Keeping up with the promise we made last week, we are very excited to tell you MDS Journal is now LIVE! - an open publication for every data enthusiast who wants to share their content with the data community. Free and forever will be.
Featured tools of the week
- Deepnote is a notebook that brings teams together to explore, analyze and present data from start to finish. Jupyter-compatible with real-time collaboration, in the cloud.
Deepnote has raised a total of $23.8M in funding over 3 rounds. Their latest funding was raised on Jan 31, 2022 from a Series A round.
- Tray.io helps organizations to automate complex processes through a powerful, flexible platform and connect their entire cloud stack thanks to APIs. Tray.io aims to make connecting data seamless and empower business users by enabling them to access and automate siloed SaaS data.
Tray.io has raised a total of $109.1M in funding over 9 rounds. Their latest funding was raised on Nov 26, 2019 from a Series C round.
Featured Data Stack of the week
Angellist is the world’s largest startup community where startups can raise money, build their team, and launch their products. Learn what data tools they are using to build their data stack.
Want us to feature your data stack? Add it here.
Good reads and resources
- ELT for the Data Consumer: MDS provides a massive unlock in value for data engineers and analytic engineers but lacks the easy-to-use and intuitive toolkits for the data analyst, citizen data scientist, and knowledge worker that sits closest to the decision. The evolution of ELT was a game-changer. But MDS is still unable to provide answers to the data consumers without the help of data producers. Read this article by Jared Parker where he discussed this gap and possible solutions for it. He talks about what does ELT mean for knowledge user and the need for purpose-built tools to truly bring data consumers into the MDS ecosystem.
- How I Have Set Up a Cost-Effective Modern Data Stack for a Charity: 90% of nonprofit organizations collect data, but about half do not fully exploit it. Most of the time they lack the know-how to leverage this data but also generally lack the financial resources to allocate some budget to it. In this article, Marie Lestavel talks about how she helped a charity known as “La Porte Bleue” by setting up a modern data stack allowing them to automate reporting, and thus unlock grants, but also to leverage their data to get insights from it. She mentioned various MDS tools she used and also discussed why she made that choice.
- How All-in-One Tools Are Accelerating Data Democratization: “In today's borderless digital world where data is the lingua franca, data democratization is no longer a nice-to-have, but a necessity for organizations to achieve their goals and gain a competitive advantage,” said Jonas Thordal in his journal. He discussed the gap between data and commercial teams, the rise of all-in-one tools, and how these tools can benefit both the data and commercial teams.
If you have interesting content that you would like to share with the data community, publish it here.
This week's question: How is the ideal MDS toolset look like for a startup?
You can answer over here
Last week's question: What are the gaps that the modern data stack promised to solve but is still unable to do so?
MDS promised to be smarter, faster, and more modular. It promised to deliver better analytics. However, its yet to solve the problem of integration. Its still not extremely modular, and plug & play of different tools are not possible. Fragmented components in MDS, still make implementation and execution extremely difficult. As a result a lot of non technical smaller companies, fail to generate any value. Organizations still rely on legacy data stack for the ease of integration, setup and support when things go wrong.
-Saikat Dutta, Azure Data Engineer, Tata Consultancy Services
One of the biggest problems that the modern data stack has yet to solve is true end to end data observability and lineage. A new wave of data quality tools show a lot of promise in bridging this gap, but I have yet to see a stack that didn’t require a large amount of effort to guarantee a certain level of data quality and traceability
-Zachary Klein, Machine Learning Engineer, Whatnot
Upcoming data event
- Bigeyedata is organizing a 2-day virtual event " Data Reliability Engineering Conference 2022" on May 25-26, 2022.
Hear from data teams, MDS companies & other speakers working to solve complex data reliability challenges. Register here for the event.
Startup funding news
- Actiondesk raised $3.9 million in a seed round!
This round of funding was led by Tiger Global, Bling Capital, Y combinator, Speed Invest, Funders Club, Liquid2, and others. With this round of funding the total funding of Actiondesk is $4.1 million. Read here.
- Monte Carlo raises $135M in Series D round of funding
The round of led by IVP, with participation from Accel, GGV Capital, Redpoint Ventures, ICONIQ Growth, Salesforce Ventures, and GIC Singapore. With this round, Monte Carlo has raised a total of $236M in a 20-month period. Read here.
- Rivery.io raises $30M in series B round of funding!
This round of funding was led by Tiger Global Management alongside existing investors State Of Mind Ventures and Entrée Capital. This financing follows from last year's A round, bringing the total venture capital raised to date to $48 million. Read the full story here.
- Bolt is hiring a 'Head of Data Analytics'
Location- Berlin, Germany
Data Stack- Redshift, Looker, Airflow, Fivetran, dbt, Databricks
- Synopsys is hiring a 'Data Engineer'
Location- US (Remote)
Data Stack- Snowflake, dbt, Tableau, PowerBI
- VTS is hiring a 'Analytics Engineer'
Location- Toronto; New York; San Francisco
Data Stack- dbt, Snowflake, Looker, Fivetran, Airbyte
Hiring for data team? Let us know and we will add it to the next week's edition.
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