Send us a text This week, host Al Martin welcomes IBMers Reena Ganga and Jenna Goldberg. Reena and Jenna both serve as senior designers working at the intersection of Augmented Reality and Big Data. Both are passionate advocates for user experience design and the Design Thinking method, determined to improve — and illuminate — the way we experience and understand data.

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Show Notes 00:10 - Connect with Producer Steve Moore on LinkedIn and Twitter.

00:15 - Connect with Producer Liam Seston on LinkedIn and Twitter.

00:20 - Connect with Producer Rachit Sharma on LinkedIn.

00:25 - Connect with Host Al Martin on LinkedIn and Twitter.

00:40 - Connect with Rena on LinkedIn & Twitter.

00:45 - Connect with Jenna on LinkedIn & Twitter.

08:40 - What is User Experience Design (UXD)?

12:40 - What is Data Visualization? 17:00 - What is Augmented Reality (AR)? 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 Database indexes are critical to ensure fast lookups of your data, but they are inherently tied to the database engine. Pilosa is rewriting that equation by providing a flexible, scalable, performant engine for building an index of your data to enable high-speed aggregate analysis. In this episode Seebs explains how Pilosa fits in the broader data landscape, how it is architected, and how you can start using it for your own analysis. This was an interesting exploration of a different way to look at what a database can be.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management 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, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. And for your machine learning workloads, they just announced dedicated CPU 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! Alluxio is an open source, distributed data orchestration layer that makes it easier to scale your compute and your storage independently. By transparently pulling data from underlying silos, Alluxio unlocks the value of your data and allows for modern computation-intensive workloads to become truly elastic and flexible for the cloud. With Alluxio, companies like Barclays, JD.com, Tencent, and Two Sigma can manage data efficiently, accelerate business analytics, and ease the adoption of any cloud. Go to dataengineeringpodcast.com/alluxio today to learn more and thank them for their support. Understanding how your customers are using your product is critical for businesses of any size. To make it easier for startups to focus on delivering useful features Segment offers a flexible and reliable data infrastructure for your customer analytics and custom events. You only need to maintain one integration to instrument your code and get a future-proof way to send data to over 250 services with the flip of a switch. Not only does it free up your engineers’ time, it lets your business users decide what data they want where. Go to dataengineeringpodcast.com/segmentio today to sign up for their startup plan and get $25,000 in Segment credits and $1 million in free software from marketing and analytics companies like AWS, Google, and Intercom. On top of that you’ll get access to Analytics Academy for the educational resources you need to become an expert in data analytics for measuring product-market fit. You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference. Go to dataengineeringpodcast.com/conferences to learn more and take advantage of our partner discounts when you register. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Your host is Tobias Macey and today I’m interviewing Seebs about Pilosa, an open source, distributed bitmap index

Interview

Introduction How did you get involved in the area of data

Summary How much time do you spend maintaining your data pipeline? How much end user value does that provide? Raghu Murthy founded DataCoral as a way to abstract the low level details of ETL so that you can focus on the actual problem that you are trying to solve. In this episode he explains his motivation for building the DataCoral platform, how it is leveraging serverless computing, the challenges of delivering software as a service to customer environments, and the architecture that he has designed to make batch data management easier to work with. This was a fascinating conversation with someone who has spent his entire career working on simplifying complex data problems.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management 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, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. And for your machine learning workloads, they just announced dedicated CPU 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! Managing and auditing access to your servers and databases is a problem that grows in difficulty alongside the growth of your teams. If you are tired of wasting your time cobbling together scripts and workarounds to give your developers, data scientists, and managers the permissions that they need then it’s time to talk to our friends at strongDM. They have built an easy to use platform that lets you leverage your company’s single sign on for your data platform. Go to dataengineeringpodcast.com/strongdm today to find out how you can simplify your systems. Alluxio is an open source, distributed data orchestration layer that makes it easier to scale your compute and your storage independently. By transparently pulling data from underlying silos, Alluxio unlocks the value of your data and allows for modern computation-intensive workloads to become truly elastic and flexible for the cloud. With Alluxio, companies like Barclays, JD.com, Tencent, and Two Sigma can manage data efficiently, accelerate business analytics, and ease the adoption of any cloud. Go to dataengineeringpodcast.com/alluxio today to learn more and thank them for their support. You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference. Go to dataengineeringpodcast.com/conferences to learn more and take advantage of our partner discounts when you register. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Your host is Tobias Macey and today I’m interviewing Raghu Murthy about DataCoral, a platform that offers a fully managed and secure stack in your own cloud that delivers data to where you need it

Interview

Introduction How did you get involved in the area of data management? Can you start by explaining what DataCoral is and your motivation for founding it? How does the data-centric approa

Summary Analytics projects fail all the time, resulting in lost opportunities and wasted resources. There are a number of factors that contribute to that failure and not all of them are under our control. However, many of them are and as data engineers we can help to keep our projects on the path to success. Eugene Khazin is the CEO of PrimeTSR where he is tasked with rescuing floundering analytics efforts and ensuring that they provide value to the business. In this episode he reflects on the ways that data projects can be structured to provide a higher probability of success and utility, how data engineers can get throughout the project lifecycle, and how to salvage a failed project so that some value can be gained from the effort.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management 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, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. And for your machine learning workloads, they just announced dedicated CPU 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! Managing and auditing access to your servers and databases is a problem that grows in difficulty alongside the growth of your teams. If you are tired of wasting your time cobbling together scripts and workarounds to give your developers, data scientists, and managers the permissions that they need then it’s time to talk to our friends at strongDM. They have built an easy to use platform that lets you leverage your company’s single sign on for your data platform. Go to dataengineeringpodcast.com/strongdm today to find out how you can simplify your systems. Alluxio is an open source, distributed data orchestration layer that makes it easier to scale your compute and your storage independently. By transparently pulling data from underlying silos, Alluxio unlocks the value of your data and allows for modern computation-intensive workloads to become truly elastic and flexible for the cloud. With Alluxio, companies like Barclays, JD.com, Tencent, and Two Sigma can manage data efficiently, accelerate business analytics, and ease the adoption of any cloud. Go to dataengineeringpodcast.com/alluxio today to learn more and thank them for their support. You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference. Go to dataengineeringpodcast.com/conferences to learn more and take advantage of our partner discounts when you register. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Your host is Tobias Macey and today I’m interviewing Eugene Khazin about the leading causes for failure in analytics projects

Interview

Introduction How did you get involved in the area of data management? The term "analytics" has grown to mean many different things to different people, so can you start by sharing your definition of what is in scope for an "analytics project" for the purposes of this discussion?

Wh

This book, Mastering MongoDB 4.x, provides an in-depth exploration of MongoDB's features and capabilities, empowering readers to create high-performance and fault-tolerant database solutions. Through practical examples and clear explanations, you will learn how to implement complex queries, optimize database performance, manage large-scale clusters, and ensure robust failover and backup strategies. What this Book will help me do Understand advanced querying techniques and best practices in data indexing and management. Effectively configure and monitor MongoDB instances for scalability and optimized performance. Master techniques for replication and sharding to support high-availability systems. Deploy MongoDB-based applications seamlessly across on-premise and cloud environments. Learn to integrate MongoDB with modern technologies like big data platforms, containers, and IoT applications. Author(s) Alex Giamas is a seasoned database administrator and developer with significant experience in working with both relational and non-relational databases. Having authored numerous articles and given lectures on MongoDB and other data management technologies, Alex brings practical insights to his writing. He emphasizes real-world applications with examples drawn from his extensive career. Who is it for? This book is designed for developers and database administrators already familiar with MongoDB and basic database concepts, who are looking to enhance their expertise for implementing advanced MongoDB solutions. It is also suitable for professionals aspiring to earn MongoDB certifications and expand their skills to manage large, high-performance database systems efficiently.

Dive into the exciting world of big data analytics with 'Hands-On Big Data Analytics with PySpark'. This practical guide offers you the tools and knowledge to tackle massive datasets using PySpark. By exploring real-world examples, you'll learn to unleash the power of distributed systems to analyze and manipulate data at scale. What this Book will help me do Master using PySpark to handle large and complex datasets efficiently and effectively. Develop skills to optimize Spark programs using best practices like reducing shuffle operations. Learn to set up a PySpark environment, process data from platforms like HDFS, Hive, and S3. Enhance your data analytics capabilities by implementing powerful SQL queries and data visualizations. Understand testing and debugging techniques to build reliable, production-quality data pipelines. Author(s) Authored by Rudy Lai and Bartłomiej Potaczek, both seasoned data engineers and authors in the big data field. Rudy and Bartłomiej bring their extensive experience working with distributed systems and scalable data architectures into this book. Their approach is hands-on, focusing on real-world applications and best practices. Who is it for? This book is tailored for data scientists, engineers, and developers eager to advance their big data analytics capabilities. Whether you're new to big data or experienced with other analytics frameworks, this book will equip you with practical knowledge to utilize PySpark for scalable data solutions.

Summary Data integration is one of the most challenging aspects of any data platform, especially as the variety of data sources and formats grow. Enterprise organizations feel this acutely due to the silos that occur naturally across business units. The CluedIn team experienced this issue first-hand in their previous roles, leading them to build a business aimed at building a managed data fabric for the enterprise. In this episode Tim Ward, CEO of CluedIn, joins me to explain how their platform is architected, how they manage the task of integrating with third-party platforms, automating entity extraction and master data management, and the work of providing multiple views of the same data for different use cases. I highly recommend listening closely to his explanation of how they manage consistency of the data that they process across different storage backends.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management 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, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. And for your machine learning workloads, they just announced dedicated CPU 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! Managing and auditing access to your servers and databases is a problem that grows in difficulty alongside the growth of your teams. If you are tired of wasting your time cobbling together scripts and workarounds to give your developers, data scientists, and managers the permissions that they need then it’s time to talk to our friends at strongDM. They have built an easy to use platform that lets you leverage your company’s single sign on for your data platform. Go to dataengineeringpodcast.com/strongdm today to find out how you can simplify your systems. Alluxio is an open source, distributed data orchestration layer that makes it easier to scale your compute and your storage independently. By transparently pulling data from underlying silos, Alluxio unlocks the value of your data and allows for modern computation-intensive workloads to become truly elastic and flexible for the cloud. With Alluxio, companies like Barclays, JD.com, Tencent, and Two Sigma can manage data efficiently, accelerate business analytics, and ease the adoption of any cloud. Go to dataengineeringpodcast.com/alluxio today to learn more and thank them for their support. You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference. Go to dataengineeringpodcast.com/conferences to learn more and take advantage of our partner discounts when you register. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Your host is Tobias Macey and today I’m interviewing Tim Ward about CluedIn, an integration platform for implementing your companies data fabric

Interview

Introduction

How did you get involved in t

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

Abstract As big data becomes more ubiquitous, businesses are wondering how they can best leverage it to gain insight into their most important business questions. Using machine learning (ML) and deep learning (DL) in big data environments can identify historical patterns and build artificial intelligence (AI) models that can help businesses to improve customer experience, add services and offerings, identify new revenue streams or lines of business (LOBs), and optimize business or manufacturing operations. The power of AI for predictive analytics is being harnessed across all industries, so it is important that businesses familiarize themselves with all of the tools and techniques that are available for integration with their data lake environments. In this IBM® Redbooks® publication, we cover the best practices for deploying and integrating some of the best AI solutions on the market, including: IBM Watson Machine Learning Accelerator (see note for product naming) IBM Watson Studio Local IBM Power Systems™ IBM Spectrum™ Scale IBM Data Science Experience (IBM DSX) IBM Elastic Storage™ Server Hortonworks Data Platform (HDP) Hortonworks DataFlow (HDF) H2O Driverless AI We map out all the integrations that are possible with our different AI solutions and how they can integrate with your existing or new data lake. We also walk you through some of our client use cases and show you how some of the industry leaders are using Hortonworks, IBM PowerAI, and IBM Watson Studio Local to drive decision making. We also advise you on your deployment options, when to use a GPU, and why you should use the IBM Elastic Storage Server (IBM ESS) to improve storage management. Lastly, we describe how to integrate IBM Watson Machine Learning Accelerator and Hortonworks with or without IBM Watson Studio Local, how to access real-time data, and security. Note: IBM Watson Machine Learning Accelerator is the new product name for IBM PowerAI Enterprise. Note: Hortonworks merged with Cloudera in January 2019. The new company is called Cloudera. References to Hortonworks as a business entity in this publication are now referring to the merged company. Product names beginning with Hortonworks continue to be marketed and sold under their original names.

Summary Delivering a data analytics project on time and with accurate information is critical to the success of any business. DataOps is a set of practices to increase the probability of success by creating value early and often, and using feedback loops to keep your project on course. In this episode Chris Bergh, head chef of Data Kitchen, explains how DataOps differs from DevOps, how the industry has begun adopting DataOps, and how to adopt an agile approach to building your data platform.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management 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, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. And for your machine learning workloads, they just announced dedicated CPU 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! Managing and auditing access to your servers and databases is a problem that grows in difficulty alongside the growth of your teams. If you are tired of wasting your time cobbling together scripts and workarounds to give your developers, data scientists, and managers the permissions that they need then it’s time to talk to our friends at strongDM. They have built an easy to use platform that lets you leverage your company’s single sign on for your data platform. Go to dataengineeringpodcast.com/strongdm today to find out how you can simplify your systems. "There aren’t enough data conferences out there that focus on the community, so that’s why these folks built a better one": Data Council is the premier community powered data platforms & engineering event for software engineers, data engineers, machine learning experts, deep learning researchers & artificial intelligence buffs who want to discover tools & insights to build new products. This year they will host over 50 speakers and 500 attendees (yeah that’s one of the best "Attendee:Speaker" ratios out there) in San Francisco on April 17-18th and are offering a $200 discount to listeners of the Data Engineering Podcast. Use code: DEP-200 at checkout You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference. Go to dataengineeringpodcast.com/conferences to learn more and take advantage of our partner discounts when you register. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Your host is Tobias Macey and today I’m interviewing Chris Bergh about the current state of DataOps and why it’s more than just DevOps for data

Interview

Introduction How did you get involved in the area of data management? We talked last year about what DataOps is, but can you give a quick overview of how the industry has changed or updated the definition since then?

It is easy to draw parallels between DataOps and DevOps, can you provide some clarity as to how they are different?

How has the conversat

Send us a text Rob Thomas, leader of IBM’s Data and AI division, talks with host Al Martin about the need to de-mystify AI. In particular, Rob recommends a "fail fast" approach to data science: run wide-ranging but short-term experiments — and expect disappointment on the way to insight. This episode offers a host of such suggestions, plus thoughtful leadership advice and tips for motivation. Part 1 of 2.

Show Notes 00:00 - Check us out on YouTube and SoundCloud.  00:10 - Connect with Producer Steve Moore on LinkedIn and Twitter.  00:15 - Connect with Producer Liam Seston on LinkedIn and Twitter.  00:20 - Connect with Producer Rachit Sharma on LinkedIn.  00:25 - Connect with Host Al Martin on LinkedIn and Twitter.  00:55 - Connect with Rob Thomas on LinkedIn and Twitter. 04:01 - Discover what big data and A.I. have in store.  06:22 - Learn more about IBM's Think conference here. 06:48 - Read more on Watson anywhere here. 10:24 - There is no A.I. without I.A. 23:21 - Check out Al's talk at Think 2019 with Jeff Jonas here. 23:51 - Check out this interview with Rob Thomas at Think 2019 here. 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.

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

Summary Customer analytics is a problem domain that has given rise to its own industry. In order to gain a full understanding of what your users are doing and how best to serve them you may need to send data to multiple services, each with their own tracking code or APIs. To simplify this process and allow your non-engineering employees to gain access to the information they need to do their jobs Segment provides a single interface for capturing data and routing it to all of the places that you need it. In this interview Segment CTO and co-founder Calvin French-Owen explains how the company got started, how it manages to multiplex data streams from multiple sources to multiple destinations, and how it can simplify your work of gaining visibility into how your customers are engaging with your business.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management 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, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. And for your machine learning workloads, they just announced dedicated CPU 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! Managing and auditing access to your servers and databases is a problem that grows in difficulty alongside the growth of your teams. If you are tired of wasting your time cobbling together scripts and workarounds to give your developers, data scientists, and managers the permissions that they need then it’s time to talk to our friends at strongDM. They have built an easy to use platform that lets you leverage your company’s single sign on for your data platform. Go to dataengineeringpodcast.com/strongdm today to find out how you can simplify your systems. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. To help other people find the show please leave a review on iTunes and tell your friends and co-workers You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with O’Reilly Media for the Strata conference in San Francisco on March 25th and the Artificial Intelligence conference in NYC on April 15th. Here in Boston, starting on May 17th, you still have time to grab a ticket to the Enterprise Data World, and from April 30th to May 3rd is the Open Data Science Conference. Go to dataengineeringpodcast.com/conferences to learn more and take advantage of our partner discounts when you register. Your host is Tobias Macey and today I’m interviewing Calvin French-Owen about the data platform that Segment has built to handle multiplexing continuous streams of data from multiple sources to multiple destinations

Interview

Introduction How did you get involved in the area of data management? Can you start by explaining what Segment is and how the business got started?

What are some of the primary ways that your customers are using the Segment platform? How have the capabilities and use cases of the Segment platform changed since it was first launched?

Layered on top of the data integration platform you have added the concepts of Protocols and Personas. Can you explain how each of those products fit into the over

"Mastering Hadoop 3" is your in-depth guide to understanding and mastering the advanced features of the Hadoop ecosystem. With a focus on distributed computing and data processing, this book covers essential tools such as YARN, MapReduce, and Apache Spark to help you build scalable, efficient data pipelines. What this Book will help me do Gain a comprehensive understanding of Hadoop Distributed File System (HDFS) and YARN for effective resource management. Master data processing with MapReduce and learn to integrate with real-time processing engines like Spark and Flink. Develop and secure enterprise-grade Hadoop-based data pipelines by implementing robust security and governance measures. Explore techniques for batch data processing, data modeling, and designing applications tailored for Hadoop environments. Understand best practices for optimizing and troubleshooting Hadoop clusters for enhanced performance and reliability. Author(s) The authors, including None Wong, None Singh, and None Kumar, bring together years of experience in big data engineering, distributed systems, and enterprise application development. They aim to provide a clear pathway to mastering Hadoop ecosystem tools. Who is it for? This book is ideal for budding big data professionals who have some familiarity with Java and basic Hadoop concepts and wish to elevate their expertise. If you're a Hadoop career practitioner keen to expand your understanding of the ecosystem's advanced capabilities or a professional looking to implement Hadoop in organizational workflows, this book is well-suited for you.

Summary Deep learning is the latest class of technology that is gaining widespread interest. As data engineers we are responsible for building and managing the platforms that power these models. To help us understand what is involved, we are joined this week by Thomas Henson. In this episode he shares his experiences experimenting with deep learning, what data engineers need to know about the infrastructure and data requirements to power the models that your team is building, and how it can be used to supercharge our ETL pipelines.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management 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, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. 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! Managing and auditing access to your servers and databases is a problem that grows in difficulty alongside the growth of your teams. If you are tired of wasting your time cobbling together scripts and workarounds to give your developers, data scientists, and managers the permissions that they need then it’s time to talk to our friends at strongDM. They have built an easy to use platform that lets you leverage your company’s single sign on for your data platform. Go to dataengineeringpodcast.com/strongdm today to find out how you can simplify your systems. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media. Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data platforms. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss the Strata conference in San Francisco on March 25th and the Artificial Intelligence conference in NYC on April 15th, both run by our friends at O’Reilly Media. Go to dataengineeringpodcast.com/stratacon and dataengineeringpodcast.com/aicon to register today and get 20% off Your host is Tobias Macey and today I’m interviewing Thomas Henson about what data engineers need to know about deep learning, including how to use it for their own projects

Interview

Introduction How did you get involved in the area of data management? Can you start by giving an overview of what deep learning is for anyone who isn’t familiar with it? What has been your personal experience with deep learning and what set you down that path? What is involved in building a data pipeline and production infrastructure for a deep learning product?

How does that differ from other types of analytics projects such as data warehousing or traditional ML?

For anyone who is in the early stages of a deep learning project, what are some of the edge cases or gotchas that they should be aware of? What are your opinions on the level of involvement/understanding that data engineers should have with the analytical products that are being built with the information we collect and curate? What are some ways that we can use deep learning as part of the data management process?

How does that shift the infrastructure requirements for our platforms?

Cloud providers have b

Summary Distributed storage systems are the foundational layer of any big data stack. There are a variety of implementations which support different specialized use cases and come with associated tradeoffs. Alluxio is a distributed virtual filesystem which integrates with multiple persistent storage systems to provide a scalable, in-memory storage layer for scaling computational workloads independent of the size of your data. In this episode Bin Fan explains how he got involved with the project, how it is implemented, and the use cases that it is particularly well suited for. If your storage and compute layers are too tightly coupled and you want to scale them independently then Alluxio is the tool for the job.

Introduction

Hello and welcome to the Data Engineering Podcast, the show about modern data management 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 Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media. Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Your host is Tobias Macey and today I’m interviewing Bin Fan about Alluxio, a distributed virtual filesystem for unified access to disparate data sources

Interview

Introduction How did you get involved in the area of data management? Can you start by explaining what Alluxio is and the history of the project?

What are some of the use cases that Alluxio enables?

How is Alluxio implemented and how has its architecture evolved over time?

What are some of the techniques that you use to mitigate the impact of latency, particularly when interfacing with storage systems across cloud providers and private data centers?

When dealing with large volumes of data over time it is often necessary to age out older records to cheaper storage. What capabilities does Alluxio provide for that lifecycle management? What are some of the most complex or challenging aspects of providing a unified abstraction across disparate storage platforms?

What are the tradeoffs that are made to provide a single API across systems with varying capabilities?

Testing and verification of distributed systems is a complex undertaking. Can you describe the approach that you use to ensure proper functionality of Alluxio as part of the development and release process?

In order to allow for this large scale testing with any regularity it must be straightforward to deploy and configure Alluxio. What are some of the mechanisms that you have built into the platform to simplify the operational aspects?

Can you describe a typical system topology that incorporates Alluxio? For someone planning a deployment of Alluxio, what should they be considering in terms of system requirements and deployment topologies?

What are some edge cases or operational complexities that they should be aware of?

What are some cases where Alluxio is the wrong choice?

What are some projects or products that provide a similar capability to Alluxio?

What do you have planned for the future of the Alluxio project and company?

Contact Info

LinkedIn @binfan on Twitter

Parting Question

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

Links

Alluxio

Project Company

Carnegie Me

A guide to the systematic analytical results for ridge, LASSO, preliminary test, and Stein-type estimators with applications Theory of Ridge Regression Estimation with Applications offers a comprehensive guide to the theory and methods of estimation. Ridge regression and LASSO are at the center of all penalty estimators in a range of standard models that are used in many applied statistical analyses. Written by noted experts in the field, the book contains a thorough introduction to penalty and shrinkage estimation and explores the role that ridge, LASSO, and logistic regression play in the computer intensive area of neural network and big data analysis. Designed to be accessible, the book presents detailed coverage of the basic terminology related to various models such as the location and simple linear models, normal and rank theory-based ridge, LASSO, preliminary test and Stein-type estimators. The authors also include problem sets to enhance learning. This book is a volume in the Wiley Series in Probability and Statistics series that provides essential and invaluable reading for all statisticians. This important resource: Offers theoretical coverage and computer-intensive applications of the procedures presented Contains solutions and alternate methods for prediction accuracy and selecting model procedures Presents the first book to focus on ridge regression and unifies past research with current methodology Uses R throughout the text and includes a companion website containing convenient data sets Written for graduate students, practitioners, and researchers in various fields of science, Theory of Ridge Regression Estimation with Applications is an authoritative guide to the theory and methodology of statistical estimation.

This IBM® Redpaper™ publication introduces and describes the IBM Elastic Storage™ Server as a scalable, high-performance data and file management solution. The solution is built on proven IBM Spectrum™ Scale technology, formerly IBM General Parallel File System (GPFS™). IBM Elastic Storage Servers can be implemented for a range of diverse requirements, providing reliability, performance, and scalability. This publication helps you to understand the solution and its architecture and helps you to plan the installation and integration of the environment. The following combination of physical and logical components are required: Hardware Operating system Storage Network Applications This paper provides guidelines for several usage and integration scenarios. Typical scenarios include Cluster Export Services (CES) integration, disaster recovery, and multicluster integration. This paper addresses the needs of technical professionals (consultants, technical support staff, IT Architects, and IT Specialists) who must deliver cost-effective cloud services and big data solutions.

In this talk Anastasiia and Mariia will show how they use data for their company purposes. Collecting data from everywhere: Office, Task Manager, Google Calendar, Gmail, SalesForce and internal systems.

Dive into the world of scalable data processing with the "Apache Spark Quick Start Guide." This book offers a foundational introduction to Spark, empowering readers to harness its capabilities for big data processing. With clear explanations and hands-on examples, you'll learn to implement Spark applications that handle complex data tasks efficiently. What this Book will help me do Understand and implement Spark's RDDs and DataFrame APIs to process large datasets effectively. Set up a local development environment for Spark-based projects. Develop skills to debug and optimize slow-performing Spark applications. Harness built-in modules of Spark for SQL, streaming, and machine learning applications. Adopt best practices and optimization techniques for high-performance Spark applications. Author(s) Shrey Mehrotra is a seasoned software developer with expertise in big data technologies, particularly Apache Spark. With years of hands-on industry experience, Shrey focuses on making complex technical concepts accessible to all. Through his writing, he aims to share clear, practical guidance for developers of all levels. Who is it for? This guide is perfect for big data enthusiasts and professionals looking to learn Apache Spark's capabilities from scratch. It's aimed at data engineers interested in optimizing application performance and data scientists wanting to integrate machine learning with Spark. A basic familiarity with either Scala, Python, or Java is recommended.