Let Your Business Intelligence Platform Build The Models Automatically With Omni Analytics
Data Engineering Podcast
The Ideal Workflow for a Team Using Omni
I'm curious about some of the ways that you address the tendency for multiple very close but slightly disparate models to grow up. And digging into the workflow for a team using Omni and wondering if you can talk through that.
Summary
Business intelligence has gone through many generational shifts, but each generation has largely maintained the same workflow. Data analysts create reports that are used by the business to understand and direct the business, but the process is very labor and time intensive. The team at Omni have taken a new approach by automatically building models based on the queries that are executed. In this episode Chris Merrick shares how they manage integration and automation around the modeling layer and how it improves the organizational experience of business intelligence.
Announcements
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
- Truly leveraging and benefiting from streaming data is hard - the data stack is costly, difficult to use and still has limitations. Materialize breaks down those barriers with a true cloud-native streaming database - not simply a database that connects to streaming systems. With a PostgreSQL-compatible interface, you can now work with real-time data using ANSI SQL including the ability to perform multi-way complex joins, which support stream-to-stream, stream-to-table, table-to-table, and more, all in standard SQL. Go to dataengineeringpodcast.com/materialize today and sign up for early access to get started. If you like what you see and want to help make it better, they're hiring across all functions!
- Your host is Tobias Macey and today I'm interviewing Chris Merrick about the Omni Analytics platform and how they are adding automatic data modeling to your business intelligence
Interview
- Introduction
- How did you get involved in the area of data management?
- Can you describe what Omni Analytics is and the story behind it?
- What are the core goals that you are trying to achieve with building Omni?
Business intelligence has gone through many evolutions. What are the unique capabilities that Omni Analytics offers over other players in the market?
- What are the technical and organizational anti-patterns that typically grow up around BI systems?
What are the elements that contribute to BI being such a difficult product to use effectively in an organization?
Can you describe how you have implemented the Omni platform?
- How have the design/scope/goals of the product changed since you first started working on it?
What does the workflow for a team using Omni look like?
What are some of the developments in the broader ecosystem that have made your work possible?
What are some of the positive and negative inspirations that you have drawn from the experience that you and your team-mates have gained in previous businesses?
What are the most interesting, innovative, or unexpected ways that you have seen Omni used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on Omni?
When is Omni the wrong choice?
What do you have planned for the future of Omni?
Contact Info
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
- Omni Analytics
- Stitch
- RJ Metrics
- Looker
- Singer
- dbt
- Teradata
- Fivetran
- Apache Arrow
- DuckDB
- BigQuery
- Snowflake
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
Sponsored By:
- Materialize:  Looking for the simplest way to get the freshest data possible to your teams? Because let's face it: if real-time were easy, everyone would be using it. Look no further than Materialize, the streaming database you already know how to use. Materialize’s PostgreSQL-compatible interface lets users leverage the tools they already use, with unsurpassed simplicity enabled by full ANSI SQL support. Delivered as a single platform with the separation of storage and compute, strict-serializability, active replication, horizontal scalability and workload isolation — Materialize is now the fastest way to build products with streaming data, drastically reducing the time, expertise, cost and maintenance traditionally associated with implementation of real-time features. Sign up now for early access to Materialize and get started with the power of streaming data with the same simplicity and low implementation cost as batch cloud data warehouses. Go to [materialize.com](https://materialize.com/register/?utm_source=depodcast&utm_medium=paid&utm_campaign=early-access)
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