Tomasz Rybczynski

May 3, 2024 | dynatrace.com | Tomasz Rybczynski

Real-time streaming needs real-time analyticsAs enterprises move their workloads to cloud service providers like Amazon Web Services, the complexity of observing their workloads increases. Log data—the most verbose form of observability data, complementing other standardized signals like metrics and traces—is especially critical. As cloud complexity grows, it brings more volume, velocity, and variety of log data. Managing this change is difficult.

Apr 17, 2024 | dynatrace.com | Tomasz Rybczynski |Troy Mangum

Mainframe is a strong choice for hybrid cloud, but it brings observability challengesIBM Z is a mainframe computing platform chosen by many organizations with a hybrid cloud strategy because of its security, resiliency, performance, scalability, and sustainability. With the availability of Linux on IBM Z and LinuxONE, the IBM Z platform brings a familiar host operating system and sustainability that could yield up to 75% energy reduction compared to x86 servers.

Nov 2, 2023 | dynatrace.com | Tomasz Rybczynski |Peter Putz

Spiraling cloud architecture and application costs have driven the need for new approaches to cloud spend. Nearly half (49%) of organizations believe their cloud bill is too high, according to a CloudZero survey. Further, a Flexera report found that small to medium-sized businesses spend approximately $1.2 million on cloud computing, while large enterprises shell out upward of $12 million annually. That’s where FinOps can help.

Oct 30, 2023 | dynatrace.com | Tomasz Rybczynski |Hans Lõugas |Troy Mangum

The growing challenge in modern IT environments is the exponential increase in log telemetry data, driven by the expansion of cloud-native, geographically distributed, container- and microservice-based architectures. Organizations need a more proactive approach to log management to tame this proliferation of cloud data. By following key log analytics and log management best practices, teams can get more business value from their data.

Oct 6, 2023 | dynatrace.com | Gary Kaiser |Tomasz Rybczynski |Mario Kahlhofer |Hans Lõugas

Grail, the Dynatrace causational data lakehouse, was explicitly designed for observability and security data, with artificial intelligence integrated into its foundation. We’ve further enhanced its capabilities to meet the high standards of large enterprises by incorporating record-level permission policies. To fully utilize Grail features, it’s recommended that you incorporate its unique buckets and security policies at the beginning of your observability journey.