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Dynatrace supports SnapStart for Lambda as an AWS launch partner

Dynatrace

The new Amazon capability enables customers to improve the startup latency of their functions from several seconds to as low as sub-second (up to 10 times faster) at P99 (the 99th latency percentile). This can cause latency outliers and may lead to a poor end-user experience for latency-sensitive applications.

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Dynatrace accelerates business transformation with new AI observability solution

Dynatrace

The RAG process begins by summarizing and converting user prompts into queries that are sent to a search platform that uses semantic similarities to find relevant data in vector databases, semantic caches, or other online data sources. Estimates show that NVIDIA, a semiconductor manufacturer, could release 1.5

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Best practices and key metrics for improving mobile app performance

Dynatrace

Examples of observability data include metrics, logs, and traces which provide visibility into the app’s behavior and performance at different levels of the stack, including the application code, infrastructure, and network. Load time and network latency metrics. Issue remediation. Performance optimization. Capacity planning.

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Redis® Monitoring Strategies for 2024

Scalegrid

Identifying key Redis® metrics such as latency, CPU usage, and memory metrics is crucial for effective Redis monitoring. To monitor Redis® instances effectively, collect Redis metrics focusing on cache hit ratio, memory allocated, and latency threshold.

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What is a data lakehouse? Combining data lakes and warehouses for the best of both worlds

Dynatrace

The result is a framework that offers a single source of truth and enables companies to make the most of advanced analytics capabilities simultaneously. The performance of these queries needs to be at a level where they can support ad-hoc analytics use cases. Data lakehouses deliver the query response with minimal latency.

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Procella: unifying serving and analytical data at YouTube

The Morning Paper

Procella: unifying serving and analytical data at YouTube Chattopadhyay et al., Typically, organizations build specialized infrastructure for each of these use cases. This, however, creates silos of data and processing, and results in a complex, expensive, and harder to maintain infrastructure. VLDB’19. are divided.

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Designing Instagram

High Scalability

FUN FACT : In this talk , Rodrigo Schmidt, director of engineering at Instagram talks about the different challenges they have faced in scaling the data infrastructure at Instagram. When a user requests for feed then there will be two parallel threads involved in fetching the user feeds to optimize for latency. System Components.

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