Operations | Monitoring | ITSM | DevOps | Cloud

By 2025, more than 180 zettabytes of data will have been created and captured around the globe. With the influx of data in this digitized world, the process of data aggregation has become an essential tool for businesses. It’s a way to take large amounts of separate data from different sources and combine them into one collective body.

This post was written by Mercy Kibet, a full-stack developer with a knack for learning and writing about new and intriguing tech stacks. In today’s digital world, software applications are becoming increasingly complex and distributed, making it more challenging than ever to diagnose and troubleshoot issues when they arise.

Remember the first time you were at a wedding, or a party and you learned about dances like The Electric Slide? You know, those dances with a clear structure and steps to follow, which were a huge help to someone who was slightly challenged on the dance floor, like me? All you had to do was learn a few simple steps, and you could hang with even the best dancers.

No matter how complicated your monthly cloud bill, all spend in the cloud is premised on a very simple formula.

In this article, we will be exploring C# date classes and how to leverage them to handle and manipulate date data in our applications. We will see the different types of date objects that C# handles and the formats that can be represented, and we will learn how to cleanly process date information from users. Let’s jump right in.

The Elasticsearch reindex API copies data from one index to another. You can use reindex to change the index mapping, copy data to another cluster, or copy only a subset of data to another index. For example, suppose you want to reindex all the data in index1 into index2. In that case, you run the following example in Kibana dev tools: In this article, we dive into some common issues solved by reindexing as well as troubleshooting issues with reindexing itself.

Elasticsearch is a search and analytics engine that allows for complex searches on large datasets of different types and formats. Elasticsearch mappings are the blueprints that define how data is indexed and searched to support these data-related features. Understanding how Elasticsearch mappings work is essential to an effective Elasticsearch deployment. In this article, we’ll explore the key concepts of Elasticsearch mappings and common Elasticsearch mapping pitfalls to avoid.

Announcing Aiven’s partnership with GitHub's secret scanning program to protect your cloud data infrastructure.