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Financial Data Engineering in SAS

DZone

Financial data engineering in SAS involves the management, processing, and analysis of financial data using the various tools and techniques provided by the SAS software suite. Here are some key aspects of financial data engineering in SAS: 1.

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Accelerate and empower Site Reliability Engineering with Dynatrace observability

Dynatrace

Planned effort Site Reliability Engineering (SRE) effort and time allocation planning typically fall into two domains: Operations Management (50%) Operations Management includes on-call responsibilities, post-mortem assessments, addressing other interruptions, and buffer time. These practices are commonly known as “ chaos engineering. ”

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Extend business observability: Extract business events from online databases (Part 1)

Dynatrace

There are also many cases where business data—transactional, inventory, or financial—is at rest or in use , stored in a database. Dynatrace extensions can easily query data from various databases and store the results in Grail™, the Dynatrace data lakehouse. This can be accomplished using Dynatrace extensions.

Database 233
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Intelligent observability for Oracle and SQL databases

Dynatrace

While applications are built using a variety of technologies and frameworks, there is one thing they usually have in common: the data they work with must be stored in databases. Now, Dynatrace has gone a step further and expanded its coverage and intelligent observability into the next layer: database infrastructure.

Database 243
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Performance Implications of Running Databases in Kubernetes

Percona

Cloud-native databases are becoming the norm, and containerized databases are a common trend (see the report from Dynatrace and Figure 1). Kubernetes—the de facto standard for platform engineers—and operators simplify database deployment and management.

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Building Resilience With Chaos Engineering and Litmus

DZone

These incidents underscore the wide-ranging causes of outages in microservices architectures, encompassing configuration errors, database issues, infrastructure scaling failures, and code problems.

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Observability engineering: Getting Prometheus metrics right for Kubernetes with Dynatrace and Kepler

Dynatrace

For busy site reliability engineers, ensuring system reliability, scalability, and overall health is an imperative that’s getting harder to achieve in ever-expanding, cloud-native, container-based environments. Because of its adaptability, Prometheus has become an essential tool for observability engineering. Jolly good!

Metrics 185