Yeah, yeah, I mean, okay, first of all, like, what you have in your mind? I think it’s a great question. As far as use cases, analytics is obvious, but what else can you do when you go from batch to real time in the context of a database? Kostas, you love Materialize and I cannot wait to hear what your burning questions are. But I’m sure that people are doing all sorts of other interesting things. As I think about our audience Kostas, the biggest question that comes to mind is, what are the immediate use cases from a tool like Materialize that, at a foundational level, can take jobs with data that are generally considered batch and happen over a long period of time with a lot of latency? And essentially turn them into real-time jobs? Analytics is absolutely a use case that I think makes a ton of sense. Today, we get to talk with the founder of a company building a database product, the company is called Materialize, and Arjun is the founder of the company. The Data Stack Show is brought to you by RudderStack, the CDP for developers. You’ll learn about new data technology and trends and how data teams and processes are run at top companies. Each week we explore the world of data by talking to the people shaping its future. RudderStack helps businesses make the most out of their customer data while ensuring data privacy and security. Each week we’ll talk to data engineers, analysts, and data scientists about their experience around building and maintaining data infrastructure, delivering data and data products, and driving better outcomes across their businesses with data. The Data Stack Show is a weekly podcast powered by RudderStack. Keeping databases to the cloud for low latency (48:31).Lessons learned from making Materialize a cloud offering (44:22).The best use case to start with (42:03).Why Materialize doubled down on writing 100% in Rust (35:43).Making Materialize simple to use (32:33).Comparing Postgres and Materialize and looking at what’s under the hood of Materialize (23:16).Using Materialize instead of the materialized view from another tool (18:44).Analytics starts with a human in the loop and comes into its own when analysts get themselves out and automate it (15:46).Looking at what Materialize brings to the stack (5:28).Introducing Arjun and how he fell in love with databases (2:51).
Highlights from this week’s episode include:
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