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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. Logs on Grail Log data is foundational for any IT analytics.

Analytics 182
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Exploratory analytics and collaborative analytics capabilities democratize insights across teams

Dynatrace

Having access to large data sets can be helpful, but only if organizations are able to leverage insights from the information. These analytics can help teams understand the stories hidden within the data and share valuable insights. “That is what exploratory analytics is for,” Schumacher explains. .

Analytics 196
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Any analysis, any time: Dynatrace Log Management and Analytics powered by Grail

Dynatrace

Log management and analytics is an essential part of any organization’s infrastructure, and it’s no secret the industry has suffered from a shortage of innovation for several years. Several pain points have made it difficult for organizations to manage their data efficiently and create actual value. What’s next for Grail?

Analytics 231
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What is software automation? Optimize the software lifecycle with intelligent automation

Dynatrace

In what follows, we define software automation as well as software analytics and outline their importance. What is software analytics? This involves big data analytics and applying advanced AI and machine learning techniques, such as causal AI. We also discuss the role of AI for IT operations (AIOps) and more.

Software 184
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Snowflake Workload Optimization

DZone

In the era of big data, efficient data management and query performance are critical for organizations that want to get the best operational performance from their data investments.

Big Data 130
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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.

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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 213