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Measuring the importance of data quality to causal AI success

Dynatrace

Traditional analytics and AI systems rely on statistical models to correlate events with possible causes. It removes much of the guesswork of untangling complex system issues and establishes with certainty why a problem occurred. Fragmented and siloed data storage can create inconsistencies and redundancies. Timeliness.

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Boost DevOps maturity with observability and a data lakehouse

Dynatrace

They’re unleashing the power of cloud-based analytics on large data sets to unlock the insights they and the business need to make smarter decisions. From a technical perspective, however, cloud-based analytics can be challenging. Research has found that 99% of organizations have embraced a multicloud architecture.

DevOps 186
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Managing risk for financial services: The secret to visibility and control during times of volatility

Dynatrace

If system failures occur, teams must resolve them quickly and resolutely. Maximizing a strong foundation of IT infrastructure performance and security means effectively utilizing all the data that these systems produce to drive risk management processes and decision-making. Automated issue resolution.

Analytics 203
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Geek Reading - Week of June 5, 2013

DZone

Simpler UI Testing with CasperJS ( Architects Zone – Architectural Design Patterns & Best Practices). Using MongoDB as a cache store ( Architects Zone – Architectural Design Patterns & Best Practices). Linux System Mining with Python ( Javalobby – The heart of the Java developer community). Hacker News).

Java 244
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Log Analysis: How to Digest 15 Billion Logs Per Day and Keep Big Queries Within 1 Second

DZone

Such a gigantic log analysis system is part of their cybersecurity management. For the need of real-time monitoring, threat tracing, and alerting, they require a log analytic system that can automatically collect, store, analyze, and visualize logs and event records.

Analytics 124
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What is a Distributed Storage System

Scalegrid

A distributed storage system is foundational in today’s data-driven landscape, ensuring data spread over multiple servers is reliable, accessible, and manageable. This guide delves into how these systems work, the challenges they solve, and their essential role in businesses and technology.

Storage 130
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Pioneering customer-centric pricing models: Decoding ingest-centric vs. answer-centric pricing

Dynatrace

The rise of cloud-native microservice architectures further exacerbates this change. Moreover, the system lacks flexibility, imposing strict schemas that administrators and developers must adhere to avoid additional costs. All data is readily accessible without storage tiers, such as costly solid-state drives (SSDs).

Retail 239