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Articles by Michael Drogalis
What I wish I knew before I quit my job
Before I quit my job at Confluent, I was worried about taking the plunge. Everyone likes to use this metaphor because it focuses on what’s so scary in the beginning: passing through the weird transition between employee and employer. But after the last 16 months of building my company, this metaphor doesn’t sit right with me. It’s not like you take a scary jump once and then you’re good. It’s more like plunging in over and over and over again. Cycles of fear, courage, and calm.
How to use GPT-4 with streaming data for real-time generative AI
2 hours ago ChatGPT's newest feature — a new plugin for writing code — is marking "the strongest case yet for a future where AI is a valuable companion for sophisticated knowledge work," according to a Wharton professor. In a blog published on Friday, Ethan Mollick, an associate professor of management, …
How To Use Event Streaming with GPT-4 for Real-Time Generative AI
By this point, just about everybody has had a go playing with ChatGPT, making it do all sorts of wonderful and strange things. But how do you go beyond just messing around and using it to build a real-world, production application? A big part of that is bringing together the general capabilities of ChatGPT with your unique data and needs. What do I mean by that? Let me give you an example of a scenario every company is thinking about right now.
Is Data Streaming Worth the Learning Curve?
Data has inherent business value because it allows organizations to make more informed -- and ideally more cost-effective -- decisions. When business services and applications can ingest, process, and react to data in real time, teams can accelerate decision-making and make smarter choices, too. To realize this for their own organizations, technical leaders need to remove roadblocks that prevent developers from learning and grasping data streaming skills.
How to Get Started with Data Streaming
Streaming data is everywhere, and today’s developer needs to learn how to build systems and applications that can ingest, process and act on data that’s continuously generated in real time. From millions of connected devices in our homes and businesses to IT infrastructure, developers are tapping into an endless number of data streams to solve operational challenges and build new products, which means the learning opportunities are endless too.
Stream Processing with Your Favorite Language
Read Time: 2 min Stream Processing in Your Native Tongue Mar 23, 2023Read Time: 2 min Editors. Tabs vs. spaces. Programming languages. Some hills are worth dying on. I’ve often wrestled with the last one when it comes to stream processing. The basic situation is that people always want to use their language of choice. But stream processors, like Kafka Streams, Apache Flink®, and Apache Spark™, are largely geared to work best in exactly one programming language.
Historical Data and Streaming: Friends, Not Foes
Real-time event streaming has become one of the most prominent tools for software engineers over the last decade. In , Apache Kafka, the de facto event-streaming platform, is ranked as one of the highest-paying tech skills and most-loved frameworks. While obscure at its outset, there are now countless stories of companies using it at massive scale for use cases like gaming and ride-sharing, where latency must remain incredibly low.
Introducing Stream Processing Use Case Recipes for Every Industry
March 30, 2022 From fraud detection and predictive analytics, to real-time customer experiences and cyber security, stream processing has countless benefits for use cases big and small. By unlocking the power of continuous data streams with real-time processing, we can now leverage data the second it happens. However, it can be tricky to get started, and there are an infinite number of use cases.
Streaming Databases vs Traditional Databases
November 2, 2021 What will the next important category of databases look like? For decades, relational databases were the undisputed home of data. They powered everything: from websites to analytics, from customer data to scheduling. No problem was out of reach because the way that we imagined how data could be used was narrow. But over time, people widened their lens. For today’s companies like Lyft, Netflix, and Airbnb, data isn’t just a tool—it’s the crown jewel.
Guide to ksqlDB Architecture and Advanced Features
August 25, 2021 To effectively use ksqlDB, the streaming database for Apache Kafka®, you should of course be familiar with its features and syntax. However, a deeper understanding of what goes on underneath ksqlDB’s surface can help you make better decisions as you design your streaming architectures. You should ideally know something about ksqlDB’s basic internals, where it fits into the Kafka ecosystem, and the functionality of its advanced features.
Are We There Yet? The Query Your Database Can't Answer
What if I told you there is a query your database can’t answer? That would probably surprise you. With decades of effort behind them, databases are one of the most robust pieces of software in existence. Because they have remained enormously popular in their current form, many engineers assume that their database can answer any query they have if they’re willing to wait long enough. This, however, isn’t a great assumption. Why?
Quickstart to Apache Kafka Stream Processing with ksqlDB
Kafka is the de-facto standard for collecting and storing event data — but are you comfortable using that data in your applications in real-time? It’s trickier than it seems — you need to integrate, secure and scale different systems for event storage, processing and querying. This is why we created ksqlDB, a database purpose-built for stream processing applications. What Is Stream Processing? Kafka is great for working with events through its durable, append-only log.
How Real-Time Stream Processing Safely Scales with ksqlDB
November 19, 2020 Software engineering memes are in vogue, and nothing is more fashionable than joking about how complicated distributed systems can be. Despite the ribbing, many people adopt them. Why? Distributed systems give us two things their single node counterparts cannot: scale and fault tolerance. ksqlDB, the event streaming database, is built with a client/server architecture. You can run it with a single server, or you can cluster many servers together.
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