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.

This week, ChaosSearch announced some exciting news -- we're honored to have been named Most Likely to be the Next Boston Unicorn in Startup Boston’s first-ever Community Awards! The award celebrates companies that have made extraordinary achievements in the startup ecosystem in Boston, and represents grassroots recognition from tech entrepreneurs, employees, investors, journalists, and educators in the region.

IT leaders’ experiences with data lakes have been a roller coaster ride since their inception in 2010. To some, that roller coaster ride might resemble the canonical Hype Cycle graphic, trademarked by Gartner to show the maturity curve of technologies in a given category over time. This year’s Hype Cycle for Data Management report was just released, revealing that modern data lakes are poised to exit the Trough of Disillusionment and enter the Slope of Enlightenment in 2022.

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.

DevOps has become the dominant application development and delivery methodology today, embraced over traditional software development methods by teams striving for lightning-fast innovation and more frequent releases without compromising on quality, stability, or productivity.

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.

Today’s enterprise networks are heterogeneous, have multiple entry points, integrate with cloud-based applications, offer data center delivered services, include applications that run at the edge of the network, and generate massive amounts of transactional data. In effect, enterprise networks have become larger, more complex, and more difficult to secure and manage.

Last year we published a popular paper on how to calculate the true cost of an Elasticsearch, or ELK (for Elasticsearch, Logstash, Kibana) stack environment. The paper helps readers calculate their overall annual cost of ownership for their ELK environment, and reveals how the cost burden of ELK is much higher than anticipated for most customers. That paper clearly hit a nerve — it’s been, by far, our most downloaded piece of content.

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.

At the turn of the 20th Century, enterprises shut down their clunky generators and started buying electricity from new utilities such as the Edison Illuminating Company. In doing so, they cut costs, simplified operations, and made profound leaps in productivity. The promise of modern cloud computing invites easy comparisons to those first electric utilities: outsource to them, save money and simplify.