Operations | Monitoring | ITSM | DevOps | Cloud

Why You Need a Centralized Approach to Monitoring

With a standard model for monitoring data across the organization, different teams can use a common infrastructure and extract maximum value from it. Monitoring (also sometimes referred to as observability) involves collecting and analyzing data from a source over time to track its health and/or performance. Because change occurs over time, virtually all monitoring data is time series data, meaning it has a timestamp.

Innovating together | Aiven + Supermetrics

In just 5 years, Supermetrics has grown almost beyond recognition. Their hugely-popular product helps over 17,000 customers move data from 100+ marketing platforms (Facebook Ads, Google Analytics, TikTok, HubSpot, and more) into their favorite analytics platforms – whether it’s a spreadsheet, a data warehouse, or a BI tool. In this case study, Supermetrics' CTO Duleepa "Dups" Wijayawardhana, explains how Aiven has helped them channel their data across the clouds.

TL;DR InfluxDB Tech Tips: Downsampling with Flight SQL and AWS Lambda

This tutorial covers how to perform downsampling with the new InfluxDB storage engine, InfluxDB IOx, in InfluxDB Cloud (available on AWS us-east-1 and AWS eu-central-1 starting January 31st) using AWS Lambda. This tutorial describes how to: InfluxDB IOx addresses key user needs including (but not limited to): We achieved these goals by building InfluxDB IOx on the Apache ecosystem (Apache Parquet, Apache DataFusion, Apache Arrow, and Apache Flight SQL).

Revolutionize Your Data Analysis with Matching Solutions

Ever found yourself frustrated with matching data using Excel? Or worse, having to redo the matching of two data sets because of too many false negatives? You're not alone. Data analysis is tough, made tougher with ancient practices that can no longer keep up with the complex nature of data today. Yet, most data analysts still spend their days manually coding scripts for data matching. Studies have shown that about 40-50% of data analysts are spending a significant amount of their time manually coding matching scripts to match data.