Speeding Up The Time To Insight For Supply Chains And Logistics With The Pathway Database That Thinks
Data Engineering Podcast
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Reliability Engineering Is a Big Data Engineering Challenge
Machine learning is not fully predictable to the human but always works in the same way so if you replicate let's say for data seem in the same order you will get the same machine learning outcomes. Every space has its flavors in terms of unpredictability yeah that's why reliability engineering is becoming its own practice and i'm definitely happy to be in an area where we can explore this and hopefully contribute as well absolutely.
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Summary
Logistics and supply chains are under increased stress and scrutiny in recent years. In order to stay ahead of customer demands, businesses need to be able to react quickly and intelligently to changes, which requires fast and accurate insights into their operations. Pathway is a streaming database engine that embeds artificial intelligence into the storage, with functionality designed to support the spatiotemporal data that is crucial for shipping and logistics. In this episode Adrian Kosowski explains how the Pathway product got started, how its design simplifies the creation of data products that support supply chain operations, and how developers can help to build an ecosystem of applications that allow businesses to accelerate their time to insight.
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- Your host is Tobias Macey and today I’m interviewing Adrian Kosowski about Pathway, an AI powered database and streaming framework. Pathway is used for analyzing and optimizing supply chains and logistics in real-time.
Interview
- Introduction
- How did you get involved in the area of data management?
- Can you describe what Pathway is and the story behind it?
- What are the primary challenges that you are working to solve?
- Who are the target users of the Pathway product and how does it fit into their work?
- Your tagline is that Pathway is "the database that thinks". What are some of the ways that existing database and stream-processing architectures introduce friction on the path to analysis?
- How does Pathway incorporate computational capabilities into its engine to address those challenges?
- What are the types of data that Pathway is designed to work with?
- Can you describe how the Pathway engine is implemented?
- What are some of the ways that the design and goals of the product have shifted since you started working on it?
- What are some of the ways that Pathway can be integrated into an analytical system?
- What is involved in adapting its capabilities to different industries?
- What are the most interesting, innovative, or unexpected ways that you have seen Pathway used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on Pathway?
- When is Pathway the wrong choice?
- What do you have planned for the future of Pathway?
Contact Info
Parting Question
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
Closing Announcements
- Thank you for listening! Don’t forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning.
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Links
- Pathway
- Pathway for developers
- SPOJ.com – competitive programming community
- Spatiotemporal Data
- Pointers in programming
- Clustering
- The Halting Problem
- Pytorch
- Tensorflow
- Markov Chains
- NetworkX
- Finite State Machine
- DTW == Dynamic Time Warping
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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