All Things Distributed

DECEMBER 14, 2011

This new Region has been highly requested by companies worldwide, and it provides low-latency access to AWS services for those who target customers in South America. The new Sao Paulo Region provides better latency to South America, which enables AWS customers to deliver higher performance services to their South American end-users.

AWS 114

AWS Latency Storage Big Data 114

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 and Memcached both provide high performance with sub-millisecond response times. Managed DBaaS solutions like ScaleGrid.io

Cache 130

Cache Storage Scalability Architecture 130

Scalegrid

FEBRUARY 8, 2024

At its core, a distributed storage system comprises three main components: a controller for managing the system’s operations, an internal datastore where information is held, and databases geared towards ensuring scalability, partitioning capabilities, and high availability for all types of data.

Storage 130

Storage Systems Big Data Azure 130

All Things Distributed

JULY 13, 2010

Cluster Computer Instances for Amazon EC2 are a new instance type specifically designed for High Performance Computing applications. In those days, my main goal was to take the advances in building the highly dedicated High Performance Cluster environments and turn them into commodity technologies for the enterprise to use.

Cloud 118

Cloud AWS Automotive Latency 118

All Things Distributed

DECEMBER 3, 2009

This new Region consists of multiple Availability Zones and provides low-latency access to the AWS services from for example the Bay Area. In addition, the EU (Ireland) Region is available to customers who want local access to services from Europe to address their performance or jurisdiction requirements. Contact Info. Werner Vogels.

AWS 60

AWS Cloud Latency Storage 60

The Netflix TechBlog

JULY 26, 2021

Operational Reporting is a reporting paradigm specialized in covering high-resolution, low-latency data sets, serving detailed day-to-day activities¹ and processes of a business domain. The Journey In the past few years, Netflix Studio has gone through few iterations of data movement approaches.

Big Data 253

Big Data Government Analytics Processing 253

All Things Distributed

DECEMBER 5, 2010

I am very excited that today we have launched Amazon Route 53, a high-performance and highly-available Domain Name System (DNS) service. Low-latency query resolution The query resolution functionality of Route 53 is based on anycast, which will route the request automatically to the DNS server that is the closest. Comments ().

Cloud 117

Cloud Internet Internet AWS 117

All Things Distributed

APRIL 28, 2010

There are four main reasons to do so: Performance - For many applications and services, data access latency to end users is important. The new Singapore Region offers customers in APAC lower-latency access to AWS services. a Fast and Scalable NoSQL Database Service Designed for Internet Scale Applications.

AWS 61

AWS Cloud Latency Storage 61

Scalegrid

MARCH 14, 2024

Tailoring resource allocation efficiently ensures faster application performance in alignment with organizational demands. On-Premises Data Center A hybrid cloud architecture necessitates that an organization retains full authority over its physical or virtual infrastructure within the private cloud segment.

Strategy 130

Strategy Cloud Artificial Intelligence Infrastructure 130

The Netflix TechBlog

DECEMBER 21, 2020

By Anupom Syam Background At Netflix, our current data warehouse contains hundreds of Petabytes of data stored in AWS S3 , and each day we ingest and create additional Petabytes. We built AutoOptimize to efficiently and transparently optimize the data and metadata storage layout while maximizing their cost and performance benefits.

Storage 203

Storage Latency Efficiency Data Engineering 203

The Netflix TechBlog

OCTOBER 27, 2020

Usually Data scientists and engineers write Extract-Transform-Load (ETL) jobs and pipelines using big data compute technologies, like Spark or Presto , to process this data and periodically compute key information for a member or a video. The processed data is typically stored as data warehouse tables in AWS S3.

Latency 243

Latency Storage Big Data Tuning 243

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. I say go ahead and use local state as a performance boost, so long as you’re fine to have that state wiped out at any moment.

Cache 79

Cache Latency Google Lambda 79

Abhishek Tiwari

DECEMBER 27, 2017

ETL refers to extract, transform, load and it is generally used for data warehousing and data integration. ETL is a product of the relational database era and it has not evolved much in last decade. There are several emerging data trends that will define the future of ETL in 2018. Machine learning meets data integration.

Big Data 49

Big Data Artificial Intelligence Storage Hardware 49

All Things Distributed

NOVEMBER 15, 2010

This incredible power is available for anyone to use in the usual pay-as-you-go model, removing the investment barrier that has kept many organizations from adopting GPUs for their workloads even though they knew there would be significant performance benefit. with a peak performance of 4.701 PetaFLOPS.

AWS 136

AWS Latency Programming Architecture 136

All Things Distributed

FEBRUARY 24, 2010

Amazon SimpleDB has launched today with a new set of features giving the customer more control over which consistency and concurrency models to use in their database operations. These new features will make it easier to transition those applications to SimpleDB that are designed with traditional database tools in mind.

AWS 69

AWS Latency Database Scalability 69

Highly Scalable

MAY 1, 2012

Analysis of such large data sets often requires powerful distributed data stores like Hadoop and heavy data processing with techniques like MapReduce. This approach often leads to heavyweight high-latency analytical processes and poor applicability to realtime use cases. what is the cardinality of the data set)?

Analytics 191

Analytics Traffic Big Data Efficiency 191