All Things Distributed

SEPTEMBER 5, 2013

Over the past few years, two important trends that have been disrupting the database industry are mobile applications and big data. The explosive growth in mobile devices and mobile apps is generating a huge amount of data, which has fueled the demand for big data services and for high scale databases.

Big Data 98

Big Data Mobile Latency Database 98

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. A high CPU cost due to marshalling data to/from the RInK store formats to the application data format. Who knew! ;).

Cache 79

Cache Latency Google Lambda 79

All Things Distributed

AUGUST 16, 2011

There are different considerations when deciding where to allocate resources with latency and cost being the two obvious ones, but compliance sometimes plays an important role as well. Government and Big Data. One particular early use case for AWS GovCloud (US) will be massive data processing and analytics.

AWS 111

AWS Government Big Data Cloud 111

The Netflix TechBlog

JULY 26, 2021

Netflix is known for its loosely coupled microservice architecture and with a global studio footprint, surfacing and connecting the data from microservices into a studio data catalog in real time has become more important than ever. In the initial stage, data consumers set up ETL pipelines directly pulling data from databases.

Big Data 253

Big Data Government Analytics Processing 253

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. a Fast and Scalable NoSQL Database Service Designed for Internet Scale Applications. Job Openings in AWS - Senior Leader in Database Services. blog comments powered by Disqus.

AWS 60

AWS Cloud Latency Storage 60

All Things Distributed

JULY 13, 2010

Further computationally intensive, highly parallel workloads have found their way to Amazon EC2 as businesses have explored using HPC types of algorithms for other application categories, for example to to process very large unstructured data sets for Business Intelligence applications. Driving down the cost of Big-Data analytics.

Cloud 118

Cloud AWS Automotive Latency 118

All Things Distributed

DECEMBER 5, 2010

A simple example is the situation with Persons and Telephones; a person has a name, a person can have one or more telephones and each phone can have one or more telephone numbers. Modern systems require much faster update propagation to for example deal with outages. Job Openings in AWS - Senior Leader in Database Services.

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. Jurisdictions - Some customers face regulatory requirements regarding where data is stored.

AWS 61

AWS Cloud Latency Storage 61

Highly Scalable

MAY 1, 2012

This approach often leads to heavyweight high-latency analytical processes and poor applicability to realtime use cases. The picture above depicts the fact that this data set basically occupies 40MB of memory (10 million of 4-byte elements). what is the cardinality of the data set)?

Analytics 191

Analytics Traffic Big Data Efficiency 191

The Netflix TechBlog

OCTOBER 27, 2020

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. The processed data is typically stored as data warehouse tables in AWS S3.

Latency 243

Latency Storage Big Data Tuning 243

The Netflix TechBlog

DECEMBER 21, 2020

For example, we allow a partition to have a few small files instead of always merging files in perfect sizes. These principles reduce resource usage by being more efficient and effective while lowering the end-to-end latency in data processing. Both automatic (event-driven) as well as manual (ad-hoc) optimization.

Storage 203

Storage Latency Efficiency Data Engineering 203

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

For example, the most fundamental abstraction trade-off has always been latency versus throughput. Modern CPUs strongly favor lower latency of operations with clock cycles in the nanoseconds and we have built general purpose software architectures that can exploit these low latencies very well. Where to go from here?

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. Consistent Read.

AWS 69

AWS Latency Database Scalability 69