ChaosSearch

Boston, MA, USA
2017
  |  By David Bunting
Streaming analytics is an invaluable capability for organizations seeking to extract real-time insights from the log data they continuously generate through applications and cloud services. To help our community get started with streaming analytics on AWS, we published a piece last year called An Overview of Streaming Analytics in AWS for Logging Applications, where we covered all the basics.
  |  By David Bunting
In the past, querying a database required Structured Query Language (SQL) skills, or knowledge of other database query languages, such as Kibana Query Language (KQL). Today, with the emergence of generative AI (GenAI), teams can query their analytic database using natural language — and get plain English results in return. Or, if you prefer to still use SQL, many teams use GenAI for database query optimization, making queries faster and more efficient.
  |  By David Bunting
Mobile and video games generate huge amounts of data that can be captured and analyzed by game developers to power gaming analytics initiatives and improve games.
  |  By David Bunting
Recently at re:Invent, Amazon unveiled S3 Express One Zone for AWS. Express Zone for S3 responds to the demand for faster analytical query speeds, with the convenience of centrally storing all of your application telemetry data in cloud object storage. In the past, for data-intensive applications, data access speeds were slower than desired.
  |  By Thomas Hazel
In 2009, as the world became increasingly data-driven, organizations began to accumulate vast amounts of data — a period that would later be characterized as the Big Data revolution. While most organizations were used to handling well-structured data in relational databases, this new data was appearing more and more frequently in semi-structured and unstructured data formats.
  |  By Thomas Hazel
With its simplicity, flexibility, and cost-efficient characteristics, Amazon Simple Storage Service (Amazon S3) cloud object storage has become the preferred platform for collecting, analyzing, and retaining today’s growing mountain of diverse and disjointed enterprise data. And as Amazon Web Services (AWS) continues to grab market share in the hyperscale IaaS/PaaS/SaaS marketplace, organizations of every size are leveraging Amazon S3 to underpin a variety of use cases, such as.
  |  By Thomas Hazel
As we venture deeper into the data-driven era, the traditional systems we have employed to store, search, and analyze data are being challenged by revolutionary advancements in Artificial Intelligence. One such groundbreaking development is the notable advent of Large Language Models (LLMs), specifically those with Multi-Mod[a]l abilities (e.g., Image & Audio).
  |  By George Hamilton
JavaScript Object Notation (JSON) has become the de facto standard format for writing cloud infrastructure and web application log data - and for good reason. Structured logging with JSON reduces the cost and complexity of extracting valuable insights from log data.
  |  By George Hamilton
ELK has become one of the most popular log analytics solutions for software-driven businesses, with thousands of organizations relying on ELK for log analysis and management in 2021. In this ultimate guide to using ELK for log management and analytics, we’re providing insights and information that will help you know what to expect when deploying, configuring, and operating an ELK stack for your organization. Keep reading to discover answers to the following.
  |  By Thomas Hazel
Data warehouses and data lakes represent two of the leading solutions for enterprise data management in 2023. While data warehouses and data lakes may share some overlapping features and use cases, there are fundamental differences in the data management philosophies, design characteristics, and ideal use conditions for each of these technologies.
  |  By ChaosSearch
What are the 7 challenges of data analytics? This preview highlights the series that Thomas Hazel, CTO & Founder of ChaosSearch will cover.
  |  By ChaosSearch
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.
  |  By ChaosSearch
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.
  |  By ChaosSearch
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.
  |  By ChaosSearch
Hear about David Noblet's work as Co-Founder + Chief Architect at ChaosSearch.
  |  By ChaosSearch
To coincide with our overview, watch this brief demo on how ChaosSearch can help with your big data analytics challenge of data preparation & governance.
  |  By ChaosSearch
ChaosSearch complements monitoring-focused observability solutions to meet the scale, cost and complexity challenges of operational log analytics.ChaosSearch transforms your cloud object storage into a hot data analytical datalake. Here is how ChaosSearch greatly simplifies the data pipeline.
  |  By ChaosSearch
Data preparation issues, particularly at scale, can be solved. How? This demo walks you through how ChaosSearch helps.
  |  By ChaosSearch
What are data destinations? In a very abstract sense, data destination is another input along the series of process elements in a data pipeline. However, when calling out an element as the destination, it is really seen as the final destination such as a database, data lake or data warehouse. And yet, any element within the data pipeline has aspects of a final destination (and scaling challenges).
  |  By ChaosSearch
Data preparation issues, particularly at scale, can be solved. How? Thomas Hazel explains in episode 2 of our binge-worthy #datajourney series.
  |  By ChaosSearch
CHAOSSEARCH is a fully managed Log Analysis SaaS solution built on our innovative architecture and revolutionary, patent-pending index technology. Our solution delivers log analysis at cloud-scale and eliminates data movement - the first SaaS solution to provide infinite data storage by accessing your data in your Amazon S3.
  |  By ChaosSearch
How to make refining data as affordable as generating it.

ChaosSearch makes it simple for organizations to run cloud-scale log analytics in their own Amazon S3 cloud storage. It uniquely transforms your cheap, secure, and durable cloud object storage into a distributed analytic data lake where scale is infinite, cost is disruptive, and access is universal.

Traditional log analytics weren’t designed for today’s tsunamis of log data. They require brute force (adding more and more compute) to search and analyze huge stores of logs. This means businesses must continually choose between spending more money or reducing data retention.

ChaosSearch’s SaaS data platform was built for a data-entrenched world. It’s based on the company’s patent-pending index technology and architecture that remove the limits, cost, and complexity inherent in conventional solutions.

  • Performance at Scale: Easily scales to petabytes and beyond so you can analyze what you need, whenever you need.
  • Fully Managed Service: There’s no software or hardware for you to deploy, configure or maintain.
  • All on Your Amazon S3: ChaosSearch stores and analyzes data directly in your own Amazon S3 cloud object storage. It does not hold or store any data.
  • Disruptive Pricing: Costs up to 80% less than other solutions, thanks to Chaos Index’s unique properties that eliminate the need to manually shard data and enable unparalleled compression ratios.