As enteprise networks grow larger and more complex, IT teams are increasingly dependent on the enhanced network visibility and monitoring capabilities provided by log analytics solutions. Log analytics gives enterprise Engineering, DevOps, and SecOps teams the ability to efficiently troubleshoot cloud services and infrastructure, monitor the security posture of enterprise IT assets, and measure application performance throughout the application lifecycle or DevOps release pipeline.

Wondering how to create a dashboard in Kibana to visualize and analyze your log data? In this blog post, we’ll provide a step-by-step explanation of how to create a dashboard in Kibana. You’ll learn how to use Kibana to query indexed application and event log data, filter query results to highlight the most critical and actionable information, build Kibana visualizations using your log data, and incorporate those visualizations into a Kibana dashboard.

Streaming analytics in AWS gives enterprises the ability to process and analyze log data in real time, enabling use cases that range from delivering personalized customer experiences to anomaly and fraud detection, application troubleshooting, and user behavior analysis. In the past, real-time log analytics solutions could process just a few thousand records per second and it would still take minutes or hours to process the data and get answers.

Since the 2006 launch of Amazon Web Services (AWS), the world’s first hyper-scale public cloud provider, thousands of data-driven businesses have shifted on-premise data storage and analytics workloads into the cloud by architecting or adopting a cloud data platform. As the volume, variety, and velocity of enterprise data continues to grow in 2023, cloud data platforms with legacy tech and complex architectures are becoming increasingly time-consuming and costly to manage.

There is a long history of clustering architectures with respect to building distributed databases for two primary reasons. The first is scalability. If a cluster of nodes has reached its capacity to perform work, adding additional nodes are introduced to handle the increased load. The second is availability. The ability to ensure that if a node fails, let’s say during ingestion and/or querying, remaining nodes would continue to execute due to state replication.

For the past 30 years, the primary data source for business intelligence (BI) and data visualization tools has generally been either a data warehouse or a data mart. But as enterprises today struggle to cope with the growing complexity, scale, and speed of data, it’s becoming clear that the data tools of 30 years ago weren’t designed to handle the enterprise data management challenges of today - especially with the growing variety and amounts of data that enterprises are generating.

If you use a data lake, chances are you need a way to keep your data searchable for business users. When combined with the analytics capabilities of a cloud data platform, a data catalog can solve some of the common pain points around “data swamps,” where users fail to gain any meaningful insights from their data. Some of a business’s most valuable assets lie within its data.

Any data-driven organization will tell you that the holy grail is faster time to insights. But the unfortunate truth is that business users often have to wait days — even weeks or months — to analyze the data they need. Behind the scenes, data engineering teams put a lot of work into joining disparate datasets, creating pipelines, and delivering a final data product back to their stakeholders for analysis.

re:Invent 2022 is just around the corner and we couldn’t be more excited to share the latest ChaosSearch innovations and capabilities with our current and future customers in the AWS ecosystem. Enterprise DevOps teams, SREs, and data engineers everywhere are struggling to navigate the growing costs and complexity of big data analytics, particularly when it comes to operational data.