ChaosSearch

Analyzing streams shouldn’t break the bank. 🏦 But as volume balloons, so does the cost of observability tools like Datadog, ELK, CloudWatch or Loki; SIEMs like Splunk or Sumo Logic; and data warehouses like Snowflake or BigQuery. That’s because the architectures of these solutions were not built to efficiently ingest and analyze streams at scale. But ChaosSearch is, which allows our customers to realize 50-80% savings with unlimited retention. 💸 Achieve an industry-leading price with guaranteed savings at scale: https://lnkd.in/dC26BrTX

Imagine if all your data across systems and storage platforms could be instantly accessible in real time. ChaosSearch’s unified live data lake approach lets you connect and analyze data from any cloud storage location seamlessly, making it easy to generate insights and take action quickly. Start turning fragmented data into a single source of insights. Ready to see the power of a unified data lake? With ChaosSearch, your data is always connected, accessible, and ready for real-time decision-making. Learn more: https://lnkd.in/dsM4nMCp

💠 1. SQL/Relational Querying Capabilities for Log Analytics Users When we think about log analytics users, we’re thinking of ITOps personnel that use log analytics to monitor the health and performance of cloud services, DevOps teams that use log analytics to detect application performance issues and assess user behavior, and SecOps teams that use log analytics to enable their security monitoring and threat hunting activities. 💠 2. Log Analytics Insights for Business Intelligence (BI) Users BI is a set of strategies, methods, and technologies used by organizations to collect, analyze, and extract useful insights from business data. When we think about BI users, we’re thinking about revenue operations (RevOps) teams who analyze sales and marketing data to optimize the customer journey, product teams who analyze data from a variety of sources to drive strategic decision-making, business analysts who create reports on everything from product performance to operational outcomes, and C-Suite executives who consume those reports to understand business results and inform strategic decision-making. 💠 3. Multi-Dimensional Data Lake Analytics on Your Data Lake for All Business Users With log analytics and business intelligence happening in a single data lake that supports both relational and full text search, the last step to achieving true multi-model analytics is to enable machine learning queries. Our final use case illustrates how both the users of BI tools and the users of log analytics software can combine text search, relational, and machine learning analysis to develop new insights. Learn more about enabling relational access to log data with ChaosSearch: https://bit.ly/3ISeQlX

Before finding ChaosSearch, BlackPoint Cyber used a hosted Elasticsearch deployment to analyze data in Amazon S3. The team quickly found their margins eroding, as the cost of ingesting and retaining data became too high to justify. The engineering team selected ChaosSearch when Murchinson challenged them to find a tool that could add 1-2 points of gross margin. Working from the thesis that elite data design on ingest, storage, and processing could drive competitive advantage, the team tested ChaosSearch. Switching to ChaosSearch was easy. The ThreatOps team could use the OpenSearch API within ChaosSearch to analyze data in S3 in a familiar way. Within a week, a portion of the system was up and running in a production environment. They quickly saw the cost difference from their previous Elasticsearch cluster, while realizing the same performance benefits. “Complexity is the enemy of any live operation,” said Murchinson. “The biggest difference from Elasticsearch is that ChaosSearch separates storage and compute, so we are able to spend less and search at the same performance. We ingest data into S3 and our analytics require little management or performance tuning. Scaling is fast and seamless. Best of all, data is stored on infrastructure we own, so we maintain command and control over it.” Today, the team uses ChaosSearch as a part of its elite ThreatOps and threat detection offerings. They are able to retain logs for longer, which is critical for long-term threat hunting, data breach investigations, and compliance purposes. In addition, the engineering team relies on ChaosSearch for troubleshooting within their own systems. 🛡️ Learn more about how the Blackpoint team uses ChaosSearch as a part of its elite ThreatOps and threat detection offerings: https://bit.ly/3LrDlK7

False positives happen when security systems misinterpret benign activity as malicious. These errors can stem from overly broad detection rules, redundant threat intelligence feeds, or outdated information. For example, a legitimate IP address might be flagged because it shares characteristics with a known malicious actor. While the intent is to err on the side of caution, the result is an overwhelming number of security alerts that provide little value. ⛔ The impact of false positives extends far beyond inconvenience. When SOC analysts are bombarded with unnecessary alerts, they can become desensitized to the constant noise, a phenomenon known as alert fatigue. This not only increases the risk of missing real threats but also takes a toll on morale and productivity. Analysts waste valuable time and resources investigating non-issues, which diverts attention from proactive tasks like threat hunting and improving security posture. 🔒 The consequences are tangible. SOCs may find themselves struggling to keep up with the volume of alerts, leaving them less prepared to address genuine threats. Over time, the organization’s overall security posture suffers, as the focus shifts from addressing real risks to managing the flood of false alarms. In essence, false positives hinder a SOC’s ability to effectively find true positives and protect the organization. Learn more about crushing false positives: https://lnkd.in/dcQaGycF

Transform you cloud storage into stateless live analytics database with Chaos LakeDB. Live operational and business use cases all leverage the same type of data - now you can collapse them into a single analytic database! Achieve #Observability and #SecurityAnalytics without retention limits at a fraction of the cost, plus User Insights in real-time across the organization, all enhanced with a natural language assistant powered by #GenAI. Learn more: https://bit.ly/3QhmWLI

Mosaic AI is a suite of tools that allows Databricks users to build, manage, and deploy software solutions that incorporate AI, ML, and large language model (LLM) technologies. Mosaic AI is fully integrated within the Databricks Data Intelligence Platform, which provides a single solution for storing data in a unified data lakehouse, training AI and machine learning models, and deploying those AI/ML solutions in production. Databricks Mosaic AI encompasses the following products: 💠 Mosaic AI Vector Search - A queryable vector database integrated with the Databricks Platform, Mosaic AI Vector Search is used in LLM solutions to store and retrieve mathematical representations of the semantic contents of text or image data. 💠 Mosaic AI Agent Framework - A set of Databricks tools that allow developers to build, deploy, and evaluate AI agents using Retrieval Augmented Generation (RAG), an AI design technique that augments an existing LLM with an external knowledge base. 💠Mosaic AI Model Serving - A solution for deploying LLMs and accessing Gen-AI models, including open LLMs (via Foundation Model APIs) and external LLMs hosted outside Databricks. 💠Mosaic AI Gateway - A tool for managing the usage of Gen-AI models, Mosaic AI Gateway delivers monitoring, governance, and production readiness features like usage tracking, access permissions, and traffic routing. 💠Mosaic AI Model Training - An AI model training solution that allows users to customize open-source LLMs or cost-effectively train new ones using enterprise data. 💠 Feature Store - A solution for creating, publishing, and re-using features used to train ML models or feed batch inference pipelines. 💠 Databricks AutoML - Databricks AutoML is a solution that provides a low-code approach to building, training, and deploying ML models. 💠 MLflow - MLflow is an open-source platform used to manage artifacts and workflows throughout the MLOps pipeline - from initial model development and training, through to deployment and operation. 💠Lakehouse Monitoring - A tool for monitoring data quality in the data lakehouse, Lakehouse Monitoring can also be used to track the performance of ML models and model-serving endpoints. Though not technically a Mosaic AI product, Databricks Unity Catalog is another important service that provides centralized discovery, management, and governance of models and data stored in the Databricks lakehouse. Learn more about Databricks Mosaic AI use cases: https://lnkd.in/dy4-aXCV

What is CloudOps? ☁️ By renting elastic cloud resources, enterprises can support new customer platforms, distributed workforces, and back-office operations. The cross-functional discipline of CloudOps helps enterprises manage cloud resources by optimizing applications and infrastructure. But, none of this can be done without the right strategies and techniques to analyze your application telemetry data — primarily logs and events. Dive deeper into the cloud management practice of CloudOps, and how it can help cloud-native teams ensure operational efficiency and business continuity, with our latest article: https://bit.ly/33YpIjx

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. 🔍 ChaosSearch is uniquely positioned to offer robust capabilities for handling JSON logs due to several key features and innovations within its platform. Sandro Lima demonstrates how the ChaosSearch platform empowers you to: 1️⃣ Efficiently Ingest JSON Logs: Say goodbye to data overload! Learn how our platform seamlessly ingests JSON logs, ensuring you capture every valuable piece of information without the headache of manual processing. 2️⃣ Tackle Array Flattening: Conquer the complexities of array flattening with ease. Discover how our solution simplifies this process, allowing you to navigate and analyze nested arrays effortlessly. 3️⃣ Navigate Nested Objects: Don't let nested objects slow you down. Our demo will showcase strategies for effortlessly navigating through nested structures, enabling you to extract insights efficiently. 4️⃣ Master Nested Field Analytics: Unlock the full potential of your data with advanced nested field analytics capabilities. Gain actionable insights from even the most intricate nested data structures. Watch the full video 👇 and learn how to process, store and analyze JSON data with ultimate flexibility 🤸♂️: https://bit.ly/3F53Wcy

Faster insights, lower costs, less hassle. That’s the ChaosSearch promise. 🚀 Say goodbye to complex data workflows and hello to seamless analytics, all while saving time and resources. Our approach eliminates the need for data migration and reduces storage costs, letting you achieve meaningful results faster. From small businesses to large enterprises, we’re helping teams get more value from their data with less hassle. Ready to take control? Discover how ChaosSearch is reducing time, cost, and complexity for our customers: https://lnkd.in/dN_KpNkJ