Amazon DynamoDB

Back in 2014, I was given an opportunity to re-architect SmartZip Analytics platform, and flagship product: SmartTargeting. This is a SaaS software helping real estate professionals keeping up with their prospects and leads in a given neighborhood/territory, finding out (thanks to predictive analytics) who's the most likely to list/sell their home, and running cross-channel marketing automation against them: direct mail, online ads, email... The company also does provide Data APIs to Enterprise customers.

I had inherited years and years of technical debt and I knew things had to change radically. The first enabler to this was to make use of the cloud and go with AWS, so we would stop re-inventing the wheel, and build around managed/scalable services.

For the SaaS product, we kept on working with Rails as this was what my team had the most knowledge in. We've however broken up the monolith and decoupled the front-end application from the backend thanks to the use of Rails API so we'd get independently scalable micro-services from now on.

Our various applications could now be deployed using AWS Elastic Beanstalk so we wouldn't waste any more efforts writing time-consuming Capistrano deployment scripts for instance. Combined with Docker so our application would run within its own container, independently from the underlying host configuration.

Storage-wise, we went with Amazon S3 and ditched any pre-existing local or network storage people used to deal with in our legacy systems. On the database side: Amazon RDS / MySQL initially. Ultimately migrated to Amazon RDS for Aurora / MySQL when it got released. Once again, here you need a managed service your cloud provider handles for you.

Future improvements / technology decisions included:

Caching: Amazon ElastiCache / Memcached CDN: Amazon CloudFront Systems Integration: Segment / Zapier Data-warehousing: Amazon Redshift BI: Amazon Quicksight / Superset Search: Elasticsearch / Amazon Elasticsearch Service / Algolia Monitoring: New Relic

As our usage grows, patterns changed, and/or our business needs evolved, my role as Engineering Manager then Director of Engineering was also to ensure my team kept on learning and innovating, while delivering on business value.

One of these innovations was to get ourselves into Serverless : Adopting AWS Lambda was a big step forward. At the time, only available for Node.js (Not Ruby ) but a great way to handle cost efficiency, unpredictable traffic, sudden bursts of traffic... Ultimately you want the whole chain of services involved in a call to be serverless, and that's when we've started leveraging Amazon DynamoDB on these projects so they'd be fully scalable.

We really drank the Google Kool-Aid on analytics. So, everything's going into Google BigQuery and almost everything is going straight into Google Cloud Pub/Sub and then doing some processing in Google Cloud Dataflow before ending up in BigQuery. We still do too much processing and augmentation on the front end before it goes into Pub/Sub. And that's using some kind of stuff we pulled together using Amazon DynamoDB and so on. And it's very brittle, actually. Actually, Dynamo throttling is one of our biggest headaches. So, I want all of that to go away and do all our augmentation in BigQuery after the data's been collected. And having it just go straight into Pub/Sub. So, we're working on that. And it'll happen, some time. #Analytics #AnalyticsPipeline

Working on a project recently, wanted an easy modern frontend to work with, decoupled from our backend. To get things going quickly, decided to go with React, Redux.js, redux-saga, Bootstrap.

On the backend side, Go is a personal favourite, and wanted to minimize server overheads so went with a #serverless architecture leveraging AWS Lambda, AWS CloudFormation, Amazon DynamoDB, etc.

For IDE/tooling I tend to stick to the #JetBrains tools: WebStorm / Goland.

Obviously using Git, with GitLab private repo's for managing code/issues/etc.

Uploadcare has built an infinitely scalable infrastructure by leveraging AWS. Building on top of AWS allows us to process 350M daily requests for file uploads, manipulations, and deliveries. When we started in 2011 the only cloud alternative to AWS was Google App Engine which was a no-go for a rather complex solution we wanted to build. We also didn’t want to buy any hardware or use co-locations.

Our stack handles receiving files, communicating with external file sources, managing file storage, managing user and file data, processing files, file caching and delivery, and managing user interface dashboards.

At its core, Uploadcare runs on Python. The Europython 2011 conference in Florence really inspired us, coupled with the fact that it was general enough to solve all of our challenges informed this decision. Additionally we had prior experience working in Python.

We chose to build the main application with Django because of its feature completeness and large footprint within the Python ecosystem.

All the communications within our ecosystem occur via several HTTP APIs, Redis, Amazon S3, and Amazon DynamoDB. We decided on this architecture so that our our system could be scalable in terms of storage and database throughput. This way we only need Django running on top of our database cluster. We use PostgreSQL as our database because it is considered an industry standard when it comes to clustering and scaling.

Over the years we have added a wide variety of different storages to our stack including PostgreSQL (some hosted by Heroku, some by Amazon RDS) for storing relational data, Amazon DynamoDB to store non-relational data like recommendations & user connections, or Redis to hold pre-aggregated data to speed up API endpoints.

Since we started running Postgres ourselves on RDS instead of only using the managed offerings of Heroku, we've gained additional flexibility in scaling our application while reducing costs at the same time.

We are also heavily testing Amazon RDS for Aurora in its Postgres-compatible version and will also give the new release of Aurora Serverless a try!

#SqlDatabaseAsAService #NosqlDatabaseAsAService #Databases #PlatformAsAService

To store device health records as it allows super fast writes and range queries. Amazon DynamoDB

몇몇 로그는 현재 AWS DynamoDB 에 기록되고 있습니다. 개선을 통해 mongodb 로 옮길 계획을 하고 있습니다. 아주 간단한 데이터를 쌓는 용도로는 나쁘지 않습니다. 다만, 쿼리가 아주 제한적입니다. 사용하기 전에 반드시 DynamoDB 의 스펙을 확인할 필요가 있습니다. Amazon DynamoDB

For most of the stuff we use MySQL. We just use Amazon RDS. But for some stuff we use Amazon DynamoDB. We love DynamoDB. It's amazing. We store usage data in there, for example. I think we have close to seven or eight hundred million records in there and it's scaled like you don't even notice it. You never notice any performance degradation whatsoever. It's insane, and the last time I checked we were paying $150 bucks for that. Amazon DynamoDB