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Data lakehouse innovations advance the three pillars of observability for more collaborative analytics

Dynatrace

As teams try to gain insight into this data deluge, they have to balance the need for speed, data fidelity, and scale with capacity constraints and cost. To solve this problem, Dynatrace launched Grail, its causational data lakehouse , in 2022. But logs are just one pillar of the observability triumvirate.

Analytics 191
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Path to NoOps part 1: How modern AIOps brings NoOps within reach

Dynatrace

The need for developers and innovation is now even greater. Organizations would still need a skeletal staff that can focus on innovation and oversee exception-based operations. By greatly reducing the effort required by the operations side of the equation, teams have more time to innovate and optimize processes.

DevOps 222
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Data Engineers of Netflix?—?Interview with Pallavi Phadnis

The Netflix TechBlog

Netflix’s unique work culture and petabyte-scale data problems are what drew me to Netflix. During earlier years of my career, I primarily worked as a backend software engineer, designing and building the backend systems that enable big data analytics. Moreover, its petabyte scale also brings unique engineering challenges.

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AIOps observability adoption ascends in healthcare

Dynatrace

Every day, healthcare organizations across the globe have embraced innovative technology to streamline the delivery of patient care. As patient care continues to evolve, IT teams have accelerated this shift from legacy, on-premises systems to cloud technology to more build, test, and deploy software, and fuel healthcare innovation.

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Seven benefits of AIOps to transform your business operations

Dynatrace

AIOps combines big data and machine learning to automate key IT operations processes, including anomaly detection and identification, event correlation, and root-cause analysis. Improved time management and event prioritization. Increased business innovation. What is AIOps, and how does it work?

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What is IT automation?

Dynatrace

At its most basic, automating IT processes works by executing scripts or procedures either on a schedule or in response to particular events, such as checking a file into a code repository. When monitoring tools release a stream of alerts, teams can easily identify which ones are false and assess whether an event requires human intervention.

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Applying real-world AIOps use cases to your operations

Dynatrace

Artificial intelligence for IT operations, or AIOps, combines big data and machine learning to provide actionable insight for IT teams to shape and automate their operational strategy. CloudOps includes processes such as incident management and event management. CloudOps: Applying AIOps to multicloud operations.

DevOps 202