MDS Newsletter #33

Ayushi Rathod -

Someone told me that you're not really a newsletter until you post a cat gif.
So let's do this - we are now OFFICIALLY A NEWSLETTER😺

Community Speaks

This week's question: Can someone explain to a 5yo what's the difference between data lake, data warehouse and data lakehouse?

Send your answers here.

Last week's question: How to create a superior data user experience while building a data product?

When thinking of building a data product, there are two thought disciplines coming together.

  1. Product Management - As the name suggests, Data Product, are supposed to be products with the product thinking underlying its build. What that essentially means is that there is a deep customer empathy, value for customers and great Product-Market fit (although at a much different scale and audience).
  2. Software Engineering - Now the hidden truth of data products, they are essentially software constructs which needs to have great value without having a UI/UX to "wow" the customers with. Most of the times, the users of the data products are going to developers and/or product managers. This means that you need to build a product which developers spectacular value with great NFR (Non-Functional Requirements) in Software Engineering parlance. For example, very easy integration capability, easy to test etc.
    To sum up, having an understanding of the end users and their use cases along with high degree of customisability and list of the NFRs ticked off so that it gives the developers/product managers peace of mind.
    Ranju Ramesh, Product @ Atlan

Read more amazing answers by Andy Carlson and Shane Gibson over here.

With so many tools in MDS, managing all of them can become a problem & not every company has the expertise to navigate infrastructure decisions alone, so the need for a solution that is more user-friendly became evident.
Enters -“out-of-the-box data stack”/ Managed Data Stack

The managed data stack is designed to make it quick and easy to set up the data stack your business needs. You receive essential elements of the modern data stack or, in some cases, the entire data stack.

Read the article by Cody Carmen, Marketing Manager at Mozart Data where he has discussed in detail what Managed Data Stack is, how it works, and how is it different from Modern Data stack.

  • Mode is a modern analytics and BI solution that combines SQL, Python, R and visual analysis to answer questions faster. It streamlines analyst workflows with a self-serve experience for business users. This reduces time-to-answers and makes it easy to share actionable data insights and stories across entire organizations.

    Mode Analytics has raised a total of $81M in funding over 8 rounds. Their latest funding was raised on Aug 6, 2020 from a Series D round.
  • Prophecy.io is a low-code data engineering platform. It democratizes the development and deployment of high-quality data pipelines, uniquely combining visual development with agile software engineering best practices. The developed code is open source and is targeted at Apache Spark & Apache Airflow.

    Prophecy.io has raised a total of $38.5M in funding over 5 rounds. Their latest funding was raised on Jan 20, 2022 from a Series A round.

Convoy is a digital freight network solving problems in the $800B trucking industry.  Have a look at Convoy's data stack

Want to add your data stack? Head over here!

Good reads and resources

  • 5 ways to measure the ROI of Data & Analytics (The simple way): Finding the ROI of Data & Analytics is rarely straightforward and requires quite a lot of thinking before reporting numbers with confidence. The problem with measuring Data & Analytics ROI is that it is a supporting function and rarely directly impacts business performance like other activities, such as marketing. In this article, Santiago Tacoronte has explained 5 methods by which the performance of Data and Analytics can be quantified.
  • 20 Questions You Need To Ask About Your Data Analytics Strategy: There are plenty of cliches about data and its likeness to oil or companies being data-driven. But are these cliches worth the hype? The answer is YES. Implementing the right data strategies can help your business gain invaluable insights and create new opportunities for business that you didn’t have before. Read this article Ben Rogojan has put up 20 questions that will help highlight where your team is doing well and where it can improve on its data strategy.
  • To 10x Analytics, Treat It Like a Product: In this article, Cameron Warren has discussed why Analytics, more particularly, Business Intelligence needs to evolve. According to him, Analytics/ Business Intelligence suffers from a serious case of  “chicken-or-egg” syndrome. Which is, the struggle to distinguish outcomes from the output. The solution to the chicken-or-egg analytics problem is: Treat analytics like a product. Read this article to know what steps can be taken by businesses to treat analytics as a product.
  • What does the future of data engineering look like?:  The role of a data engineer was almost nonexistent ten years ago. But, the need for this particular kind of software engineer has grown. As the field got more mature, the role evolved. The responsibilities of a data engineer vary from one company to another and the role does not evolve at the same pace everywhere. Read this article by
    Benoît Goujon, where he shares what trends according to him will shape the future of data engineering.
  • A layered approach to data meaning and measurement w/ Nick Handel: In this episode of Catalog & Cocktails Nick Handel, CEO of Transform, talks about all things semantics and metrics and why enterprises need to be thinking more about more about relationships.

Upcoming events and webinars

  • Economist Impact Events is organizing an in-person/virtual event "The data dividend: reimagining data strategies to deepen insights" on May 17, 2022, in San Francisco, CA.
    Register here
  • Datatechvibe is hosting an in-person summit “ Velocity Data and Analytics Summit” on May 17-18, 2022 in Dubai, UAE.
    Register here

MDS Jobs

  • Maze is hiring a ‘Head of Data'
    Location- Remote
    Data Stack- Fivetran, Segment, Snowflake, dbt
    Apply here
  • yearup is hiring a ‘Senior Analytics Engineer’
    Location- United States
    Data Stack- Salesforce, Fivetran, Snowflake, dbt, Tableau
    Apply here
  • Flock Freight is hiring a ‘Staff Data Engineer’
    Location- San Diego, CA, Chicago, IL, or Remote
    Check out Flock Freight’s data stack here
    Apply here
  • Fluence is hiring a ‘Senior Data Engineer’
    Location- US, Remote
    Data Stack- Snowflake, Postgres, AWS
    Apply here
  • AXS is looking for a ‘Business Intelligence Analyst'
    Location- Los Angeles, CA
    Check out AXS’s data stack here
    Apply here

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