Looking up to data practitioners is the most underrated way to improve your data game. We always talk about top data tools, companies, funding, great resources, and what not. But what I find more amazing is the person behind it. Let’s take a moment to appreciate the amazing people in data.
On that note, who are your favorite data practitioners ? Let me know!
Featured Tools of the week
- Shipyard is an orchestration platform for data engineers to build a solid data infrastructure from the get-go by connecting data tools and processes and streamlining data workflows.
Shipyard offers low-code templates that are configured using a visual interface, replacing the need to write code to build data workflows while enabling data engineers to get their work into production faster.
- Sigma is a cloud analytics solution with a spreadsheet-like interface that enables anyone to explore data at cloud scale and speed. Discover what happened, why it happened, and what will happen.
Sigma Computing has raised a total of $381.3M in funding over 6 rounds. Their latest funding was raised on Dec 16, 2021 from a Series C round.
Featured Data Stack of the week
OneFootball’s goal is to fuel the world’s football obsession. Reaching over 100m football fans worldwide every month, OneFootball is the most popular football media platform for the new generation of football fans. Learn what tools they are using to reach football fans.
Good reads and resources
- What Is Active Metadata, and Why Does It Matter? : “As metadata becomes big data and big data becomes a behemoth, active metadata isn’t just a wonderful dream. It’s a necessity — the only way to understand today’s data.” said Prukalpa in her latest blog. If Metadata sits passively in its own little world, with no one seeing or sharing it, does it even matter? But if it actively moves to the places where people already are, it becomes part of and adds context to a larger conversation. Active metadata functions as a layer on top of the modern data stack. Read this article to know why active metadata matters, its characteristics and use cases, and the future ahead.
- The Unbundling of SaaS Analytics: Most data warehouses are fed with data that are collected by all-in-one-box SaaS analytics tools. There is a big difference between what data teams want their raw data to look like and what it actually looks like when it comes from SaaS analytics tools. In practice, this often leads to discussions about why numbers of different teams don’t seem to be adding up, as the data team and data consumers both use different versions of the same source of data as input. In this article, Vincent Hoogsteder talks about the need to unbundle the SaaS analytics tools and shift from vendor-locked tools to raw data and code.
- How to Make Better Decisions Together with Collaborative Analytics: The ways we share insights and make decisions together haven’t evolved over the last decade. Too many companies spend too long collecting, preparing, and reporting on data to improve. In this article, Ryan Buick break down some of the problems in data collaboration and offered advice on how you can create a better environment for creating, sharing, and measuring data together. He considers data warehouse and collaborative workspace as some of the key components to give employees access to real-time data to make everyday decisions.
If you have interesting content that you would like to share with the data community, publish it here.
This week's question: How would dashboards 2.0 look like? (they are far away from being dead)
You can answer over here
Last week's questions: How is the ideal MDS toolset look like for a startup?
"I think a startup should focus on validating their product-market fit quickly as well as economically. In other words, fail fast and fail cheap. For the first few rounds of iterations, the data platform should not be set in stone. Instead, startup should resort to a tech stack that is more friendlier and cost-effective for them in the long term. Modern cloud-native managed SaaS products reduces the burden of infrastructure and accelerates the time to market and time to insights.Always start with a data warehouse. Cloud-native warehouses BigQuery, Snowflake, Redshift, and Databricks are prominent vendors in the space. Then again, use any hosted ETL/ELT solutions including but not limited Fivetran or Airbyte to bring operational data into the warehouse....."
To read the full answer click here
By Dunith Dhanushka, Developer Advocate at StarTree
Upcoming data events, summits and webinars
- IDC is organising Data & Intelligence Summit from June 7-9, 2022 to be held in Portugal.
The IDC European Data & Intelligence Summit 2022 will provide essential guidance on:
- Navigating the Business Crosswinds with the Data-Driven Enterprise
- Getting to Grips with Operational Intelligence
- Optimal Customer Experience Through Intelligent Technologies
- Towards an Enterprise-Wide Data Culture
Register for the event here.
Startup funding news
- Manta raises $35M in series B round of funding!
This round of funding was led by Forestay Capital, with participation from Bessemer VP, SAP, Senovo VC, Credo Ventures, Dan Fougere, and European Bank for Reconstruction and Development. Read the full story here.
- Catch.com.au is hiring a “Head of Data and Analytics”
Check out Catch Data Stack here
- Gitlab is hiring a “Senior Data Engineer, Big Data”
Data Stack- dbt, Snowflake, Airflow, Fivetran, Stitch
- Ritmo is hiring a “Analytics Engineer”
Data Stack- dbt, Snowflake, Metabase, AWS
Want us to feature data jobs? Ping us up
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