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.

More data is available than ever, challenging organizations to change how they interact with their data so they can get the most out of it. Ahmed Munir, Lead SAP Functional Technology Architect Manager at Whirlpool Corporation, has 16 years of SAP leadership experience. He joins us to share what he has learned about building great data teams, upcoming trends in data analytics to keep an eye on, and how data teams will evolve over the next 5-10 years.

As a global leader in technology research, Gartner supports enterprise organizations, non-profits, and government agencies by sharing information and in-depth analysis of emerging technological trends, tools, and products. With the continued growth of big data over the past decade, Gartner has been especially invested in helping data and analytics (D&A) leaders make the right decisions for managing and generating value from data within their organizations.

Global data, analytics and technology companies such as Equifax, and their Engineering teams, depend on log analytics for a variety of operational analytics use cases, from application troubleshooting to streamlining cloud operations and regulatory compliance management. ChaosSearch is uniquely positioned to help companies like Equifax significantly reduce the time, cost, and complexity of log analytics.