Data Lake

O’Reilly’s Mac Slocum talks with Edd Dumbbell, VP of strategy at Silicon Valley Data Science, about the data lake, the opportunities the model presents, and the driving forces behind the concept. In the second segment, O’Reilly’s big data guru Ben Lorica chats with Rajiv Maheswaran, CEO of SecondSpectrum, about spatial-temporal pattern recognition — what Maheswaran calls “the science of moving dots.”

The data lake model is a powerhouse for invention

In this episode, Ravi Dharnikota and Enterprise Solution Architect Rich Dill discuss the lifecycle of data including the transition of data to the cloud, implications of distributed data, a "multi-tiered data lifecycle," and the evolution of the data lake. 

SnapTalk - The Lifecycle of Data with Rich Dill

In this episode, Ravi talks with Rakesh Raghavan, Director of Snap Engineering at SnapLogic. Rakesh describes his first-hand experiences developing data lakes for leading online retailers and consumer-facing websites.

They'll discuss the challenges of architecting and implementing a data lake that supports both traditional reporting and ad hoc data exploration plus operational considerations before diving into a data lake project.

SnapTalk: Navigating the Data Lake - Tips From a Practitioner with Rakesh Raghavan

Episode 3 of TeK Talk is the second part of a two-part series on Data Lakes. Harj Dhillon, Akshay Goel, and Sharad Kumar, all senior data and analytics professionals, discuss how to go about creating a data lake, the difference between a Hadoop instance and a true data lake, security concerns, the future of agility in the cloud, and a marketplace user experience.

Kimberly Booth's Album

Episode 3: Data Lakes – Part 2

Building a Data Lake in AWS - Mick Bass and Carla Cole - 47Lining

In this podcast episode, O’Reilly’s Shannon Cutt talks with Damon Feldman, solutions director at MarkLogic, a company that has developed an operational and transactional NoSQL database that integrates data silos to give customers a single view of their data. They discuss data lakes, data hubs, integrating data in a lake format, data governance related to security, and more.

Damon Feldman on Combining a Data Hub and Data Lake

In this episode of the O’Reilly podcast, I speak with Han Yang, senior product manager at Cisco, working on analytics solutions. We discuss the impact of data analytics across industries, building data lifecycles for Internet of Things (IoT) applications, and advice for establishing successful analytics and machine learning projects.

Building Data Lifecycles for IoT Projects With Edge Analytics

In this podcast, Henry Eckerson and Stephen Smith discuss the movement to operationalize data science.

Smith is a well-respected expert in the fields of data science, predictive analytics and their application in the education, pharmaceutical, healthcare, telecom and finance industries. He co-founded and served as CEO of G7 Research LLC and the Optas Corporation which provided the leading CRM / Marketing Automation solution in the pharmaceutical and healthcare industries.

Smith has published journal articles in the fields of data mining, machine learning, parallel supercomputing, text understanding, and simulated evolution. He has published two books through McGraw-Hill on big data and analytics and holds several patents in the fields of educational technology, big data analytics, and machine learning.  He holds a BS in Electrical Engineering from MIT and an MS in Applied Sciences from Harvard University. He is currently the research director of data science at Eckerson Group.

Stephen Smith: Operationalizing Data Science

In this episode, Wayne Eckerson and Jeff Magnusson discuss a self-service model for data science work and the role of a data platform in that environment. Magnusson also talks about Flotilla, a new open source API that makes it easy for data scientists to execute tasks on the data platform. 

Magnusson is the vice president of data platform at Stitch Fix. He leads a team responsible for building the data platform that supports the company's team of 80+ data scientists, as well as other business users. That platform is designed to facilitate self-service among data scientists and promote velocity and innovation that differentiate Stitch Fix in the marketplace. Before Stitch Fix, Magnusson managed the data platform architecture team at Netflix where he helped design and open source many of the components of the Hadoop-based infrastructure and big data platform.

Jeff Magnusson: How To Create A Self-Service Data Platform For Data Scientists

In this podcast, Henry Eckerson interviews Dave Wells on the current health and future of the data warehouse. Wells acknowledges that data warehouses are struggling, but argues they are still necessary and cannot be replaced by data lakes. He then explains what the role of the modern data warehouse should be, practical steps forward for evolving the data warehouse, and much more.

Wells is an advisory consultant, educator, and industry analyst dedicated to building meaningful connections throughout the path from data to business value. He works at the intersection of information management and business management, driving business impact through analytics, business intelligence, and active data management. More than forty years of information systems experience combined with over ten years of business management give him a unique perspective about the connections among business, information, data, and technology. Knowledge sharing and skill building are Dave’s passions, carried out through consulting, speaking, teaching, and writing. 

He is now the practice director of data management at Eckerson Group, cofounder and director of education at eLearningCurve, and a faculty member at The Data Warehousing Institute.

Dave Wells: Data Lakes Are Cool, But You Still Need A Data Warehouse

In this podcast, Wayne Eckerson and James Serra discuss myths of modern data management. Some of the myths discussed include 'all you need is a data lake', 'the data warehouse is dead', 'we don’t need OLAP cubes anymore', 'cloud is too expensive and latency is too slow', 'you should always use a NoSQL product over a RDBMS.'

Serra is big data and data warehousing solutions architect at Microsoft with over thirty years of IT experience. He is a popular blogger and speaker and has presented at dozens of Microsoft PASS and other events. Prior to Microsoft, Serra was an independent data warehousing and business intelligence architect and developer.

James Serra: Myths of Modern Data Management

In this podcast, Wayne Eckerson and Joe Caserta discuss data migration, compare cloud offerings from Amazon, Google, and Microsoft, and define and explain artificial intelligence.

You can contact Caserta by visiting caserta.com or by sending him an email to joe@caserta.com. Follow him on Twitter @joe_caserta.

Caserta is President of a New York City-based consulting firm he founded in 2001 and a longtime data guy. In 2004, Joe teamed up with data warehousing legend, Ralph Kimball to write to write the book The Data Warehouse ETL Toolkit. Today he’s now one of the leading authorities on big data implementations. This makes Joe one of the few individuals with in-the-trenches experience on both sides of the data divide, traditional data warehousing on relational databases and big data implementations on Hadoop and the cloud.

Data Lake

Matheus Ricardo Espanhol