Building A Shared Understanding Of Data Assets In A Business Through A Single Pane Of Glass With Workstream
Data Engineering Podcast
00:00
Using Workstream to Unify Data Assets
In rolling it out, we work closely with customers in their self service, and go clear round the product on your own. We normally tell customers to start simple, start small. And ten counto any of those others throughout the organization conjoin that work space and access what you've crurated for them. So there's some training that's that's involved, just getting the initial empoyment up to speed. But once you're up an running, it's pretty seem les for others to get into the comian access.
Transcript
Play full episode
Transcript
Episode notes
Summary
There is a constant tension in business data between growing siloes, and breaking them down. Even when a tool is designed to integrate information as a guard against data isolation, it can easily become a silo of its own, where you have to make a point of using it to seek out information. In order to help distribute critical context about data assets and their status into the locations where work is being done Nicholas Freund co-founded Workstream. In this episode he discusses the challenge of maintaining shared visibility and understanding of data work across the various stakeholders and his efforts to make it a seamless experience.
Announcements
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
- When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their new managed database service you can launch a production ready MySQL, Postgres, or MongoDB cluster in minutes, with automated backups, 40 Gbps connections from your application hosts, and high throughput SSDs. Go to dataengineeringpodcast.com/linode today and get a $100 credit to launch a database, create a Kubernetes cluster, or take advantage of all of their other services. And don’t forget to thank them for their continued support of this show!
- Atlan is the metadata hub for your data ecosystem. Instead of locking your metadata into a new silo, unleash its transformative potential with Atlan’s active metadata capabilities. Push information about data freshness and quality to your business intelligence, automatically scale up and down your warehouse based on usage patterns, and let the bots answer those questions in Slack so that the humans can focus on delivering real value. Go to dataengineeringpodcast.com/atlan today to learn more about how Atlan’s active metadata platform is helping pioneering data teams like Postman, Plaid, WeWork & Unilever achieve extraordinary things with metadata and escape the chaos.
- Prefect is the modern Dataflow Automation platform for the modern data stack, empowering data practitioners to build, run and monitor robust pipelines at scale. Guided by the principle that the orchestrator shouldn’t get in your way, Prefect is the only tool of its kind to offer the flexibility to write code as workflows. Prefect specializes in glueing together the disparate pieces of a pipeline, and integrating with modern distributed compute libraries to bring power where you need it, when you need it. Trusted by thousands of organizations and supported by over 20,000 community members, Prefect powers over 100MM business critical tasks a month. For more information on Prefect, visit dataengineeringpodcast.com/prefect.
- Data engineers don’t enjoy writing, maintaining, and modifying ETL pipelines all day, every day. Especially once they realize 90% of all major data sources like Google Analytics, Salesforce, Adwords, Facebook, Spreadsheets, etc., are already available as plug-and-play connectors with reliable, intuitive SaaS solutions. Hevo Data is a highly reliable and intuitive data pipeline platform used by data engineers from 40+ countries to set up and run low-latency ELT pipelines with zero maintenance. Boasting more than 150 out-of-the-box connectors that can be set up in minutes, Hevo also allows you to monitor and control your pipelines. You get: real-time data flow visibility, fail-safe mechanisms, and alerts if anything breaks; preload transformations and auto-schema mapping precisely control how data lands in your destination; models and workflows to transform data for analytics; and reverse-ETL capability to move the transformed data back to your business software to inspire timely action. All of this, plus its transparent pricing and 24*7 live support, makes it consistently voted by users as the Leader in the Data Pipeline category on review platforms like G2. Go to dataengineeringpodcast.com/hevodata and sign up for a free 14-day trial that also comes with 24×7 support.
- Your host is Tobias Macey and today I’m interviewing Nicholas Freund about Workstream, a platform aimed at providing a single pane of glass for analytics in your organization
Interview
- Introduction
- How did you get involved in the area of data management?
- Can you describe what Workstream is and the story behind it?
- What is the core problem that you are trying to solve at Workstream?
- How does that problem manifest for the different stakeholders in an organization?
- What are the contributing factors that lead to fragmentation of visibility for data workflows at different stages?
- What are the sources of information that you use to build a cohesive view of an organization’s data assets?
- What are the lifecycle stages of a data asset that are most often overlooked or un-maintained?
- What are the risks and challenges associated with retirement of a data asset?
- Can you describe how Workstream is implemented?
- How have the design and goals of the system changed since you first started it?
- What does the day-to-day interaction with workstream look like for different roles in a company?
- What are the long-range impacts on team behaviors/productivity/capacity that you hope to catalyze?
- What are the most interesting, innovative, or unexpected ways that you have seen Workstream used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on Workstream?
- When is Workstream the wrong choice?
- What do you have planned for the future of Workstream?
Contact Info
- @nickfreund on Twitter
Parting Question
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
Closing Announcements
- Thank you for listening! Don’t forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning.
- Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
- If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com) with your story.
- To help other people find the show please leave a review on Apple Podcasts and tell your friends and co-workers
Links
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
Remember Everything You Learn from Podcasts
Save insights instantly, chat with episodes, and build lasting knowledge - all powered by AI.
