Alternatives to Boomerang

When I started at StackShare, I needed an easy way to create a pipeline of the different partners we were engaged with, and track the status of those conversations. Having just begun to explore partnerships in earnest, our needs at StackShare don't necessitate something as robust as Salesforce Sales Cloud, Close.io or similar offerings that exist.

Nevertheless, I didn't want one more Google Doc to track things either, and I heard about Streak, so I figured I'd give the free version a try. So far, it has accommodated everything I need, and it integrates simply with Gmail which meant I had it running in a few minutes. You can create a pipeline entry directly from an email thread, which I find useful compared to logging into a separate platform, and there is basic functionality for scheduling follow-ups and tasks. The pipeline stages are fully customizable as are the fields you can add - (I added a note field to explain why someone may be in the "Backburner" stage, for example).

As we scale our Business Development initiatives and grow our team, we'll likely need to look at upgrading Streak or incorporating other tools like Yesware. But, for a quick and easy way to organize a sales pipeline and track the respective conversations, the Streak free version nicely accommodates what I need and has been very helpful in managing discussions with a variety of partners thus far.

#StackDecisionsLaunch

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 )