Big Data, Cache and Latency - Technology Performance Pulse

Big Data

Cache

Latency

In-Stream Big Data Processing

Highly Scalable

AUGUST 20, 2013

The shortcomings and drawbacks of batch-oriented data processing were widely recognized by the Big Data community quite a long time ago. This system has been designed to supplement and succeed the existing Hadoop-based system that had too high latency of data processing and too high maintenance costs.

Big Data

Big Data Processing Lambda Database

Migrating Critical Traffic At Scale with No Downtime?—?Part 1

The Netflix TechBlog

MAY 4, 2023

It provides a good read on the availability and latency ranges under different production conditions. The upstream service calls the existing and new replacement services concurrently to minimize any latency increase on the production path. It helps expose memory leaks, deadlocks, caching issues, and other system issues.

Traffic

Traffic Latency Tuning Systems

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Trending Sources

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

Dynatrace

OCTOBER 4, 2022

Data lakehouses deliver the query response with minimal latency. While data lakehouses combine the flexibility and cost-efficiency of data lakes with the querying capabilities of data warehouses, it’s important to understand how these storage environments differ. Data warehouses.

Artificial Intelligence

Artificial Intelligence Storage Analytics Government

Formulating ‘Out of Memory Kill’ Prediction on the Netflix App as a Machine Learning Problem

The Netflix TechBlog

JULY 21, 2022

We at Netflix, as a streaming service running on millions of devices, have a tremendous amount of data about device capabilities/characteristics and runtime data in our big data platform. With large data, comes the opportunity to leverage the data for predictive and classification based analysis.

Big Data

Big Data Cache Engineering Data Engineering

Helios: hyperscale indexing for the cloud & edge – part 1

The Morning Paper

OCTOBER 26, 2020

Helios also serves as a reference architecture for how Microsoft envisions its next generation of distributed big-data processing systems being built. What follows is a discussion of where big data systems might be heading, heavily inspired by the remarks in this paper, but with several of my own thoughts mixed in.

Cloud

Cloud Big Data Latency Architecture

Redis vs Memcached in 2024

Scalegrid

MARCH 28, 2024

Key Takeaways Redis offers complex data structures and additional features for versatile data handling, while Memcached excels in simplicity with a fast, multi-threaded architecture for basic caching needs. Introduction Caching serves a dual purpose in web development – speeding up client requests and reducing server load.

Cache

Cache Storage Architecture Scalability

Expanding the Cloud – An AWS Region is coming to Hong Kong

All Things Distributed

JUNE 20, 2017

This enables customers to serve content to their end users with low latency, giving them the best application experience. In 2008, AWS opened a point of presence (PoP) in Hong Kong to enable customers to serve content to their end users with low latency. Since then, AWS has added two more PoPs in Hong Kong, the latest in 2016.

AWS

AWS Logistics Cloud Social Media

Expanding the Cloud with DNS - Introducing Amazon Route 53 - All.

All Things Distributed

DECEMBER 5, 2010

There are two main types of DNS servers: authoritative servers and caching resolvers. But the real robustness of the DNS system comes through the way lookups are handled, which is what caching resolvers do. Caching techniques ensure that the DNS system doesnt get overloaded with queries. No Server Required - Jekyll & Amazon S3.

Cloud

Cloud Internet Internet AWS

How LinkedIn Serves Over 4.8 Million Member Profiles per Second

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

Cache Latency Traffic Database

Fast key-value stores: an idea whose time has come and gone

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

Cache Latency Google Network

What is a Distributed Storage System

Scalegrid

FEBRUARY 8, 2024

Opting for synchronous replication within distributed storage brings about reinforced consistency and integrity of data, but also bears higher expenses than other forms of replicating data. By implementing data replication strategies, distributed storage systems achieve greater.

Storage

Storage Systems Big Data Azure

Seer: leveraging big data to navigate the complexity of performance debugging in cloud microservices

The Morning Paper

MAY 14, 2019

Seer: leveraging big data to navigate the complexity of performance debugging in cloud microservices Gan et al., on end-to-end latency) and less than 0.15% on throughput. This tracing system is similar to Dapper and Zipkin and records per-microservice latencies and number of outstanding requests. ASPLOS’19.

Big Data

Big Data Cloud Performance Hardware

5 data integration trends that will define the future of ETL in 2018

Abhishek Tiwari

DECEMBER 27, 2017

A unified data management (UDM) system combines the best of data warehouses, data lakes, and streaming without expensive and error-prone ETL. It offers reliability and performance of a data warehouse, real-time and low-latency characteristics of a streaming system, and scale and cost-efficiency of a data lake.

Big Data

Big Data Artificial Intelligence Storage Hardware

Current status, needs, and challenges in Heterogeneous and Composable Memory from the HCM workshop (HPCA’23)

ACM Sigarch

MAY 31, 2023

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. even lowered the latency by introducing a multi-headed device that collapses switches and memory controllers.

Latency

Latency Hardware Cache Architecture

In-Stream Big Data Processing

Migrating Critical Traffic At Scale with No Downtime?—?Part 1

Trending Sources

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

Formulating ‘Out of Memory Kill’ Prediction on the Netflix App as a Machine Learning Problem

Helios: hyperscale indexing for the cloud & edge – part 1

Redis vs Memcached in 2024

Expanding the Cloud – An AWS Region is coming to Hong Kong

Expanding the Cloud with DNS - Introducing Amazon Route 53 - All.

How LinkedIn Serves Over 4.8 Million Member Profiles per Second

Fast key-value stores: an idea whose time has come and gone

What is a Distributed Storage System

Seer: leveraging big data to navigate the complexity of performance debugging in cloud microservices

5 data integration trends that will define the future of ETL in 2018

Current status, needs, and challenges in Heterogeneous and Composable Memory from the HCM workshop (HPCA’23)

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