Companies

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.

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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.

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Operational Analytics At Speed With Minimal Busy Work Using Incorta - Episode 284

A huge amount of effort goes into modeling and shaping data to make it available for analytical purposes. This is often due to the need to simplify the final queries so that they are performant for visualization or limited exploration. In order to cut down the level of effort involved in making data usable, Matthew Halliday and his co-founders created Incorta as an end-to-end, in-memory analytical engine that removes barriers to insights on your data. In this episode he explains how the system works, the use cases that it empowers, and how you can start using it for your own analytics today.

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Gain Visibility Into Your Entire Machine Learning System Using Data Logging With WhyLogs - Episode 283

There are very few tools which are equally useful for data engineers, data scientists, and machine learning engineers. WhyLogs is a powerful library for flexibly instrumenting all of your data systems to understand the entire lifecycle of your data from source to productionized model. In this episode Andy Dang explains why the project was created, how you can apply it to your existing data systems, and how it functions to provide detailed context for being able to gain insight into all of your data processes.

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Connecting To The Next Frontier Of Computing With Quantum Networks - Episode 282

The next paradigm shift in computing is coming in the form of quantum technologies. Quantum procesors have gained significant attention for their speed and computational power. The next frontier is in quantum networking for highly secure communications and the ability to distribute across quantum processing units without costly translation between quantum and classical systems. In this episode Prineha Narang, co-founder and CTO of Aliro, explains how these systems work, the capabilities that they can offer, and how you can start preparing for a post-quantum future for your data systems.

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DataOps As A Service For Your Data Integration Workflows With Rivery - Episode 280

Data engineering is a practice that is multi-faceted and requires integration with a large number of systems. This often means working across multiple tools to get the job done which can introduce significant cost to productivity due to the number of context switches. Rivery is a platform designed to reduce this incidental complexity and provide a single system for working across the different stages of the data lifecycle. In this episode CEO and founder Itamar Ben hemo explains how his experiences in the industry led to his vision for the Rivery platform as a single place to build end-to-end analytical workflows, including how it is architected and how you can start using it today for your own work.

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Synthetic Data As A Service For Simplifying Privacy Engineering With Gretel - Episode 279

Any time that you are storing data about people there are a number of privacy and security considerations that come with it. Privacy engineering is a growing field in data management that focuses on how to protect attributes of personal data so that the containing datasets can be shared safely. In this episode Gretel co-founder and CTO John Myers explains how they are building tools for data engineers and analysts to incorporate privacy engineering techniques into their workflows and validate the safety of their data against re-identification attacks.

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Accelerate Development Of Enterprise Analytics With The Coalesce Visual Workflow Builder - Episode 278

The flexibility of software oriented data workflows is useful for fulfilling complex requirements, but for simple and repetitious use cases it adds significant complexity. Coalesce is a platform designed to reduce repetitive work for common workflows by adopting a visual pipeline builder to support your data warehouse transformations. In this episode Satish Jayanthi explains how he is building a framework to allow enterprises to move quickly while maintaining guardrails for data workflows. This allows everyone in the business to participate in data analysis in a sustainable manner.

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Repeatable Patterns For Designing Data Platforms And When To Customize Them - Episode 277

Building a data platform for your organization is a challenging undertaking. Building multiple data platforms for other organizations as a service without burning out is another thing entirely. In this episode Brandon Beidel from Red Ventures shares his experiences as a data product manager in charge of helping his customers build scalable analytics systems that fit their needs. He explains the common patterns that have been useful across multiple use cases, as well as when and how to build customized solutions.

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Eliminate The Bottlenecks In Your Key/Value Storage With SpeeDB - Episode 276

At the foundational layer many databases and data processing engines rely on key/value storage for managing the layout of information on the disk. RocksDB is one of the most popular choices for this component and has been incorporated into popular systems such as ksqlDB. As these systems are scaled to larger volumes of data and higher throughputs the RocksDB engine can become a bottleneck for performance. In this episode Adi Gelvan shares the work that he and his team at SpeeDB have put into building a drop-in replacement for RocksDB that eliminates that bottleneck. He explains how they redesigned the core algorithms and storage management features to deliver ten times faster throughput, how the lower latencies work to reduce the burden on platform engineers, and how they are working toward an open source offering so that you can try it yourself with no friction.

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