Scalegrid

FEBRUARY 8, 2024

Their design emphasizes increasing availability by spreading out files among different nodes or servers — this approach significantly reduces risks associated with losing or corrupting data due to node failure. These distributed storage services also play a pivotal role in big data and analytics operations.

Storage 130

Storage Systems Big Data Azure 130

All Things Distributed

DECEMBER 5, 2010

While registrars manage the namespace in the DNS naming architecture, DNS servers are used to provide the mapping between names and the addresses used to identify an access point. There are two main types of DNS servers: authoritative servers and caching resolvers. Driving down the cost of Big-Data analytics.

Cloud 111

Cloud Internet Internet AWS 111

All Things Distributed

APRIL 27, 2011

To our shareowners: Random forests, naïve Bayesian estimators, RESTful services, gossip protocols, eventual consistency, data sharding, anti-entropy, Byzantine quorum, erasure coding, vector clocks. Look inside a current textbook on software architecture, and youll find few patterns that we dont apply at Amazon.

Technology 127

Technology Technology AWS Storage 127

Scalegrid

JANUARY 10, 2025

They keep the features that developers like but can handle much more data, similar to NoSQL systems. Notably, they simplify handling big data flows, offer consistent transactions, and sustain high performance even when they’re used for real-time data analysis and complex queries.

Database 130

Database Scalability Best Practices Blockchain 130

The Morning Paper

MAY 14, 2019

Seer: leveraging big data to navigate the complexity of performance debugging in cloud microservices Gan et al., We’re not told how Seer figures out that a major architectural change has happened. An equally large fraction are due to compute contention, followed by network, cache, memory, and disk contention.

Big Data 45

Big Data Cloud Performance Hardware 45

The Morning Paper

JUNE 23, 2019

Generally to cache data (including non-persistent data that never sees a backing store), to share non-persistent data across application services (e.g. If you want to store time-expiring data that should be shared across application processes, used Memcached or Redis. Fetching too much data in a single query (i.e.,

Cache 79

Cache Latency Google Network 79

Abhishek Tiwari

DECEMBER 27, 2017

A common theme across all these trends is to remove the complexity by simplifying data management as a whole. In 2018, we anticipate that ETL will either lose relevance or the ETL process will disintegrate and be consumed by new data architectures. Unified data management architecture.

Big Data 49

Big Data Artificial Intelligence Storage Hardware 49

InfoQ

JULY 3, 2023

LinkedIn introduced Couchbase as a centralized caching tier for scaling member profile reads to handle increasing traffic that has outgrown their existing database cluster. The new solution achieved over 99% hit rate, helped reduce tail latencies by more than 60% and costs by 10% annually. By Rafal Gancarz

Cache 86

Cache Latency Traffic Database 86

ACM Sigarch

MAY 31, 2023

Introduction Memory systems are evolving into heterogeneous and composable architectures. Heterogeneous and Composable Memory (HCM) offers a feasible solution for terabyte- or petabyte-scale systems, addressing the performance and efficiency demands of emerging big-data applications.

Latency 52

Latency Hardware Cache Architecture 52

ScaleOut Software

NOVEMBER 24, 2020

However, telematics architectures face challenges in responding to telemetry in real time. Current Telematics Architecture. The volume of incoming telemetry challenges current telematics systems to keep up and quickly make sense of all the data. Challenges for Current Architectures.

Analytics 52

Analytics Architecture Scalability Software Architecture 52

The Netflix TechBlog

OCTOBER 15, 2019

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.),

Transportation 199

Transportation Architecture Processing Storage 199