Monte Carlo Announces Release of Observability Platform for Locally Sourced, Small-Batch Data

Today, on April 1st, Monte Carlo announced the release of Data Observability Small Batch, a next-generation platform for locally-sourced, small-batch data. The solution was painstakingly crafted by artisan developers to serve a new wave of data engineers who are nostalgic for data platforms the way they used to be.

“The world is tired of over-processed data, mass-marketed to them in over-hyped dashboards,” says Barr Moses, CEO, Monte Carlo. “Now, data engineers want clean, organic data that is farm-to-table. They don’t want synthetic data meshes. They want authentic data fabrics, hand-woven by local craftspeople.”

The Data Observability Small Batch platform focuses on the same five pillars of data observability, but optimized for data that is locally foraged, hand-made, and produced in small quantities:

  • Freshness: Never again will a data engineer hear an analyst say “this data stinks!” We infuse your data with scents of lavender, lemon-grass, and Mountain Dew Code Red.
  • Volume: Row counts and growth rates are frequently checked to prevent over-harvesting of wild data.
  • Distribution: Values in important columns must have originated from a cage-free environment. It must have been given a wide range with infinite boundaries and no preconceived expectations. It’s very Montessori. 
  • Schema: Every small batch of data is reviewed by a Master of Fine Arts to ensure its arrangement is completely unique and personalized to every data engineer. 
  • Lineage: The origins of your data will be tracked back to the source to ensure it is sustainably collected and Fair Trade.

And this just scratches the surface. Quite literally… there just isn’t enough data.

Critically acclaimed reception

Data Observability Small Batch received rave reviews from the data observability platform critics during its soft-launch at the famed French data festival Caché.

Data engineering tastemaker Ivanna Goodrow said:

We used Snowflake, dbt, Monte Carlo, and Looker. We sync’d data in with Fivetran. Things were easy… the data simply showed up every morning and was processed and delivered to the business in dashboards. I didn’t need to think about it.

 But I grew dissatisfied. Something was missing for me, and it wasn’t just the metadata. I’d wonder, ‘who made this data? Where did it come from?’ I wanted to touch it, feel it, and know the typists who input it.

Finally, with Data Observability Small Batch, there is a platform that doesn’t treat data like something that should be run through an assembly line.

Mononymous senior data engineering manager, Hype, said:

Data Observability Small Batch is a perfect fit for us. Our organization rejects mainstream data culture and its false constructs of scalability, efficiency, and reliability.  

We’ve upended our systems. We went back to receiving data as flat-file email attachments. We processed it with a team of interns with hand-calculators. We even brought in a guy with an abacus. 

We now draft charts and graphs on paper, which I deliver to the CEO’s desk each morning. The results have been wonderful. Nobody complains about the data anymore. Nobody has access to it. It’s very exclusive.

Invitation only

Small Batch Data Observability will be in beta for three years and is accessible by private invitation only. It has no website.

“This is a hot release. All of my customers are hounding me to get an invitation to Small Batch,” said Will Robins, Head of Customer Success, Monte Carlo. “And frankly, if you have to ask, it’s probably not for you.”

To learn more about Small Batch, see our billboard just outside of the San Francisco International Airport on Highway 101 next Saturday between 2:00 and 4:00 am. 

Happy April Fools Day from your friends at Monte Carlo! This is satire (unless we get enough customer feature requests).

Book a time to speak with us in the form below.

2 responses to “Monte Carlo Announces Release of Observability Platform for Locally Sourced, Small-Batch Data”

  1. marcel grossert says:

    Many thanks – so great and awesome – it produced a big smile on my face!!

  2. Will Robins says:

    Thanks Marcel!!

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