Operations | Monitoring | ITSM | DevOps | Cloud

Sometimes Query Optimizers are seen as a sort of black magic, “the most challenging problem in computer science,” according to Father Pavlo, or some behind-the-scenes player. We believe this perception is because: However, Query Optimizers are no more complicated in theory or practice than other parts of a database system, as we will argue in a series of posts: Part 1: Part 2: After reading these blogs, we hope people will use DataFusion to.

You start a query, grab a coffee, and come back to… a report that’s still loading. We’ve all been there. When your database wasn’t designed for analytics, even basic reporting can feel painfully slow. Databases aren’t one-size-fits-all, especially when it comes to analytics. The system that works fine for storing and retrieving customer transactions or app data isn’t necessarily built to process complex queries on massive datasets.

A common problem in monitoring and alerting systems is not just alerting on what you’re seeing but preventing alert storms from overwhelming operators. When a system generates multiple notifications for the same incident, it leads to alert fatigue and can mask other important issues. For time series data, alert fatigue can result in missed anomalies, delayed responses to critical trends, and difficulty distinguishing real performance degradations from noise.

The Amazon Redshift data cloud provides a fast, secure, and widely accessible data warehouse solution. It is an ideal platform for performing complex analytics and processing large data sets. In addition to supporting multi-parallel processing (MPP), Redshift is also a type of Online Analytical Processing (OLAP) database. Yet, one of Redshift’s main selling points is its cost-effectiveness over alternatives like Snowflake and BigQuery. Is this the case? What is the actual cost of Amazon Redshift?

In Industrial IoT for real-time monitoring, timely alerts are crucial. While Slack and email notifications are common, they can be easily missed or buried in a flood of other notifications. SMS and WhatsApp on the other hand, offer a level of immediacy and directness that’s hard to ignore.

InfluxDB 3 Core and Enterprise introduce a powerful new diskless architecture that lets you store your time series data in cloud object storage while running the database engine locally. This approach offers significant advantages: you get the performance of a local database combined with the durability, scalability, and cost-effectiveness of cloud storage. In this tutorial, I’ll show you how to set up InfluxDB 3 Core or Enterprise with Azure Blob Storage as your object store.

The idea behind KIP-405 is to simply store most of the cluster’s data in another service. As we covered in detail in the last article - it’s a simple-sounding idea that goes a very long way. This other server where the data gets stored is pluggable. KIP-405 was designed in such a way to make Kafka seamlessly extensible to store its data in any kind of external store through a solid interface.