What is PubNub and what are its top alternatives?
PubNub alternatives & related posts
related Socket.IO posts
I use Socket.IO because the application has 2 frontend clients, which need to communicate in real-time. The backend-server handles the communication between these two clients via websockets. Socket.io is very easy to set up in Node.js and ExpressJS.
In the research project, the 1st client shows panoramic videos in a so called cave system (it is the VR setup of our research lab, which consists of three big screens, which are specially arranged, so the user experience the videos more immersive), the 2nd client controls the videos/locations of the 1st client.
related Kafka posts
The algorithms and data infrastructure at Stitch Fix is housed in #AWS. Data acquisition is split between events flowing through Kafka, and periodic snapshots of PostgreSQL DBs. We store data in an Amazon S3 based data warehouse. Apache Spark on Yarn is our tool of choice for data movement and #ETL. Because our storage layer (s3) is decoupled from our processing layer, we are able to scale our compute environment very elastically. We have several semi-permanent, autoscaling Yarn clusters running to serve our data processing needs. While the bulk of our compute infrastructure is dedicated to algorithmic processing, we also implemented Presto for adhoc queries and dashboards.
Beyond data movement and ETL, most #ML centric jobs (e.g. model training and execution) run in a similarly elastic environment as containers running Python and R code on Amazon EC2 Container Service clusters. The execution of batch jobs on top of ECS is managed by Flotilla, a service we built in house and open sourced (see https://github.com/stitchfix/flotilla-os).
At Stitch Fix, algorithmic integrations are pervasive across the business. We have dozens of data products actively integrated systems. That requires serving layer that is robust, agile, flexible, and allows for self-service. Models produced on Flotilla are packaged for deployment in production using Khan, another framework we've developed internally. Khan provides our data scientists the ability to quickly productionize those models they've developed with open source frameworks in Python 3 (e.g. PyTorch, sklearn), by automatically packaging them as Docker containers and deploying to Amazon ECS. This provides our data scientist a one-click method of getting from their algorithms to production. We then integrate those deployments into a service mesh, which allows us to A/B test various implementations in our product.
For more info:
- Our Algorithms Tour: https://algorithms-tour.stitchfix.com/
- Our blog: https://multithreaded.stitchfix.com/blog/
- Careers: https://multithreaded.stitchfix.com/careers/
#DataScience #DataStack #Data
As we've evolved or added additional infrastructure to our stack, we've biased towards managed services. Most new backing stores are Amazon RDS instances now. We do use self-managed PostgreSQL with TimescaleDB for time-series data—this is made HA with the use of Patroni and Consul.
We also use managed Amazon ElastiCache instances instead of spinning up Amazon EC2 instances to run Redis workloads, as well as shifting to Amazon Kinesis instead of Kafka.
related Firebase posts
This is my stack in Application & Data
My Utilities Tools
Google Analytics Postman Elasticsearch
My Devops Tools
Git GitHub GitLab npm Visual Studio Code Kibana Sentry BrowserStack
My Business Tools
Fontumi focuses on the development of telecommunications solutions. We have opted for technologies that allow agile development and great scalability.
Firebase and Node.js + FeathersJS are technologies that we have used on the server side. Vue.js is our main framework for clients.
Our latest products launched have been focused on the integration of AI systems for enriched conversations. Google Compute Engine , along with Dialogflow and Cloud Firestore have been important tools for this work.
Git + GitHub + Visual Studio Code is a killer stack.
related Twilio posts
Nexmo vs Twilio ?
Back in the early days at SmartZip Analytics, that evaluation had - for whatever reason - been made by Product Management. Some developers might have been consulted, but we hadn't made the final call and some key engineering aspects of it were omitted.
When revamping the platform, I made sure to flip the decision process how it should be. Business provided an input but Engineering lead the way and has the final say on all implementation matters. My engineers and I decided on re-evaluating the criteria and vendor selection. Not only did we need SMS support, but were we not thinking about #VoiceAndSms support as the use cases evolved.
Also, on an engineering standpoint, SDK mattered. Nexmo didn't have any. Twilio did. No-one would ever want to re-build from scratch integration layers vendors should naturally come up with and provide their customers with.
Twilio won on all fronts. Including costs and implementation timelines. No-one even noticed the vendor switch.
Many years later, Twilio demonstrated its position as a leader by holding conferences in the Bay Area, announcing features like Twilio Functions. Even acquired Authy which we also used for 2FA. Twilio's growth has been amazing. Its recent acquisition of SendGrid continues to show it.