As an Elasticsearch administrator, you will inevitably have to delete an index at some point. There could be several reasons why this might be necessary: This article provides an overview of available methods to back up and restore an Elasticsearch index in the event of deletion.

When a data index is created in Elasticsearch, the data is divided into shards for horizontal scaling across multiple nodes. These shards are small pieces of data that make up the index and play a significant role in the performance and stability of Elasticsearch deployments. A shard can be classified as either a primary shard or a replica shard. A replica is a copy of the primary shard, and whenever Elasticsearch indexes data, it is first indexed to one of the primary shards.

Customers now have access to the complete Aiven portfolio via self-service from AWS Marketplace.

Data analysis is a crucial aspect of any business or organization because it helps with making informed decisions and improving overall performance. However, with the vast amounts of data generated every day, it can be overwhelming to manually analyze and derive insights from it.

Aiven Terraform Provider 4.x is an important step on our move to the new Terraform Plugin Framework, providing better type checking. Read on for details.

This article was originally published on Medium.com and is reposted here with permission from the author. Trying to connect Grafana & InfluxDB IOx? Follow this tutorial to learn how.

ChaosSearch was built for live analytics at scale on cloud storage. Our architecture was designed for high volume ingestion of streams & analytics at scale via ElasticSearch & Trino API via a stateless fabric that can scale to meet the customers’ scale & latency requirements. Because we don’t store any data, under the hood, ChaosSearch is basically a set of containers that are deployed in cloud compute instances in a dedicated VPC to each customer managed by ChaosSearch.

Databases are often the biggest performance bottleneck in an application. They are also hard to migrate from once being used in production, so making the right choice for your application’s database is crucial. A big part of making the right decision is knowing what your options are. The database landscape has been changing rapidly in the past few years, so this article will try to simplify things for you by going over the following topics.

How machine learning in AppDynamics Cloud accelerates log analysis and reduces mean time to detect. Site recovery engineers (SREs) need to investigate unknown problems reported in production. The common approach is to search and filter log files to find the root cause, and we all know how painful it is to sift through log contents. It’s like finding a needle in a haystack. A machine learning approach is essential to assist SREs to quickly identify the root cause.