Building Real-Time Data Platforms For Large Volumes Of Information With Aerospike
Data Engineering Podcast
00:00
Aerow Spike, Lenley Henserling
lenley and sarling talk about building real time data platforms. Henserling is chief strategy officer at aerow spike, while Sarling managed the product management group. Have worked with aero spike founder srene srenewasen for several years. They will be appearing on this week's episode of The Next Big Data.
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Summary
Aerospike is a database engine that is designed to provide millisecond response times for queries across terabytes or petabytes. In this episode Chief Strategy Officer, Lenley Hensarling, explains how the ability to process these large volumes of information in real-time allows businesses to unlock entirely new capabilities. He also discusses the technical implementation that allows for such extreme performance and how the data model contributes to the scalability of the system. If you need to deal with massive data, at high velocities, in milliseconds, then Aerospike is definitely worth learning about.
Announcements
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
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- Your host is Tobias Macey and today I’m interviewing Lenley Hensarling about Aerospike and building real-time data platforms
Interview
- Introduction
- How did you get involved in the area of data management?
- Can you describe what Aerospike is and the story behind it?
- What are the use cases that it is uniquely well suited for?
- What are the use cases that you and the Aerospike team are focusing on and how does that influence your focus on priorities of feature development and user experience?
- What are the driving factors for building a real-time data platform?
- How is Aerospike being incorporated in application and data architectures?
- Can you describe how the Aerospike engine is architected?
- How have the design and architecture changed or evolved since it was first created?
- How have market forces influenced the product priorities and focus?
- What are the challenges that end users face when determining how to model their data given a key/value storage interface?
- What are the abstraction layers that you and/or your users build to manage reliational or hierarchical data architectures?
- What are the operational characteristics of the Aerospike system? (e.g. deployment, scaling, CP vs AP, upgrades, clustering, etc.)
- What are the most interesting, innovative, or unexpected ways that you have seen Aerospike used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on Aerospike?
- When is Aerospike the wrong choice?
- What do you have planned for the future of Aerospike?
Contact Info
Parting Question
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
Links
- Aerospike
- EnterpriseDB
- "Nobody Expects The Spanish Inquisition"
- ARM CPU Architectures
- AWS Graviton Processors
- The Datacenter Is The Computer (Affiliate link)
- Jepsen Tests
- Cloud Native Computing Foundation
- Prometheus
- Grafana
- OpenTelemetry
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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