Alternatives to Help Scout

As a small startup we are very conscious about picking up the tools we use to run the project. After suffering with a mess of using at the same time Trello , Slack , Telegram and what not, we arrived at a small set of tools that cover all our current needs. For product management, file sharing, team communication etc we chose Basecamp and couldn't be more happy about it. For Customer Support and Sales Intercom works amazingly well. We are using MailChimp for email marketing since over 4 years and it still covers all our needs. Then on payment side combination of Stripe and Octobat helps us to process all the payments and generate compliant invoices. On techie side we use Rollbar and GitLab (for both code and CI). For corporate email we picked G Suite. That all costs us in total around 300$ a month, which is quite okay.

We moved from Intercom to Crisp this April because the price-value ratio of Intercom was not satisfying anymore.

We paid ~140eur for the very basic features of Intercom - Messages Essential and Inbox Essential. This is enough for a chat and API access, but that's all. The price would go up as mkdev grows.

Now there are some features we really would love to have: like a Help Center and Bots, for example. All various advanced routing of messages. Or any other features that Intercom actually has, but sells them separately.

Even though it's very hard to properly calculate the price by looking at Pricing page of Intercom, my guess is that with simple Answer bot and Help Center integration our bill for Intercom could easily double or triple.

After doing a bit of research and looking for a better price-value ration we found Crisp.

Crisp gives us the same Chat features we had from Intercom, but then it adds really cool bot builder, various marketing automation utilities, Help Center that supports multiple languages already today (feature still missing in Intercom) - https://help.mkdev.me/en/, fancy MagicBrowse and LiveAssist, direct integration with Telegram and many more. Price? 95eur for all the features and unlimited operators. And no dependency on number of active users (Crisp founders directly say that charging for active users is bullshit and I can only agree with them).

We've been using Crisp not for too long and even though it's been pretty smooth so far - from integrating with our backend systems to creating a Help Center from scratch - it might be a bit too early to do any conclusions. mkdev co-founder Leo has things to say about the UX of Crisp and I am not really satisfied with Crisp's mobile app. But this is something to get used to, or something that will be improved by Crisp over time. And some aspects of Crisp UX/UI are much nicer than Intercom - for example, custom fields on clients are on very top, so we can quickly jump to admin page of a client in mkdev.me. In Intercom we had to do two clicks and scroll a lot to find this link.

To sum it up, if you are looking for a change from Intercom, give Crisp a try. It's way cheaper and doesn't have any major downsides if you are used to Intercom.

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

Why we built Marmaray, an open source generic data ingestion and dispersal framework and library for Apache Hadoop :

Built and designed by our Hadoop Platform team, Marmaray is a plug-in-based framework built on top of the Hadoop ecosystem. Users can add support to ingest data from any source and disperse to any sink leveraging the use of Apache Spark . The name, Marmaray, comes from a tunnel in Turkey connecting Europe and Asia. Similarly, we envisioned Marmaray within Uber as a pipeline connecting data from any source to any sink depending on customer preference:

https://eng.uber.com/marmaray-hadoop-ingestion-open-source/

(Direct GitHub repo: https://github.com/uber/marmaray Kafka Kafka Manager )