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By David Bunting
Datadog is a Software-as-a-Service (SaaS) cloud monitoring solution that enables multiple observability use cases by making it easy for customers to collect, monitor, and analyze telemetry data (logs, metrics and traces), user behavior data, and metadata from hundreds of sources in a single unified platform.
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By George Hamilton
Calculating log analytics ROI is often complicated. For many teams, this technology can be a cost center. Depending on your platform, the cost of a log management solution can quickly add up. For example, many organizations use solutions like the ELK stack because the initial startup costs are low. Yet, over time, costs can creep up for many reasons, including the volume of data collected and ingested per day, required retention periods, and the associated personnel needed to manage the deployment.
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By David Bunting
Establishing a proactive security posture involves a data-driven approach to threat detection, investigation, and response. In the past, this was challenging because there wasn’t a centralized way to collect and analyze security data across sources, but with Amazon Security Lake it is much simpler.
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By Tom O'Connell
Organizations are building data lakes and bringing data together from many systems in raw format into these data lakes, hoping to process and extract differentiated value out of this data. However, if you’re trying to get value out of operational data, whether on prem or in the cloud, there are inherent risks and costs associated with moving data from one environment to another.
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By David Bunting
Developing a proactive security strategy can potentially save an organization millions of dollars per year. According to IBM, the average cost of a data breach in 2023 added up to a staggering $4.45 million, up 15% over the last three years. This is especially true for cloud-native environments, which face unique security challenges due to their dynamic nature. Instead of waiting to respond to cybersecurity incidents after they happen, it's much better to embrace a proactive approach, and prevent them in the first place.
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By Thomas Hazel
Establishing an effective cyber threat hunting program is among the top priorities of enterprise security leaders seeking a proactive approach to detecting and counteracting potential threats. Furthermore, implementing a proactive threat hunting program, security teams that leverage formalized frameworks or threat hunting methodologies are far more likely to detect vulnerabilities or in-process malicious activities in their environments than those that do not. However, data from a 2023 threat hunting survey revealed that while 73% of organizations have adopted a defined threat hunting framework, only 38% actually follow it.
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By David Bunting
Modern data-driven organizations are synergizing operations observability, business intelligence, and data science with digital business observability programs that break down data silos, increase productivity, and drive innovation. Digital business observability combines IT and business data with cutting-edge data science techniques, enabling deeper analysis and unlocking valuable insights that propel innovation across use cases from sales and marketing to product design and financial operations.
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By Dave Armlin
Observability is a key pillar for today’s cloud-native companies. Cloud elasticity and the emergence of microservices architectures allow cloud native companies to build massively scalable architectures but also exponentially increase the complexity of IT systems.
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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.
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By David Bunting
Today's applications demand efficient data handling to provide users with seamless experiences. One solution that has gained prominence is the use of embedded databases, which are integrated within applications rather than relying on external servers. Different from a database for embedded systems, databases embedded within applications offer several advantages for storing data and analyzing it, especially in scenarios where performance, deployment simplicity, and data security are important. Embedded databases, or an embedded database management system (DBMS), can serve a variety of use cases, but are especially valuable for applications that need to provide analytics capabilities.
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By ChaosSearch
In today's data-driven landscape, the ability to derive actionable insights from log data is more critical than ever. Among the plethora of log formats, JSON (JavaScript Object Notation) has emerged as a prevalent choice for logging due to its flexibility and readability.
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By ChaosSearch
Learn how ChaosSearch analyzes log and event gaming data from fictitious car racing game racing legends.
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By ChaosSearch
Thomas Hazels talks to John Furrier, co-founder of SiliconANGLE on data: AI, LLM, and Chaos LakeDB.
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By ChaosSearch
Built from the ground up to transform your cloud storage into a Live Search+SQL+GenAI Analytics Database. Ed Walsh shares details on the new Chaos LakeDB.
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By ChaosSearch
Built from the ground up to transform your cloud storage into a Live Search+SQL+GenAI Analytics Database.
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By ChaosSearch
Built from the ground up to transform your cloud storage into a Live Search+SQL+GenAI Analytics Database.
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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.
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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.