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Privacy spotlight: Retain data in Grail with 1-day precision, for up to 10 years

Dynatrace

Streamline privacy requirements with flexible retention periods Data retention is a critical aspect of data handling, and it’s not just about privacy compliance—it’s about having the flexibility to optimize data storage times in Grail for your Dynatrace use cases.

Storage 170
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Dynatrace Perform 2024 Guide: Deriving business value from AI data analysis

Dynatrace

AI data analysis can help development teams release software faster and at higher quality. So how can organizations ensure data quality, reliability, and freshness for AI-driven answers and insights? And how can they take advantage of AI without incurring skyrocketing costs to store, manage, and query data?

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Data Engineers of Netflix?—?Interview with Pallavi Phadnis

The Netflix TechBlog

Data Engineers of Netflix?—?Interview Interview with Pallavi Phadnis This post is part of our “ Data Engineers of Netflix ” series, where our very own data engineers talk about their journeys to Data Engineering @ Netflix. Pallavi Phadnis is a Senior Software Engineer on the Product Data Science and Engineering team.

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Best practices for Fluent Bit 3.0

Dynatrace

Fluent Bit is a telemetry agent designed to receive data (logs, traces, and metrics), process or modify it, and export it to a destination. Fluent Bit can serve as a proxy before you send data to Dynatrace or similar. However, you can also use Fluent Bit as a processor because you can perform various actions on the data.

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Faster time to value with enhanced handling of OneAgent runtime data

Dynatrace

This blog post highlights a group of improvements that were released with OneAgent version 1.199. Recent improvements in OneAgent runtime-data handling. Storage mount points in a system might be larger or smaller, local or remote, with high or low latency, and various speeds. Customizable location of large runtime files.

Storage 149
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Delta: A Data Synchronization and Enrichment Platform

The Netflix TechBlog

Part I: Overview Andreas Andreakis , Falguni Jhaveri , Ioannis Papapanagiotou , Mark Cho , Poorna Reddy , Tongliang Liu Overview It is a commonly observed pattern for applications to utilize multiple datastores where each is used to serve a specific need such as storing the canonical form of data (MySQL etc.), caching (Memcached etc.),

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Pioneering customer-centric pricing models: Decoding ingest-centric vs. answer-centric pricing

Dynatrace

In today’s data-driven landscape, businesses are grappling with an unprecedented surge in data volume. The data generated by applications to support operational teams has reached overwhelming proportions. This data overload also prevents customer-centric pricing models as users consider cost-effective technology platforms.

Retail 241