Systems | Development | Analytics | API | Testing

If you are a candidate looking for your next role that involves an in-depth knowledge of Elasticsearch and the wider Elastic Stack then you will want to revise beforehand. In this resource guide on the top ELK interview questions, we've listed all of the leading questions that candidates are commonly asked about Elasticsearch, Logstash & Kibana (and their contemporary tools and plugins) alongside the answers. Want to improve your knowledge further?

In our Data Lake vs Data Warehouse blog, we explored the differences between two of the leading data management solutions for enterprises over the last decade. We highlighted the key capabilities of data lakes and data warehouses with real examples of enterprises using both solutions to support data analytics use cases in their daily operations.

Logging helps developers in reducing errors and cyber-attacks. The application is designed to be dynamic. We can't always predict how an application will react to data changes, errors, or program changes. Logging in allows us to better understand our own programs. For all applications, an absolute logging solution is essential. A good logging system increases the application's stability and makes it easier to maintain on the production server.

We are excited to inform all of our users that we will be bringing OpenSearch and OpenDashboards onto the Logit.io platform in the coming months. You may have already been aware that we’ve previously announced our support for the previous iteration of OpenSearch & OpenDashboards known as Open Distro in our response here. Due to our early public support of these oncoming changes you can see our platform cited on the official AWS OpenSearch website.

In our guide on the best Grafana dashboards examples, we wanted to show you some of the best ways you can use Grafana for a variety of different use cases across your organisation. Whether you are a software architect or a lead DevOps engineer, Grafana is used to make analysis and data visualisation far easier to conduct for busy engineering and technical teams throughout the world.

In the past few years, JavaScript has evolved in several ways and has come a long way. With the evolving technology, machines are becoming more powerful, and browsers are getting more robust and compatible. In addition, Node.js’s recent development for JavaScript’s execution on servers, JavaScript has been getting more and more popular than ever before.

Commonly, your website or app functions perfectly until you release it. During testing, you might seem to have control over everything. But, sooner or later, you will face some challenges. In fact, it is totally normal when something goes wrong. The most important thing is how you settle these problems. In most cases, issues with availability alerts and users’ complaints can be addressed by the means of IIS logs. IIS logging will provide you with the necessary data to deal with a breakdown.

Sometimes, the data you want to analyze lives in AWS S3 buckets by default. If that’s the case for the data you need to work with, good on you: You can easily ingest it into an analytics tool that integrates with S3. But what if you have a data source — such as logs generated by applications running in a Kubernetes cluster — that isn’t stored natively in S3? Can you manage and analyze that data in a cost-efficient, scalable way? The answer is yes, you can.