This site uses cookies to improve your experience. To help us insure we adhere to various privacy regulations, please select your country/region of residence. If you do not select a country, we will assume you are from the United States. Select your Cookie Settings or view our Privacy Policy and Terms of Use.
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Used for the proper function of the website
Used for monitoring website traffic and interactions
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Strictly Necessary: Used for the proper function of the website
Performance/Analytics: Used for monitoring website traffic and interactions
Stream processing One approach to such a challenging scenario is stream processing, a computing paradigm and softwarearchitectural style for data-intensive software systems that emerged to cope with requirements for near real-time processing of massive amounts of data.
This is both frustrating for companies that would prefer making ML an ordinary, fuss-free value-generating function like softwareengineering, as well as exciting for vendors who see the opportunity to create buzz around a new category of enterprise software. SoftwareArchitecture. This approach is not novel.
The net result is, for many datasets, vastly more efficient use of RAM. and can achieve orders of magnitude more efficient data access, which opens up many possibilities. with the same amount of memory Hollow may be able to cache 100% of your dataset and achieve a 100% hit rate.
. • More than one-third have adopted site reliability engineering (SRE); slightly less have developed production AI services. Softwareengineers represent the largest cohort, comprising almost 20% of all respondents (see Figure 1 ). For this audience, SRE’s future is brighter than AI’s, however. Respondent Demographics.
It acts as a collaboration tool to balance the need for business correctness, user experience, and technical efficiency. If you’d like to go through the whole process of modelling domains, shaping the softwarearchitecture, and finding aggregates, join my 2 day workshop at DDD EU in February 2020. Hope to see you there.
Two particularly relevant patterns are Efficiency Enables Evolution and Higher Order Systems Create New Sources of Worth. In Wardley lingo, Google Maps is so efficient that it acts as a building block for higher-order systems (e.g. A good engineering organization moves at speed with high reliability.
We organize all of the trending information in your field so you don't have to. Join 5,000+ users and stay up to date on the latest articles your peers are reading.
You know about us, now we want to get to know you!
Let's personalize your content
Let's get even more personalized
We recognize your account from another site in our network, please click 'Send Email' below to continue with verifying your account and setting a password.
Let's personalize your content