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Get Kafka-Nated S2E1: Giannis Polyzos on Fluss, Lakehouse, and the Future of Streaming
Season 2 of Get Kafka-Nated kicks off with Giannis Polyzos for a deep dive into Fluss and one of the most enduring questions in data infrastructure: streaming vs batch, or both? Drawing on his experience as a member of the Fluss PMC, Giannis breaks down what Fluss is, how it fits into modern lakehouse architectures, and where the lakehouse model is heading as we look toward 2026.
Time Series Meets Graph: Understanding Relationships in Streaming Data
Data systems rarely operate as isolated components. Machines depend on sensors, services rely on other services, and devices exchange data through shared gateways. When something changes, the impact often spreads beyond a single metric. To trace how changes move through complex systems, many teams turn to graph-style analysis to map dependencies and follow cause and effect.
Optimizing BESS Operations: Real-Time Monitoring & Predictive Maintenance with InfluxDB 3
For IT and OT engineers managing Battery Energy Storage Systems (BESS) and other distributed energy resources (DER), the challenge isn’t just dealing with energy. It’s a data problem, or managing the massive stream of real-time telemetry these systems generate. For example, a BESS site produces a constant stream of time-series data from BMS, PCS, SCADA, EMS, and more, and operating it means ingesting, correlating, and acting on that data in real time. And this challenge changes with scope.
Technology forecasting should have better tools
Technology moves in waves: breakthroughs, hype, adoption, disappointment, then quiet infrastructure building. The challenge is that traditional forecasting often lags behind reality. Reports are published after the market has moved, expert opinions can conflict, and social media trends can distort what feels important. This is why the idea of a technologies prediction market is compelling. It offers a mechanism for turning diverse beliefs into a live probability signal that updates as new information appears.
InfluxDB For Beginners
In this video, we go over getting started with InfluxDB, including downloading, installing, writing, and reading data with a couple different clients.
How to Integrate Grafana with Home Assistant
This post covers how to get started with Home Assistant and Grafana, including setting up InfluxDB and Grafana with Docker, configuring InfluxDB to receive data from Home Assistant, and creating a Grafana dashboard to visualize your data. It provides a comprehensive guide for real-time monitoring and analysis of Home Assistant data. In this tutorial, you’ll learn how to integrate Grafana with Home Assistant using InfluxDB.
Implement dbt data quality checks with dbt-expectations
dbt is one of the most popular solutions for data transformations and modeling. Many commercial data pipelines rely on dozens, or even hundreds, of individual dbt jobs. Data engineers, data platform engineers, and analytics engineers who own these pipelines need to maintain a testing framework to prevent mistakes in data processing that can compromise analysis.
A Guide to Regression Analysis with Time Series Data
Regression analysis with time series data in Python provides a basis for understanding how values change over time. By following this guide, you’ll understand regression as applied to time series data, how to prepare it in Python, and how to create regression models that’ll help discover trends and influence decisions. With the vast amount of time series data generated, captured, and consumed daily, how can you make sense of it?
From Market Noise to Clear Strategy: How AI Is Changing Business Intelligence
Modern businesses are drowning in data. Every click, transaction, customer interaction, and campaign generates information. Yet having more data does not automatically lead to better decisions. In fact, many organizations struggle because they are surrounded by insights but lack clarity. Reports contradict each other, dashboards multiply, and teams spend more time interpreting data than acting on it. This gap between data and direction is where artificial intelligence is reshaping business intelligence.