Launch YC S21: Meet the Batch, Thread #9

There are 4 startups in this thread. The initial order is random:

Snowboard Software (YC S21) - Fast and automated data catalog for Snowflake - https://news.ycombinator.com/item?id=28315047

Jovian (YC S21) - Online data science school for professionals - https://news.ycombinator.com/item?id=28315045

Chari (YC S21) - Ecommerce and fintech app for mom and pop shops in North Africa - https://news.ycombinator.com/item?id=28315043

Tuli Health (YC S21) - Turning UK pharmacies into diagnostic centres - https://news.ycombinator.com/item?id=28315046

Four years ago, we quit our jobs and spent several months learning data science and deep learning. We completed several online courses, read some books, and participated in some Kaggle competitions. We were finally able to make a career transition to data science, but looking back at the journey, we felt “It shouldn’t be this hard!”

While there are various platforms offering data science education, most learners don't feel confident while approaching real-world projects even after completing multiple courses and earning certifications. They find it hard to clear interviews to land their first job, and often require several months of training on the job.

Jovian is the platform we wished we had access to when we started learning. There are three things we focus on: (1) we teach practical skills that students use to build real-world projects, (2) we offer 24x7 support & detailed feedback on every project, (3) students learn together with a community of like-minded learners in a university-like environment. We’ve taken inspiration from courses like fast.ai and mlcourse.ai.

Our 6-month program consists of 7 courses, 14 coding assignments and 4 portfolio projects. Every week, students watch 5-6 hrs video lectures and work on assignments for 15-20 hrs. Students can ask questions and get help 24x7 from our TAs over Slack and Zoom. Every 5 weeks, they complete a unique guided real-world project and publish a blog post. We offer 1:1 resume review, interview practice, and job search support for 12 months. We make money by charging a flat fee for the course, which students can pay up-front or monthly.

One thing we discovered and found surprising is that most of the math used in data science can be explained through code. Many people find math intimidating not because the concepts themselves are tough, but because they aren't used to mathematical symbols and equations.

We look forward to your comments!

In past jobs, I (Rick) rolled out Snowflake at a manufacturer with 11000 employees and $2B in revenue, and Théo built the data team and infrastructure at a B2B company that raised $200M. My experience introducing Snowflake was that after 2 years and more than 1000 tables and many more columns, it was hard to find data and tedious to understand it, and users would not trust the data without a lot of upfront due diligence.

We index all data and visualization assets that are connected, making it easy to find them. We profile data continuously to show what's inside, how it is changing, and make it easy to document and tag information. What helps us surface a lot of context is that we parse Snowflake's query history. This lets us provide automated data lineage and indicate data freshness and usage.

Most of our customers that see their actual data flows are surprised by the complexity they have built. Some see for the first time how their data flows work and how many redundant data assets they have created from one base table.

For now, we focus on the Snowflake ecosystem and don't support the long-tail of other database and analytics solutions. This lets us "overfit" to the needs and problems of companies that sit on this stack, e.g.: Fivetran > DBT > Snowflake > Tableau. Under the hood we leverage the flexibility of ArangoDB to offer a fast application that offers great search speed and also graph workloads e.g. for data lineage.

Our initial market is 5k Snowflake customers, but the global data warehousing market consists of 250k companies. We charge per indexed data asset. The number of data assets in companies is doubling on average every 2 years, so this market is taking off.

We offer both cloud and self-hosted. Self-hosted means a Docker Compose configuration that starts 3 Docker containers. The only data that is transmitted to our system is a license check in this case.

Your questions and comments are welcome. Thank you!

I ask because I built a SaaS product that helped customers understand their data inside a legacy accounting product. Similarly it looked in the meta data on the back end of that accounting product and built visualizations. The problem we had was that customers didn't know how to or didn't want to fix the problems that our tool found. We had to hire accountants to go into the accounting tool and fix their problems. So it ended up being more of a tool for our accountants and less of a stand alone SaaS tool that customers actually used.