Big Data, Cache, Data and Latency - Technology Performance Pulse

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

Dynatrace

OCTOBER 4, 2022

While data lakes and data warehousing architectures are commonly used modes for storing and analyzing data, a data lakehouse is an efficient third way to store and analyze data that unifies the two architectures while preserving the benefits of both. What is a data lakehouse? How does a data lakehouse work?

Artificial Intelligence

Artificial Intelligence Analytics Storage Government

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

Redis vs Memcached in 2024

Scalegrid

MARCH 28, 2024

In this comparison of Redis vs Memcached, we strip away the complexity, focusing on each in-memory data store’s performance, scalability, and unique features. Redis is better suited for complex data models, and Memcached is better suited for high-throughput, string-based caching scenarios.

Cache

Cache Storage Scalability Architecture

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

The Netflix TechBlog

MAY 4, 2023

It can happen on an edge API system servicing customer devices, between the edge and mid-tier services, or from mid-tiers to data stores. It provides a good read on the availability and latency ranges under different production conditions. For instance, envision a response payload that delivers media streams for a playback session.

Traffic

Traffic Latency Tuning Systems

What is a Distributed Storage System

Scalegrid

FEBRUARY 8, 2024

A distributed storage system is foundational in today’s data-driven landscape, ensuring data spread over multiple servers is reliable, accessible, and manageable. Understanding distributed storage is imperative as data volumes and the need for robust storage solutions rise.

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

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

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

The Morning Paper

OCTOBER 26, 2020

On the surface this is a paper about fast data ingestion from high-volume streams, with indexing to support efficient querying. Helios also serves as a reference architecture for how Microsoft envisions its next generation of distributed big-data processing systems being built. PVLDB’20. Emphasis mine ).

Cloud

Cloud Big Data Latency Architecture

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

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

The Morning Paper

JUNE 23, 2019

Coupled with stateless application servers to execute business logic and a database-like system to provide persistent storage, they form a core component of popular data center service archictectures. If you want to store time-expiring data that should be shared across application processes, used Memcached or Redis.

Cache

Cache Latency Google Lambda

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

All Things Distributed

JUNE 20, 2017

The new region will give Hong Kong-based businesses, government organizations, non-profits, and global companies with customers in Hong Kong, the ability to leverage AWS technologies from data centers in Hong Kong. This enables customers to serve content to their end users with low latency, giving them the best application experience.

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

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

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

Abhishek Tiwari

DECEMBER 27, 2017

ETL refers to extract, transform, load and it is generally used for data warehousing and data integration. There are several emerging data trends that will define the future of ETL in 2018. A common theme across all these trends is to remove the complexity by simplifying data management as a whole.

Big Data

Big Data Artificial Intelligence Storage Hardware

Technology Performance Pulse

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

In-Stream Big Data Processing

Trending Sources

Redis vs Memcached in 2024

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

What is a Distributed Storage System

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

How LinkedIn Serves Over 4.8 Million Member Profiles per Second

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

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

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

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

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

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

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

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