Systems | Development | Analytics | API | Testing



Process, Store and Analyze JSON Data with Ultimate Flexibility

Javascript Object Notation (JSON) is becoming the standard log format, with most modern applications and services taking advantage of its flexibility for their logging needs. However, the great flexibility for developers quickly turns into complexity for the DevOps and Data Engineers responsible for ingesting and processing the logs. That’s why we developed JSON Flex: a scalable analytics solution for complex, nested JSON data.


Road to re:Invent 2022: The 7 Costly and Complex Challenges of Big Data Analytics

re:Invent 2022 is just around the corner and we couldn’t be more excited to share the latest ChaosSearch innovations and capabilities with our current and future customers in the AWS ecosystem. Enterprise DevOps teams, SREs, and data engineers everywhere are struggling to navigate the growing costs and complexity of big data analytics, particularly when it comes to operational data.

Episode 7 | Data Lifecycle | 7 Challenges of Big Data Analytics

What is a data lifecycle? From birth to death, from source to destination, data seems to always be on a journey. If storage and compute were free or there were no laws like the “Right to be Forgotten” within policies such as “General Data Protection Regulation” or GDPR for short, organizations might never delete information. However, at scale data gets extremely expensive and customers do have liberties with regards to governance and sovereignty. Often it is the case that platforms have whole controls and procedures around the lifecycle of data. And in this episode, we will focus on the complexity of scale when it comes to the day in the life of data.

Episode 6 | Data Analytics | 7 Challenges of Big Data Analytics

The first 5 challenges of #bigdataanalytics have been solved, bringing us closer to the end of the #datajourney. And here is where it starts getting real: Data Analytics. Today, there are struggles between operational and business analysis departments. SQL and ML functionality natively without data movement or duplication. How can you access and share the data timely, and efficiently, without data movement or duplication or an insane cost increase? Thomas Hazel shares his insights on how any organization can overcome this challenge, easily.

Episode 5 | Data Platform | Data Journey | 7 Challenges of Big Data Analytics

What are data platforms? A data platform (or more topical, “cloud data platform”) is an integrated set of technologies that collectively meet an organization’s end-to-end data needs. In totality, it enables the storage, delivery, and governance of company data, as well as a security layer for users and applications. The heart of a platform is an actual database where it might be better called a data “analytics” platform or in our case big data analytics platform. Learn more about data platforms and how the ChaosSearch platform solves the challenges faced in big data analytics.

Transforming Kong Logs for Ingestion into Your Observability Stack

As a Solutions Engineer here at Kong, one question that frequently comes across my desk is “how can I transform a Kong logging plugin message into a format that my insert-observability-stack-here understands, i.e. ELK, Loki, Splunk, etc.?” In this blog, I’m going to show you how to easily accomplish converting a Kong logging payload to the Elastic Common Schema. In order to accomplish this task, we’re going to be running Kong Gateway in Kubernetes and using two Kong plugins.