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By George Hamilton
Modern organizations generate and collect vast amounts of log data each day from an ever-increasing number of sources that includes IT infrastructure, networking devices, applications, cloud services, security tools, and more. This data is essential for powering use cases from security operations and threat hunting to application performance monitoring, but tapping into the full potential of log data can be challenging for organizations without the right tools and capabilities.
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By David Bunting
Machine learning operations (MLOps) is a practice that focuses on the operationalization of machine learning models. It involves automating and streamlining the lifecycle of ML models, from development and training to deployment and monitoring. Much like data operations (DataOps), MLOps aims to improve the speed and accuracy of the data you’re accessing and analyzing.
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By Sandro Lima
Many enterprises face significant challenges when it comes to building data pipelines in AWS, particularly around data ingestion. As data from diverse sources continues to grow exponentially, managing and processing it efficiently in AWS is critical. Without these capabilities, it’s harder to analyze and get any meaning from your data.
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By Thomas Hazel
While data lakes make it easy to store and analyze a wide variety of data types, they can become data swamps without the proper documentation and governance. Until you solve the biggest data lake challenges — tackling exponential big data growth, costs, and management complexity — efficient and reliable data analytics will remain out of reach.
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By David Bunting
Databricks is lighting the way for organizations to thrive in an increasingly AI-driven world. The Databricks Platform is built on lakehouse architecture, empowering organizations to break down existing data silos, store enterprise data in a single centralized repository with unified data governance powered by Unity Catalog, and make the data available to a variety of user groups to support diverse analytics use cases.
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By David Bunting
Businesses today rely heavily on data to inform decisions, predict trends, and optimize operations. However, more data volume and complexity has led to growing pressure to find scalable, cost-effective solutions for data storage while staying within IT budgets. Companies want to handle both structured and unstructured data efficiently, while supporting advanced data analysis and machine learning use cases.
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By Dave Armlin
Today’s enterprise networks are complex. Potential attackers have a wide variety of access points, particularly in cloud-based or multi-cloud environments. Modern threat hunters have the challenge of wading through vast amounts of data in an effort to separate the signal from the noise. That’s where a security data lake can come into play.
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By David Bunting
Started in 2009 as a research project at UC Berkeley, Apache Spark transformed how data scientists and engineers work with large data sets, empowering countless organizations to accelerate time-to-value for their analytics activities. Apache Spark is now the most popular engine for distributed data processing at scale, with thousands of companies (including 80% of the Fortune 500) using Spark to support their big data analytics initiatives.
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By David Bunting
Built on the foundation of Apache Spark, Databricks is a unified, open data lakehouse platform that empowers customers to efficiently and cost-effectively process, store, manage, and analyze large volumes of enterprise data.
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By David Bunting
For organizations that generate large amounts of data, implementing a cloud database solution is a critical step towards enabling performant and cost-effective data storage, transformation, and analytics. Choosing the right cloud database solution involves careful consideration of features, capabilities, costs, and use cases to ensure alignment with your organization’s needs and objectives. This blog post features an in-depth comparison of four popular cloud database solutions: Databricks vs.
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By ChaosSearch
ChaosSearch brings key capabilities like full tech search to data bricks enabling log and event analytics for observable and security use cases natively in the data lakehouse to deploy chaos search in your data bricks workspace. Check out our 2 minute demo of ChaosSearch running on Databricks.
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By ChaosSearch
ChaosSearch + Databricks Deliver on the best of Databricks (open Spark-based data lakehouse) and ELK (efficient search, flexible live ingestion, API/UI) via ChaosSearch on Databricks. Log analytics for observability / security with unlimited retention at a fraction of the cost now with Databricks’ AI/ML. Watch as ChaosSearch CEO, Ed Walsh, shares the power of ChaosSearch in your Databricks environment.
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By ChaosSearch
What are the 7 challenges of data analytics? This preview highlights the series that Thomas Hazel, CTO & Founder of ChaosSearch will cover.
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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.
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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.
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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.
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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.
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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.
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By ChaosSearch
Data preparation issues, particularly at scale, can be solved. How? This demo walks you through how ChaosSearch helps.
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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.