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Improve Application Latency With Read Replicas Using YugabyteDB [Video]

DZone

Scalability and low latency are crucial for any application that relies on real-time data. One way to achieve this is by storing data closer to the users. In this post, we'll discuss how you can use YugabyteDB and its read replica nodes to improve the read latency for users across the globe.

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Scalable Annotation Service?—?Marken

The Netflix TechBlog

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. But there are more interesting cases where users want to store temporal (time-based) data or spatial data. Movie Entity with id 1234 has violence.

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Why applying chaos engineering to data-intensive applications matters

Dynatrace

The jobs executing such workloads are usually required to operate indefinitely on unbounded streams of continuous data and exhibit heterogeneous modes of failure as they run over long periods. This significantly increases event latency. Performance is usually a primary concern when using stream processing frameworks.

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The Power of Caching: Boosting API Performance and Scalability

DZone

Caching is the process of storing frequently accessed data or resources in a temporary storage location, such as memory or disk, to improve retrieval speed and reduce the need for repetitive processing. Bandwidth optimization: Caching reduces the amount of data transferred over the network, minimizing bandwidth usage and improving efficiency.

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Streaming SQL in Data Mesh

The Netflix TechBlog

Democratizing Stream Processing @ Netflix By Guil Pires , Mark Cho , Mingliang Liu , Sujay Jain Data powers much of what we do at Netflix. On the Data Platform team, we build the infrastructure used across the company to process data at scale. The existing Data Mesh Processors have a lot of overlap with SQL.

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Bulldozer: Batch Data Moving from Data Warehouse to Online Key-Value Stores

The Netflix TechBlog

By Tianlong Chen and Ioannis Papapanagiotou Netflix has more than 195 million subscribers that generate petabytes of data everyday. Data scientists and engineers collect this data from our subscribers and videos, and implement data analytics models to discover customer behaviour with the goal of maximizing user joy.

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Datadog Creates Scalable Data Ingestion Architecture

InfoQ

Datadog created a dedicated data ingestion architecture offering exactly-once semantics for their third-generation event store, Husky. The event-driven architecture (EDA) can accommodate bursts in traffic in the multi-tenant platform with reasonable ingestion latency and acceptable operational costs. By Rafal Gancarz