Should data scientists know Java? Java and Scala underpin many real-time, ML-based applications—yet data scientists usually work in Python. Someone has to port the Python into Java or adapt it to use a Python wrapper. Neither of these options is ideal, so what are some better solutions?
Explore the fundamentals of time series analysis in this comprehensive article. Learn about key concepts, use cases, and types of time series analysis, and discover models, techniques, and methods to analyze time series data.
Gain a thorough understanding of telemetry data and how it works, learn about its benefits, challenges, and applications across different industries, and discover technologies you can use to operationalize telemetry.
Moving code from prototype to production can be tricky—especially for data scientists. There are many challenges in deploying code that needs to calculate features for ML models in real-time. I look at potential solutions to ease the friction.
Learn how to reprocess a stream of data with the Quix Streams Python library and Apache Kafka. You'll ingest some GPS telemetry data into a topic and replay the stream to try out different distance calculation methods.
Explore the differences between Quix and Apache Flink and find out when it's better to use Quix as a Flink alternative. If you’re searching for Apache Flink alternatives, this guide offers a detailed, fair comparison to help you make an informed decision.
A detailed comparison of Apache Kafka and Amazon Kinesis that covers categories such as operational attributes, pricing model, and time to production while highlighting their key differences and use cases that they typically address.
Lightweight, powerful, no JVM and no need for separate clusters of orchestrators. Here’s a look at our next-gen streaming library for C# and Python developers including feature summaries, code samples, and a sneak peek into our roadmap.