A data lake is a data repository for terabytes or petabytes of raw data stored in its original format. The data can originate from a variety of data sources: IoT and sensor data, a simple file, or a binary large object (BLOB) such as a video, audio, image or multimedia file. Any manipulation of the data — to put it into a data pipeline and make it usable — is done when the data is extracted from the data lake.

Dark data is all of the unused, unknown and untapped data across an organization. This data is generated as a result of users’ daily interactions online with countless devices and systems — everything from machine data to server log files to unstructured data derived from social media. Organizations may consider this data too old to provide value, incomplete or redundant, or limited by a format that can’t be accessed with available tools.

Personalized Service Health sends custom granular alerts about Google Cloud service disruptions, and integrates with incident management tooling.

Large Language Models (LLMs) can give notoriously inconsistent responses when asked the same question multiple times. For example, if you ask for help writing an Elasticsearch query, sometimes the generated query may be wrapped by an API call, even though we didn’t ask for it. This sometimes subtle, other times dramatic variability adds complexity when integrating generative AI into analyst workflows that expect specifically-formatted responses, like queries.

As today’s businesses increasingly rely on their digital services to drive revenue, the tolerance for software bugs, slow web experiences, crashed apps, and other digital service interruptions is next to zero. Developers and engineers bear the immense burden of quickly resolving production issues before they impact customer experience.

Logs and log management have been around far longer than monitoring and it is easy to forget just how useful and essential they can be for modern observability. Most of you will know us for VictoriaMetrics, our open source time series database and monitoring solution. Metrics are our “thing”; but as engineers, we’ve had our fair share of frustrations in the past caused by modern logging systems that tend to create further complexity, rather than removing it.

Logz.io is excited to announce Easy Connect, which will enable our customers to go from zero to full observability in minutes. By automating service discovery and application instrumentation, Easy Connect provides nearly instant visibility into any component in your Kubernetes-based environment – from your infrastructure to your applications. Since applications have been monitored, collecting logs, metrics, and traces have often been siloed and complex.

A recently conducted survey of 51 CISOs and other security leaders a series of questions about the current demand for cybersecurity solutions, spending intentions, security posture strategies, tool preferences, and vendor consolidation expectations. While the report highlights the trends around platform consolidation over the short run, 82% of respondents stated they expect to increase the number of vendors in the next 2-3 years.

Data pipelines are the foundational support to any business analytics project. They are growing more critical than ever as companies are leaning on their insights to drive their business: 54% of enterprises said it was vital to their future business strategies. Data pipelines play a crucial role as they perform calculations and transformations used by analysts, data scientists, and business intelligence teams.