4 min read

MDS Newsletter #38

MDS Newsletter #38


  • Rockset, the real-time analytics platform built for the cloud, delivers fast analytics on real-time data with surprising efficiency. It powers sub-second SQL analytics with the ability to search, aggregate and join any data at scale. Rockset's converged indexing, built-in connectors and cloud-native architecture enable users to scale simply and efficiently. Users iterate faster, building data apps in weeks, not months, regardless of the shape of their data.

    Rockset has raised a total of $61.5M in funding over 3 rounds. Their latest funding was raised on Oct 27, 2020 from a Series B round.
  • data.world is the enterprise data catalog for the modern data stack. It is a cloud-native SaaS platform that combines a consumer-grade user experience with a powerful knowledge graph to deliver enhanced data discovery, agile data governance, and actionable insights.

    Data.world has raised a total of $132.3M in funding over 7 rounds. Their latest funding was raised on Apr 5, 2022 from a Series C round.
  • Fintech companies are really bringing this world closer by providing a streamlined payment solution. Ebury is one of the fintech companies, specializing in international payments, collections, and foreign exchange services. Ebury offers foreign exchange activity in over 130 currencies. Take a look at how fintech companies are organising their data stack, here.

Good reads and resources

  • The data lakes concept in modern data architecture: With the increasing number of applications and processes, the volume of big data has grown exponentially. Higher growth will result in the high complexity of big data, which will reduce the efficiency of organizations that rely heavily on their information assets. Traditional storage systems are not suitable for keeping and processing large volumes of raw data. This is where data lakes enter the picture. Read this article by Sasha Andrieiev, where he explored the concept of data lakes in-depth, explaining its benefits, characteristics, architecture, and use cases.
  • Tips for a tidy data warehouse: Building the right data models in your data warehouse can have a huge impact on how much value you get from your data. It can make the difference between a report or analysis taking a week of analyst time vs a day. When data models are organized and clearly reflect business processes, it makes it easier for analysts to understand and make use of a company’s data. Read this article by Paul Singman where he has discussed the do's and dont's of data modeling.
  • Why I will not build my next data platform myself: Engineers love to build, they love to write their own code which gives them a sense of freedom. These are the reasons why many engineers are in favor of building the next data platform themselves because that’s what engineers love to do. When engineers try to build a data platform from scratch there are number of pitfalls that are overlooked. A mature company understands the difference between can we build our own data platform and should we build it ourselves because it is our core business. In this article Niels Claeys has discussed these pitfalls in detail.

Community Speaks

This week's question: What data engineering practices you will recommend bridging the gap between data producers and data consumers?

You can answer it here.

Last week's question: While building a data platform, how do you approach the build vs. buy problem and quantify the tradeoffs?

The build vs buy purely depend up on the business needs. If the problem faced is common and the solution is readily available which can solve the purpose then there is no need to build in-house as building takes up a lot of resources for both developing and maintaining. Whereas, if the the problem is unique and or need modifications to match the use-case, the build strategy is more preferred. You can refer my go-to guide (https://cdn.windwardstudios.com/Website/WhitePapers/Build-vs-Buy.pdf) that helps make decision whether to buy vs build.
Owais Multani, Senior Software Engineer at Cliff.ai / Greendeck.co

Upcoming data summits and webinars

  • Segment is organising CDP Week Summer Edition from June 21–23, 2022 which is going to be a virtual event.

    Join the event with other industry leaders for CDP Week: Summer Edition to learn the new ways leading companies have been creating outstanding customer experiences with customer data in our evolving pandemic-influenced world. Register here.
  • Data Stack Summit 2022, a virtual event, will be held virtually on June 22nd, 2022 with talks kicking off at 8:30 AM PDT.

    Hear real-world perspectives from long-time enterprise data visionaries, data engineers, data and cloud architects, DataOps and DevOps practitioners as they talk through topics like the building blocks of the modern data platform, open-source considerations, best practices for enterprise data operations, migrations, data observability, and tuning data pipelines for performance at scale. Register here.

MDS Jobs

  • Gopuff is hiring a 'Sr. Manager, Data Engineering'
    Location: Remote, US
    Apply here
    Check Gopuff's data stack here
  • Canva is hiring a 'Head of Analytics Engineering'
    Location: Remote
    Apply here
    Check Canva's data stack here
  • ThredUPis hiring a 'Senior BI Data Engineer'
    Location: Remote
    Apply here
    Data Stack: Looker, Tableau, Redshift

Are you hiring for data roles? Let us know and we will feature it in the next edition.

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If you have any suggestions, want us to feature an article, or list a data engineering job, hit us up! We would love to include it in our next edition😎

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