Operations | Monitoring | ITSM | DevOps | Cloud

Best Practices for Managing and Scaling Digital Advertising Campaigns

Digital advertising has become increasingly complex as platforms evolve and competition intensifies. Algorithms change frequently, audience behavior shifts quickly, and costs can rise without warning. In this environment, even well-funded campaigns can underperform if they are not managed with precision. Businesses often underestimate how quickly inefficiencies can accumulate, leading to wasted spend and inconsistent results. Effective campaign management is no longer optional; it is a critical function that directly impacts revenue and operational stability.

How to Use Time Series Autoregression (With Examples)

Time series autoregression is a powerful statistical technique that uses past values of a variable to predict its future values. This approach is particularly valuable for forecasting applications where historical patterns can inform future trends. In this hands-on tutorial, you’ll learn how to implement autoregressive (AR) models using Python and see how InfluxDB can enhance your time series analysis workflow.

Power BI Dashboard Best Practices for Data Engineers and BI Developers

A strong Power BI dashboard is not built solely on visuals. For data engineers and BI developers, the dashboard is the final expression of a much larger analytics system. Its quality depends on the data model's structure, the discipline of the transformation layer, the clarity of the DAX logic, the dataset's performance, and the security model governing access.

From Edge to Enterprise: How Litmus and InfluxDB Are Modernizing the Industrial Data Stack

Today at Hannover Messe, InfluxData is announcing a strategic partnership with Litmus to address one of the most persistent challenges in industrial data: getting reliable, contextualized telemetry from the shop floor into production systems. Litmus bridges the gap between OT systems and modern IT infrastructure, while InfluxDB serves as the industrial data hub, giving organizations both real-time operational visibility and enterprise-scale historical analysis in a unified architecture.

The Strategic Advantage of App Intelligence: How Data-Driven Insights Fuel Mobile Growth

In today's hyper-competitive mobile ecosystem, launching an app is no longer the hardest part-scaling it is. With millions of apps competing for attention across major app stores, success depends on more than just a great idea or clean design. Developers, marketers, and analysts must rely on data to understand user behavior, monitor trends, and outmaneuver competitors. This is where mobile app intelligence platforms have become essential.

Setting Up an MQTT Data Pipeline with InfluxDB

In this blog, we’re going to take a look at how you can set up a fully-functioning, robust data pipeline to centralize your data into an InfluxDB instance by collecting and sending messages with the MQTT protocol. We’ll start with a brief overview of the technologies and protocols used in the pipeline, then dive into how you can connect, configure, and test them to ensure your data pipeline is fully functional. It’s going to be a long post, so let’s jump right in.

From Edge to Cloud: How Litmus Edge and InfluxDB Unlock Industrial Intelligence at Hannover Messe

If you’ve spent time in industrial environments, you know the problem isn’t a lack of data. It’s collecting it reliably, contextualizing it, and storing it at scale. Most stacks weren’t built to fight all three battles.

What's New in InfluxDB 3 Explorer 1.7: Table Management, Data Import, Transforms, and More

InfluxDB 3 Explorer 1.7 is a step forward for anyone who wants to manage their time series data without constantly switching between the UI and a terminal. This release adds table-level schema management, the ability to import data from other InfluxDB instances, and a new Transform Data section to reshape your data, all within the Explorer UI.

Scaling Technical Research: Integrating Proxies into Your Data Operations (DataOps) Pipeline

In the world of Big Data, success depends on more than just algorithms. The quality of the incoming data stream is crucial. When a company scales its technical research, it inevitably encounters barriers such as CAPTCHAs, geoblocks, and anti-fraud systems.

Introducing Aiven for DataHub: Managed context for humans and AI

Discover Aiven for DataHub: a fully managed, open-source data catalog that gives your teams and AI agents the context they need to find and understand data. According to an MIT study, 95% of AI projects fail to deliver value. I've been thinking about why that number is so stubbornly high, and I've come to believe the answer isn't about models,compute or even data quality in the traditional sense -It's about context.