Dynatrace

OCTOBER 7, 2020

Seamlessly report and be alerted on non-topology-related custom metrics, using Dynatrace as a metric database. Because you can now seamlessly report non-topological metrics, you can now use Dynatrace as a metric database. This allows you to: Use auto-adaptive baselines for all your custom metrics.

Metrics 246

Metrics Analytics Storage Monitoring 246

All Things Distributed

JUNE 29, 2011

For Amazon retail, some of those dimensions are low pricing, large catalog, fast shipping, and convenience. For example, when our retail customers contributed to create larger economies of scale for Amazon.com, we used the savings to lower pricing such that our customers could also benefit. Driving Storage Costs Down for AWS Customers.

AWS 84

AWS Retail Innovation Strategy 84

All Things Distributed

OCTOBER 2, 2017

Today marks the 10 year anniversary of Amazon's Dynamo whitepaper , a milestone that made me reflect on how much innovation has occurred in the area of databases over the last decade and a good reminder on why taking a customer obsessed approach to solving hard problems can have lasting impact beyond your original expectations.

Internet 111

Internet Internet AWS Performance 111

Highly Scalable

APRIL 2, 2012

Some time ago I participated in design of a backend for one large online retailer company. From the technical perspective, the following properties should be highlighted: All data is initially stored in the relational database, but this database is heavily loaded because it is a master record for many applications.

Ecommerce 100

Ecommerce Cache Storage Architecture 100

ScaleOut Software

JANUARY 9, 2020

Consider a retail chain of stores or restaurants with tens of thousands of outlets. It’s not enough just to pick out interesting events from an aggregated data stream and then send them to a database for offline analysis using Spark. Walgreens has more than 9,000, and McDonald’s has more than 14,000 in the U.S.

Logistics 52

Logistics Retail Analytics Azure 52

ScaleOut Software

JANUARY 9, 2020

Consider a retail chain of stores or restaurants with tens of thousands of outlets. It’s not enough just to pick out interesting events from an aggregated data stream and then send them to a database for offline analysis using Spark. Walgreens has more than 9,000, and McDonald’s has more than 14,000 in the U.S.

Logistics 52

Logistics Retail Analytics Azure 52

ScaleOut Software

JANUARY 9, 2020

Consider a retail chain of stores or restaurants with tens of thousands of outlets. It’s not enough to just pick out interesting events from an aggregated data stream and then send them to a database for offline analysis using Spark. Walgreens has more than 9,000, and McDonald’s has more than 14,000 in the U.S.

Logistics 52

Logistics Retail Analytics Azure 52

Smashing Magazine

SEPTEMBER 20, 2021

For example, someone might web scrape all the product pages of a competitor’s retail site to harvest information about products being offered and current pricing to try to gain a competitive edge. These are page views loaded from a previously-viewed web page that was saved to device local storage. Turning The Data Into Information.

Website 108

Website Analytics Google Cache 108

Abhishek Tiwari

DECEMBER 22, 2017

Cheap storage and on-demand compute in the cloud coupled with the emergence of new big data frameworks and tools are forcing us to rethink the whole ETL and data warehousing architecture. In classic Extract, transform, and load (ETL) model, we store entities in their corresponding application databases i.e. as rows in the relational tables.

Big Data 40

Big Data Retail Storage Google 40

Smashing Magazine

NOVEMBER 2, 2021

Some of the names include Amazon’s Luna, TikTok, Tinder, among many online retailers. On the contrary, a native application of an e-commerce store can come at 30, 50, or even 100 MB and up, consuming internal device storage. These multiple “heads” are attached to the backend and database. Image credit: Apivita. Large preview ).

Code 90

Code Cache Mobile Retail 90

ScaleOut Software

AUGUST 19, 2014

With their tightly integrated client-side caching, IMDGs typically provide much faster access to this shared data than backing stores, such as blob stores, database servers, and NoSQL stores. The Need to Keep It Simple. Likewise, it must be easy to remove IMDG servers when the workload decreases and creates excess capacity.

Cloud 40

Cloud Azure Virtualization Scalability 40

ScaleOut Software

AUGUST 19, 2014

With their tightly integrated client-side caching, IMDGs typically provide much faster access to this shared data than backing stores, such as blob stores, database servers, and NoSQL stores. The Need to Keep It Simple. Likewise, it must be easy to remove IMDG servers when the workload decreases and creates excess capacity.

Cloud 40

Cloud Azure Virtualization Scalability 40