"Learning Elastic Stack 7.0" introduces you to the tools and techniques of Elastic Stack, covering Elasticsearch, Logstash, Beats, and Kibana. With clear explanations and practical examples, this book helps you grasp the 7.0 version's new features and capabilities, empowering you to build and deploy robust, real-time data processing applications. What this Book will help me do Gain the necessary skills to install and configure Elastic Stack for professional use. Master the data handling capabilities of Elasticsearch for distributed search and analytics. Develop expertise in creating data pipelines with Logstash and other ingestion tools. Learn to utilize Kibana to visualize and interpret complex datasets. Acquire knowledge of deploying Elastic Stack solutions both on-premise and in cloud environments. Author(s) Pranav Shukla and Sharath Kumar M N are experienced software engineers and data professionals with a profound knowledge of databases, distributed systems, and cloud architectures. They specialize in educating developers through structured guidance and proven methodologies related to data handling and visualization. Who is it for? This book is designed for software engineers, data analysts, and technical architects interested in learning the Elastic Stack tools from the ground up. Readers familiar with database concepts but new to Elastic Stack will find this book particularly helpful. Advanced users seeking to understand the updates in Elastic Stack 7.0 are also a complementary audience. If you wish to apply Elastic Stack to real-time data processing and analytics, this book provides a strong foundation.

In this podcast, Dr. Michael Stonebraker discussed his perspective on the growing data ops industry and its future. Dr. Stonebraker has launched several startups that defined data ops. He shares his insights into the data ops market and what to expect in the future of data and operations.

Timeline: 0:30 Mike's take on the "no sequel movement". 6:48 Evolution of database. 13:55 Mobility of data and cloud. 18:41 Tamr's shift from the database to AI. 29:00 Ingredient for a successful start-up. 36:50 Leadership qualities that keep you successful and sane. 41:50 Mike's parting thoughts.

Podcast Link: https://futureofdata.org/dr-mikestonebraker-on-the-future-of-dataops-and-ai/

Dr. Stonebraker's BIO: Dr. Stonebraker has been a pioneer of database research and technology for more than forty years. He was the main architect of the INGRES relational DBMS, and the object-relational DBMS, POSTGRES. These prototypes were developed at the University of California at Berkeley, where Stonebraker was a Professor of Computer Science for twenty-five years. More recently, at M.I.T., he was a co-architect of the Aurora/Borealis stream processing engine, the C-Store column-oriented DBMS, the H-Store transaction processing engine, which became VoltDB, the SciDB array DBMS, and the Data Tamer data curation system. Presently he serves as an advisor to VoltDB and Chief Technology Officer of Paradigm4 and Tamr, Inc.

Professor Stonebraker was awarded the ACM System Software Award in 1992 for his work on INGRES. Additionally, he was awarded the first annual SIGMOD Innovation award in 1994 and was elected to the National Academy of Engineering in 1997. He was awarded the IEEE John Von Neumann award in 2005 and the 2014 Turing Award and is presently an Adjunct Professor of Computer Science at M.I.T, where he is co-director of the Intel Science and Technology Center focused on big data.

About #Podcast:

FutureOfData podcast is a conversation starter to bring leaders, influencers, and lead practitioners to discuss their journey in creating the data-driven future.

Wanna Join? If you or any you know wants to join in, Register your interest by emailing us @ [email protected]

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Summary Some problems in data are well defined and benefit from a ready-made set of tools. For everything else, there’s Pachyderm, the platform for data science that is built to scale. In this episode Joe Doliner, CEO and co-founder, explains how Pachyderm started as an attempt to make data provenance easier to track, how the platform is architected and used today, and examples of how the underlying principles manifest in the workflows of data engineers and data scientists as they collaborate on data projects. In addition to all of that he also shares his thoughts on their recent round of fund-raising and where the future will take them. If you are looking for a set of tools for building your data science workflows then Pachyderm is a solid choice, featuring data versioning, first class tracking of data lineage, and language agnostic data 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. 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 simple recipe for a delicious analytics platform: combine 3 cups of data schema with a pinch of JavaScript in a large pot of cloud storage. Bake in the deployment oven for a couple of months, and savory insights will emerge. Right? Why does this recipe have both 5-star and 1-star ratings?! On this episode, long-standing digital analytics maven June Dershewitz, Director of Analytics at Twitch, drops by the podcast's analytics kitchen to discuss the relative merits of building versus buying an analytics platform. Or, of course, doing something in between!

The episode was originally 3.5 hours long, but we edited out most of Michael's tangents into gaming geekdown, which brought the run-time down to a more normal length.

For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Kyle chats with Rohan Kumar about hyperscale, data at the edge, and a variety of other trends in data engineering in the cloud.

This is the eBook of the printed book and may not include any media, website access codes, or print supplements that may come packaged with the bound book. For introductory-level Python programming and/or data-science courses. A groundbreaking, flexible approach to computer science and data science The Deitels’ Introduction to Python for Computer Science and Data Science: Learning to Program with AI, Big Data and the Cloud offers a unique approach to teaching introductory Python programming, appropriate for both computer-science and data-science audiences. Providing the most current coverage of topics and applications, the book is paired with extensive traditional supplements as well as Jupyter Notebooks supplements. Real-world datasets and artificial-intelligence technologies allow students to work on projects making a difference in business, industry, government and academia. Hundreds of examples, exercises, projects (EEPs), and implementation case studies give students an engaging, challenging and entertaining introduction to Python programming and hands-on data science. Related Content Video: Python Fundamentals Live courses: Python Full Throttle with Paul Deitel: A One-Day, Fast-Paced, Code-Intensive Python Presentation Python® Data Science Full Throttle with Paul Deitel: Introductory Artificial Intelligence (AI), Big Data and Cloud Case Studies The book’s modular architecture enables instructors to conveniently adapt the text to a wide range of computer-science and data-science courses offered to audiences drawn from many majors. Computer-science instructors can integrate as much or as little data-science and artificial-intelligence topics as they’d like, and data-science instructors can integrate as much or as little Python as they’d like. The book aligns with the latest ACM/IEEE CS-and-related computing curriculum initiatives and with the Data Science Undergraduate Curriculum Proposal sponsored by the National Science Foundation.

Summary The database market continues to expand, offering systems that are suited to virtually every use case. But what happens if you need something customized to your application? FoundationDB is a distributed key-value store that provides the primitives that you need to build a custom database platform. In this episode Ryan Worl explains how it is architected, how to use it for your applications, and provides examples of system design patterns that can be built on top of it. If you need a foundation for your distributed systems, then FoundationDB is definitely worth a closer look.

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 Ryan Worl about FoundationDB, a distributed key/value store that gives you t

This second article in a series on modern data architectures. It focuses on what drives customers to want a modern data architecture (i.e., fear and opportunity) in the first place. It then lists ten requirements that customers desire for a modern data architecture, ranging from “cloud-first” and “streaming-first” to “best of breed” and “predictable pricing”.

Originally published at: https://www.eckerson.com/articles/ten-things-companies-want-from-a-modern-data-architecture

Summary Kubernetes is a driving force in the renaissance around deploying and running applications. However, managing the database layer is still a separate concern. The KubeDB project was created as a way of providing a simple mechanism for running your storage system in the same platform as your application. In this episode Tamal Saha explains how the KubeDB project got started, why you might want to run your database with Kubernetes, and how to get started. He also covers some of the challenges of managing stateful services in Kubernetes and how the fast pace of the community has contributed to the evolution of KubeDB. If you are at any stage of a Kubernetes implementation, or just thinking about it, this is definitely worth a listen to get some perspective on how to leverage it for your entire application stack.

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 fri

Every decade since the 1960s, researchers at companies like IBM, Amazon, and many others have introduced major new frameworks and techniques to handle rising data management problems. This concise ebook explains how these new systems helped data science evolve quickly—from hierarchical and relational databases to big data and cloud computing to streaming and graph data. Computer scientist Paco Nathan shows members of your data science team how major companies created each of these data management systems not just to deal with new data types but also to take full advantage of the opportunities the data presented. Their efforts over the years have propelled an entire industry. This report covers the historical progression of data management topics including: Hierarchical databases—1960s mainframe batch systems are still used in finance, healthcare, manufacturing, energy, and other industries. Relational databases—these enabled faster transactions, mathematical optimization, and budgeting guarantees for many businesses. Big data—this includes relatively cheap horizontal scale-out systems for collecting huge amounts of customer data. Cloud computing—large companies began managing reliable, scalable, cost-effective data centers; Amazon turned the concept into a business. Cluster schedulers—managing horizontal clusters was difficult before schedulers such as Apache Mesos appeared. Streaming data—data continuously generated by different sources requires responses in "real time"—generally milliseconds.

Summary One of the biggest challenges for any business trying to grow and reach customers globally is how to scale their data storage. FaunaDB is a cloud native database built by the engineers behind Twitter’s infrastructure and designed to serve the needs of modern systems. Evan Weaver is the co-founder and CEO of Fauna and in this episode he explains the unique capabilities of Fauna, compares the consensus and transaction algorithm to that used in other NewSQL systems, and describes the ways that it allows for new application design patterns. One of the unique aspects of Fauna that is worth drawing attention to is the first class support for temporality that simplifies querying of historical states of the data. It is definitely worth a good look for anyone building a platform that needs a simple to manage data layer that will scale 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! 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 Evan Weaver about FaunaDB, a modern operational data platform built for your cloud

Interview

Introduction How did you get involved in the area of data management? Can you start by explaining what FaunaDB is and how it got started? What are some of the main use cases that FaunaDB is targeting?

How does it compare to some of the other global scale databases that have been built in recent years such as CockroachDB?

Can you describe the architecture of FaunaDB and how it has evolved? The consensus and replication protocol in Fauna is intriguing. Can you talk through how it works?

What are some of the edge cases that users should be aware of? How are conflicts managed in Fauna?

What is the underlying storage layer?

How is the query layer designed to allow for different query patterns and model representations?

How does data modeling in Fauna compare to that of relational or document databases?

Can you describe the query format? What are some of the common difficulties or points of confusion around interacting with data in Fauna?

What are some application design patterns that are enabled by using Fauna as the storage layer? Given the ability to replicate globally, how do you mitigate latency when interacting with the database? What are some of the most interesting or unexpected ways that you have seen Fauna used? When is it the wrong choice? What have been some of the most interesting/unexpected/challenging aspects of building the Fauna database and company? What do you have in store for the future of Fauna?

Contact Info

@evan on Twitter LinkedIn

Parting Question

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

Links

Fauna Ruby on Rails CNET GitHub Twitter NoSQL Cassandra InnoDB Redis Memcached Timeseries Spanner Paper DynamoDB Paper Percolator ACID Calvin Protocol Daniel Abadi LINQ LSM Tree (Log-structured Merge-tree) Scala Change Data Capture GraphQL

Podcast.init Interview About Graphene

Fauna Query Language (FQL) CQL == Cassandra Query Language Object-Relational Databases LDAP == Lightweight Directory Access Protocol Auth0 OLAP == Online Analytical Processing Jepsen distributed systems safety research

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

Support Data Engineering Podcast

This IBM® Redbooks® publication covers IBM TS7700 R4.2. The IBM TS7700 is part of a family of IBM Enterprise tape products. This book is intended for system architects and storage administrators who want to integrate their storage systems for optimal operation. Building on over 20 years of virtual tape experience, the TS7760 now supports the ability to store virtual tape volumes in an object store. The TS7700 has supported off loading to physical tape for over two decades. Off loading to physical tape behind a TS7700 is utilized by hundreds of organizations around the world. Using the same hierarchical storage techniques, the TS7700 can also off load to object storage. Given object storage is cloud based and accessible from different regions, the TS7760 Cloud Storage Tier support essentially allows the cloud to be an extension of the grid. As of the release of this document, the TS7760C supports the ability to off load to IBM Cloud Object Storage as well as Amazon S3. To learn about the TS7760 cloud storage tier function, planning, implementation, best practices, and support see IBM Redpaper IBM TS7760 R4.2 Cloud Storage Tier Guide, redp-5514 at: http://www.redbooks.ibm.com/abstracts/redp5514.html The IBM TS7700 offers a modular, scalable, and high-performance architecture for mainframe tape virtualization for the IBM Z® environment. It is a fully integrated, tiered storage hierarchy of disk and tape. This storage hierarchy is managed by robust storage management microcode with extensive self-management capability. It includes the following advanced functions: Improved reliability and resiliency Reduction in the time that is needed for the backup and restore process Reduction of services downtime that is caused by physical tape drive and library outages Reduction in cost, time, and complexity by moving primary workloads to virtual tape More efficient procedures for managing daily backup and restore processing Infrastructure simplification through reduction of the number of physical tape libraries, drives, and media TS7700 delivers the following new capabilities: TS7760C supports the ability to off load to IBM Cloud Object Storage as well as Amazon S3 8-way Grid Cloud consisting of any generation of TS7700 Synchronous and asynchronous replication Tight integration with IBM Z and DFSMS policy management Optional Transparent Cloud Tiering Optional integration with physical tape Cumulative 16Gb FICON throughput up to 4.8GB/s 8 IBM Z hosts view up to 496 8 equivalent devices Grid access to all data independent of where it exists The TS7760T writes data by policy to physical tape through attachment to high-capacity, high-performance IBM TS1150 and IBM TS1140 tape drives installed in an IBM TS4500 or TS3500 tape library. The TS7760 models are based on high-performance and redundant IBM POWER8® technology. They provide improved performance for most IBM Z tape workloads when compared to the previous generations of IBM TS7700.

-update for Release 8.5 - IBM experts recognize the need for data protection, both from hardware or software failures, and also from physical relocation of hardware, theft, and retasking of existing hardware. The IBM DS8880 supports encryption-capable hard disk drives (HDDs) and flash drives. These Full Disk Encryption (FDE) drive sets are used with key management services that are provided by IBM Security Key Lifecycle Manager software or Gemalto SafeNet KeySecure to allow encryption for data at rest on a DS8880. Use of encryption technology involves several considerations that are critical for you to understand to maintain the security and accessibility of encrypted data. The IBM Security Key Lifecycle Manager software also supports Transparent Cloud Tiering (TCT) data object encryption, which is part of this publication. With TCT encryption, data is encrypted before it is transmitted to the Cloud. The data remains encrypted in cloud storage and is decrypted after it is transmitted back to the DS8000®. This IBM Redpaper™ publication contains information that can help storage administrators plan for disk and TCT data object encryption. It also explains how to install and manage the encrypted storage and how to comply with IBM requirements for using the IBM DS8000 encrypted disk storage system. This edition focuses on IBM Security Key Lifecycle Manager Version 3.0 which enables support Key Management Interoperability Protocol (KMIP) with the DS8000 Release 8.5 code or later and updated GUI for encryption functions. The publication also discusses support for data at rest encryption with Gemalto SafeNet KeySecure Version 8.3.2.

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

IBM Storage Solutions for IBM Cloud™ Private delivers a blueprint for multicloud architecture. IBM, delivering solutions to help you win. In this blueprint, learn how to: Combine the benefits of IBM Systems with the performance of IBM Storage solutions so that you can deliver the right services to your clients today. Deliver optimized private cloud services ahead of schedule and under budget with a complete IBM Cloud Private stack. Containerize applications and deliver the SLAs that your team needs to thrive and win. Implement IBM Cloud Private to deploy modern applications like blockchain and AI or modernize what you already have. You now have the capabilities. This edition applies to IBM Storage Solutions for IBM Cloud Private Version 1 Release 5.0.

Today's global organizations depend on being able to unlock business insights from massive volumes of data. Now, with IBM® FlashSystem 900 Model AE3 that is powered by IBM FlashCore® technology, they can make faster decisions that are based on real-time insights. They also can unleash the power of the most demanding applications, including online transaction processing (OLTP) and analytics databases, virtual desktop infrastructures (VDIs), technical computing applications, and cloud environments. This IBM Redbooks® publication introduces clients to the IBM FlashSystem® 900 Model AE3. It provides in-depth knowledge of the product architecture, software and hardware, implementation, and hints and tips. Also presented are use cases that show real-world solutions for tiering, flash-only, and preferred-read. Examples of the benefits that are gained by integrating the FlashSystem storage into business environments also are described. This book is intended for pre-sales and post-sales technical support professionals and storage administrators, and anyone who wants to understand how to implement this new and exciting technology.

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

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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

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