Does your team know how to use data analytics to their advantage? For the vast majority of companies, the answer may be “no.” According to Accenture, only 21% of people are confident in their data literacy skills, and just 32% of companies have realized tangible, measurable value from their data. While the definition of data literacy varies depending on who you ask, at its core, the term means equipping anyone in your organization to know how to use data in a business context.

Troubleshooting cloud services and infrastructure is an ongoing challenge for organizations of all sizes. As organizations adopt more cloud services and their cloud environments grow more complex, they naturally produce more telemetry data – including application, system and security logs that document all types of events. All cloud services and infrastructure components generate their own, distinct logs.

In our Data Lake vs Data Warehouse blog, we explored the differences between two of the leading data management solutions for enterprises over the last decade. We highlighted the key capabilities of data lakes and data warehouses with real examples of enterprises using both solutions to support data analytics use cases in their daily operations.

Sometimes, the data you want to analyze lives in AWS S3 buckets by default. If that’s the case for the data you need to work with, good on you: You can easily ingest it into an analytics tool that integrates with S3. But what if you have a data source — such as logs generated by applications running in a Kubernetes cluster — that isn’t stored natively in S3? Can you manage and analyze that data in a cost-efficient, scalable way? The answer is yes, you can.

The global market for log management was valued at $1.9 billion in 2020, according to MarketsandMarkets. Analysts predict this will grow to $3.7 billion by 2025. With this massive growth in log data, there will also be massive demand for escalated support in log management systems.

ChaosSearch is participating in the upcoming Gartner Data & Analytics Summit (May 4-6), a virtual conference for professionals and executive leaders in Data & Analytics (D&A). The summit will feature expert talks from Gartner analysts, engaging workshops, and the opportunity to participate in roundtable discussions with D&A professionals and executive leaders. This blog post was inspired by the tagline of this year’s Gartner Data & Analytics Summit: Learn, Unlearn, Relearn.

The success of data-driven initiatives for enterprise organizations depends largely on the quality of data available for analysis. This axiom can be summarized simply as garbage in, garbage out: low-quality data that is inaccurate, inconsistent, or incomplete often results in low-validity data analytics that can lead to poor business decision-making.

Since the data lake concept emerged more than a decade ago, data lakes have been pitched as the solution to many of the woes surrounding traditional data management solutions, like databases and data warehouses. Data lakes, we have been told, are more scalable, better able to accommodate widely varying types of data, cheaper to build and so on. Much of that is true, at least theoretically.

Enterprise data growth is accelerating rapidly in 2021, challenging organizations to adopt cloud data retention strategies that maximize the value of data and fulfill compliance needs while minimizing costs. To meet this challenge, organizations are adopting or refining their cloud data retention strategies. In this blog post, we’ll take a closer look at the state of data retention and analytics in the cloud.

Logs are automatically-generated records of events that take place within a cloud-based application, network, or infrastructure service. These records are stored in log files, creating an audit trail of system events that can be analyzed for a variety of purposes, including: Enterprise organizations use log analytics software to aggregate, transform, and analyze data from log files, developing insights that drive business decisions and operational excellence.