The Netflix TechBlog
JANUARY 25, 2023
Scalable Annotation Service — Marken by Varun Sekhri , Meenakshi Jindal Introduction At Netflix, we have hundreds of micro services each with its own data models or entities. All data should be also available for offline analytics in Hive/Iceberg. Unlike Java, we support multiple inheritance as well.
Dynatrace
NOVEMBER 3, 2023
that offers security, scalability, and simplicity of use. focused on technology coverage, building on the flexibility of JMX for Java and Python-based coded extensions for everything else. Python code also carries limited scalability and the burden of governing its security in production environments and lifecycle management.
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Dynatrace
JANUARY 12, 2022
Messaging systems can significantly improve the reliability, performance, and scalability of the communication processes between applications and services. We’ve introduced brand-new analytics capabilities by building on top of existing features for messaging systems. Dynatrace news. New to Dynatrace?
ScaleOut Software
JULY 14, 2021
Whether it’s health-tracking watches, long-haul trucks, or security sensors, extracting value from these devices requires streaming analytics that can quickly make sense of the telemetry and intelligently react to handle an emerging issue or capture a new opportunity.
ScaleOut Software
JULY 21, 2020
ScaleOut StateServer® Pro Adds Analytics to In-Memory Data Grids . Designed to help scalable applications deliver high performance, it stores live, fast-changing data in memory (DRAM) for fast updates and retrieval. Java applications use a similar mechanism.). In-Memory Data Grids for Fast-Changing Data.
ScaleOut Software
JULY 21, 2020
ScaleOut StateServer® Pro Adds Analytics to In-Memory Data Grids . Designed to help scalable applications deliver high performance, it stores live, fast-changing data in memory (DRAM) for fast updates and retrieval. Java applications use a similar mechanism.). In-Memory Data Grids for Fast-Changing Data.
The Netflix TechBlog
OCTOBER 19, 2020
Our tactical approach was to use Netflix-specific libraries for collecting traces from Java-based streaming services until open source tracer libraries matured. We chose Open-Zipkin because it had better integrations with our Spring Boot based Java runtime environment. Storage: don’t break the bank!
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