PHP powers 90% of our application; both rendering the front-end, as the background processes (data capture, processing, etc.)
GitHub's git repositories, issues database (for tracking bugs & possible improvements) and pull requests (for peer reviews of code) are all used extensively.
Used for sending real-time notifications to connected browsers.
Used as main communication tool for the whole team.
Used as main storage for user settings, account settings, etc. (Our social data itself resides in ElasticSearch.)
Used as a caching layer (when we need more functionality than simple key-value storage); keeping lists of online users, used for our smart-assigning feature, keeping track of sliding-window rate limiting information.
Powers the more advanced aspects of our front-end interface, esp. the real-time inbox.
Used for continuous integration; e.g. running unit tests before deployment and of open GitHub Pull Requests.
All social data we track is stored in ElasticSearch to make it easily searchable and for advanced statistics. Our several ElasticSearch clusters hold several billion social messages.
Used as simple key-value cache store. To store query results etc.
Used as central Message Broker; off-loading tasks to be executed asynchronous, used as communication tool between different microservices, used as tool to handle peaks in incoming data, etc.
Used for sending real-time updates to connected browsers; to give real-time feedback about e.g. messages assigned to you, resolves in the inbox, etc.
We're using the Authy service of Twilio to support 2FA for our web application.
All wireframes & mockups for new features & updated features are stored, commented & revised via InVision.
Used for graphing internal logging data; including metrics related to how fast we serve pages and execute MySQL/ElasticSearch queries.
We use HAProxy to load balance web requests for our web application, but also for some internal load balancing of microservices.
BrowserStack is used by our development and support teams during QA and to try and reproduce bugs.
Our main machine learning service is written in Clojure.
Used as main Server Monitoring (load averages, system metrics) and App Monitoring (via custom plugins) tool. Both for graphing data as for sending alerts.
Several of the visualisations in our Insights pages use D3.js as their engine.
Helps us in showing thumbnails & detailed images for urls found in tracked social data.
Used for all mockups of new & improved features. Discussing Sketch files happens via InVision, creating style guides of Sketch files via Zeplin.