are they important to set? [18:05] Key Quote: For me, it was obviously a needed skill set to add. - Brent Warren [18:45] Swiss Knife: Unappreciated, underpaid, and non-existent type that rebuilt metrics and reports without help. For full show notes, and the links mentioned visit: https://bibrainz.com/podcast/53  

Enjoyed the Show?  Please leave us a review on iTunes.

Send us a text  Want to be featured as a guest on Making Data Simple? Reach out to us at [[email protected]] and tell us why you should be next.

Abstract Hosted by Al Martin, VP, Data and AI Expert Services and Learning at IBM , Making Data Simple provides the latest thinking on big data, A.I., and the implications for the enterprise from a range of experts.

This week on Making Data Simple, we have Priya Srinivasan Director, IBM Data and AI Expert Labs SWAT. In this week’s podcast we talk about Data (world’s new oil), AI (world’s refinery), Cloud (the pipeline), Unified Governance and Data Ops, Security, Analytics and Services.

Show Notes 4:50 Expert labs and what it means 5:52 Al talks how SWAT was for him 6:10 Priya discusses SWAT now 7:18 Priya gives examples of SWAT 9:04 Deliverables from Expert Labs 13:37 Al talks about Services 17:30 Priya talks about solving long term problems 20:04 Priya discusses GROW (Guidance, Resources, and Outreach for Women) 21:25 Al asks Priya what excites her

Linkedin - https://www.linkedin.com/in/sripriya-srinivasan-385a0812/ Twitter - https://twitter.com/Priyavikram2

GROW - https://w3-connections.ibm.com/wikis/home?lang=en-us#!/wiki/W7e7074647e13_420c_9abf_875dd706e4b4/page/Welcome%20to%20GROW%20in%20Hybrid%20Cloud%20-%20Guidance,%20Resources,%20Outreach%20for%20Women%20in%20Hybrid%20Cloud

Connect with the Team Producer Kate Brown - LinkedIn. Producer Michael Sestak - LinkedIn. Producer Meighann Helene - LinkedIn.

Producer Steve Templeton - LinkedIn. Host Al Martin - LinkedIn and Twitter. Want to be featured as a guest on Making Data Simple? Reach out to us at [email protected] and tell us why you should be next. The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun.

Summary Gaining a complete view of the customer journey is especially difficult in B2B companies. This is due to the number of different individuals involved and the myriad ways that they interface with the business. Dreamdata integrates data from the multitude of platforms that are used by these organizations so that they can get a comprehensive view of their customer lifecycle. In this episode Ole Dallerup explains how Dreamdata was started, how their platform is architected, and the challenges inherent to data management in the B2B space. This conversation is a useful look into how data engineering and analytics can have a direct impact on the success of the business.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management What are the pieces of advice that you wish you had received early in your career of data engineering? If you hand a book to a new data engineer, what wisdom would you add to it? I’m working with O’Reilly on a project to collect the 97 things that every data engineer should know, and I need your help. Go to dataengineeringpodcast.com/97things to add your voice and share your hard-earned expertise. When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, a 40Gbit public network, fast object storage, and a brand new managed Kubernetes platform, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. And for your machine learning workloads, they’ve got dedicated CPU and GPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! You listen to this show because you love working with data and want to keep your skills up to date. Machine learning is finding its way into every aspect of the data landscape. Springboard has partnered with us to help you take the next step in your career by offering a scholarship to their Machine Learning Engineering career track program. In this online, project-based course every student is paired with a Machine Learning expert who provides unlimited 1:1 mentorship support throughout the program via video conferences. You’ll build up your portfolio of machine learning projects and gain hands-on experience in writing machine learning algorithms, deploying models into production, and managing the lifecycle of a deep learning prototype. Springboard offers a job guarantee, meaning that you don’t have to pay for the program until you get a job in the space. The Data Engineering Podcast is exclusively offering listeners 20 scholarships of $500 to eligible applicants. It only takes 10 minutes and there’s no obligation. Go to dataengineeringpodcast.com/springboard and apply today! Make sure to use the code AISPRINGBOARD when you enroll. Your host is Tobias Macey and today I’m interviewing Ole Dallerup about Dreamdata, a platform for simplifying data integration for B2B companies

Interview

Introduction How did you get involved in the area of data management? Can you start by describing what you are building at Dreamata?

What was your inspiration for starting a company and what keeps you motivated?

How do the data requirements differ between B2C and B2B companies? What are the challenges that B2B companies face in gaining visibility across the lifecycle of their customers?

How does that lack of visibility impact the viability or growth potential of the business? What are the factors that contribute to silos in visibility of customer activity within a business?

What are the data sources that you are dealing with to generate meaningful analytics for your customers? What are some of the challenges that business face in either generating or collecting useful informati

This book, the first in a series of three, provides a look at the foundations of artificial intelligence and analytics and why readers need an unbiased understanding of the subject.

Use this guide to one of SQL Server 2019’s most impactful features—Big Data Clusters. You will learn about data virtualization and data lakes for this complete artificial intelligence (AI) and machine learning (ML) platform within the SQL Server database engine. You will know how to use Big Data Clusters to combine large volumes of streaming data for analysis along with data stored in a traditional database. For example, you can stream large volumes of data from Apache Spark in real time while executing Transact-SQL queries to bring in relevant additional data from your corporate, SQL Server database. Filled with clear examples and use cases, this book provides everything necessary to get started working with Big Data Clusters in SQL Server 2019. You will learn about the architectural foundations that are made up from Kubernetes, Spark, HDFS, and SQL Server on Linux. You then are shown how to configure and deploy Big Data Clusters in on-premises environments or in the cloud. Next, you are taught about querying. You will learn to write queries in Transact-SQL—taking advantage of skills you have honed for years—and with those queries you will be able to examine and analyze data from a wide variety of sources such as Apache Spark. Through the theoretical foundation provided in this book and easy-to-follow example scripts and notebooks, you will be ready to use and unveil the full potential of SQL Server 2019: combining different types of data spread across widely disparate sources into a single view that is useful for business intelligence and machine learning analysis. What You Will Learn Install, manage, and troubleshoot Big Data Clusters in cloud or on-premise environments Analyze large volumes of data directly from SQL Server and/or Apache Spark Manage data stored in HDFS from SQL Server as if it wererelational data Implement advanced analytics solutions through machine learning and AI Expose different data sources as a single logical source using data virtualization Who This Book Is For Data engineers, data scientists, data architects, and database administrators who want to employ data virtualization and big data analytics in their environments

Free Data Storytelling Training Attend our FREE 'How to be the Chief Data Storyteller in your Org - Part 2 using our Analytics Design Guide' training at webinars.bidatastorytelling.com and download the FREE 50-page Guide! In this episode, you'll learn: [09:08] Key Quote: It's really changing how we think about management, how we think about working together as a team. - Nick Caldwell [19:37] Today's Topic: Where analytics is today, where it's going, and a future forecast? [21:58] Data Experiences Defined: Moving beyond static dashboards to build experiences that allow anyone in a company to get their job done. For full show notes, and the links mentioned visit: https://bibrainz.com/podcast/52  

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While several market-leading companies have successfully transformed their business models by following data- and AI-driven paths, the vast majority have yet to reap the benefits. How can your business and analytics units gain a competitive advantage by capturing the full potential of this predictive revolution? This practical guide presents a battle-tested end-to-end method to help you translate business decisions into tractable prescriptive solutions using data and AI as fundamental inputs. Author Daniel Vaughan shows data scientists, analytics practitioners, and others interested in using AI to transform their businesses not only how to ask the right questions but also how to generate value using modern AI technologies and decision-making principles. You’ll explore several use cases common to many enterprises, complete with examples you can apply when working to solve your own issues. Break business decisions into stages that can be tackled using different skills from the analytical toolbox Identify and embrace uncertainty in decision making and protect against common human biases Customize optimal decisions to different customers using predictive and prescriptive methods and technologies Ask business questions that create high value through AI- and data-driven technologies