Industrial applications are one of the primary adopters of Internet of Things (IoT) technologies, with business critical operations being informed by data collected across a fleet of sensors. Vopak is a business that manages storage and distribution of a variety of liquids that are critical to the modern world, and they have recently launched a new platform to gain more utility from their industrial sensors. In this episode Mário Pereira shares the system design that he and his team have developed for collecting and managing the collection and analysis of sensor data, and how they have split the data processing and business logic responsibilities between physical terminals and edge locations, and centralized storage and compute.
Designing And Deploying IoT Analytics For Industrial Applications At Vopak - Episode 290May 16, 2022 tmacey Comments Off on Designing And Deploying IoT Analytics For Industrial Applications At Vopak - Episode 290
Insights And Advice On Building A Data Lake Platform From Someone Who Learned The Hard Way - Episode 289May 16, 2022 tmacey Comments Off on Insights And Advice On Building A Data Lake Platform From Someone Who Learned The Hard Way - Episode 289
Exploring The Insights And Impact Of Dan Delorey's Distinguished Career In Data - Episode 288May 9, 2022 tmacey Comments Off on Exploring The Insights And Impact Of Dan Delorey's Distinguished Career In Data - Episode 288
Insights And Advice On Building A Data Lake Platform From Someone Who Learned The Hard Way - Episode 289
Designing a data platform is a complex and iterative undertaking which requires accounting for many conflicting needs. Designing a platform that relies on a data lake as its central architectural tenet adds additional layers of difficulty. Srivatsan Sridharan has had the opportunity to design, build, and run data lake platforms for both Yelp and Robinhood, with many valuable lessons learned from each experience. In this episode he shares his insights and advice on how to approach such an undertaking in your own organization.
Dan Delorey helped to build the core technologies of Google's cloud data services for many years before embarking on his latest adventure as the VP of Data at SoFi. From being an early engineer on the Dremel project, to helping launch and manage BigQuery, on to helping enterprises adopt Google's data products he learned all of the critical details of how to run services used by data platform teams. Now he is the consumer of many of the tools that his work inspired. In this episode he takes a trip down memory lane to weave an interesting and informative narrative about the broader themes throughout his work and their echoes in the modern data ecosystem.
Scaling Analysis of Connected Data And Modeling Complex Relationships With The TigerGraph Graph Database - Episode 287
Many of the events, ideas, and objects that we try to represent through data have a high degree of connectivity in the real world. These connections are best represented and analyzed as graphs to provide efficient and accurate analysis of their relationships. TigerGraph is a leading database that offers a highly scalable and performant native graph engine for powering graph analytics and machine learning. In this episode Jon Herke shares how TigerGraph customers are taking advantage of those capabilities to achieve meaningful discoveries in their fields, the utilities that it provides for modeling and managing your connected data, and some of his own experiences working with the platform before joining the company.
Leading The Charge For The ELT Data Integration Pattern For Cloud Data Warehouses At Matillion - Episode 286
The predominant pattern for data integration in the cloud has become extract, load, and then transform or ELT. Matillion was an early innovator of that approach and in this episode CTO Ed Thompson explains how they have evolved the platform to keep pace with the rapidly changing ecosystem. He describes how the platform is architected, the challenges related to selling cloud technologies into enterprise organizations, and how you can adopt Matillion for your own workflows to reduce the maintenance burden of data integration workflows.