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ABOUT THE TALK: Modern data stacks focus on the most common use-cases and dashboards, but what about all the ad-hoc requests that come? The current tool set fails to allow data analysts to iterate easily with stakeholders. In this talk, we will discuss that without an ad-hoc layer, data analysts are left to answer questions with hacky live SQL or have every request go through the resource-intensive and expensive production processes and workflows.

An ad-hoc layer solves this by allowing data analysts to answer data questions, change their mind, and deliver data dumps or simple analyses incredibly fast and reliably. Allowing them to prioritize putting it into production only if it needs to be reused.

ABOUT THE SPEAKER: Ahmed Elsamadisi is the founder and CEO of Narrator. Narrator enables companies to make better decisions by providing them with the ability to answer any question in under 10 minutes. Ahmed started his career building algorithms for self-driving cars and human-robot interaction. He then joined Raytheon to develop AI algorithms for missile defense, focusing on tracking and discrimination. In 2015, Ahmed joined WeWork as the first hire on their data team. He built their data engineering infrastructure and grew the team of data engineers and analysts.

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Summary The perennial question of data warehousing is how to model the information that you are storing. This has given rise to methods as varied as star and snowflake schemas, data vault modeling, and wide tables. The challenge with many of those approaches is that they are optimized for answering known questions but brittle and cumbersome when exploring unknowns. In this episode Ahmed Elsamadisi shares his journey to find a more flexible and universal data model in the form of the "activity schema" that is powering the Narrator platform, and how it has allowed his customers to perform self-service exploration of their business domains without being blocked by schema evolution in the data warehouse. This is a fascinating exploration of what can be done when you challenge your assumptions about what is possible.

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 managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. By acting as a virtual hub for data assets ranging from tables and dashboards to SQL snippets & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Snowflake, Slack, Looker and more. Go to dataengineeringpodcast.com/atlan today and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription Modern Data teams are dealing with a lot of complexity in their data pipelines and analytical code. Monitoring data quality, tracing incidents, and testing changes can be daunting and often takes hours to days. Datafold helps Data teams gain visibility and confidence in the quality of their analytical data through data profiling, column-level lineage and intelligent anomaly detection. Datafold also helps automate regression testing of ETL code with its Data Diff feature that instantly shows how a change in ETL or BI code affects the produced data, both on a statistical level and down to individual rows and values. Datafold integrates with all major data warehouses as well as frameworks such as Airflow & dbt and seamlessly plugs into CI workflows. Go to dataengineeringpodcast.com/datafold today to start a 30-day trial of Datafold. Your host is Tobias Macey and today I’m interviewing Ahmed Elsamadisi about Narrator, a platform to enable anyone to go from question to data-driven decision in minutes

Interview

Introduction How did you get involved in the area of data management? Can you describe what Narrator is and the story behind it? What are the challenges that you have seen organizations encounter when attempting to make analytics a self-serve capability? What are the use cases that you are focused on? How does Narrator fit within the data workflows of an organization? How is the Narrator platform implemented?

How has the design and focus of the technology evolved since you first started working on Narrator?

The core element of the analyses that you are building is the "activity schema". Can you describe the design process that led you to that format?

What are the challenges that are posed by more widely used modeling techniques such as star/s

Send us a text Want to be featured as a guest on Making Data Simple? Reach out to us at [[email protected]] and tell us why you should be next.

Abstract Hosted by Al Martin, VP, IBM Expert Services Delivery, Making Data Simple provides the latest thinking on big data, A.I., and the implications for the enterprise from a range of experts.

This week on Making Data Simple, we have Ahmed Elsamadisi. Ahmed started his career at Cornell’s Autonomous Systems Laboratory focusing on human-robot interaction and Bayesian data fusion as well as building algorithms for autonomous cars. He then joined Raytheon to develop tactical AI algorithms for missile defense, four of which are still in use today by the US Military. Eventually he moved on to Raytheon’s Advanced Technology division to focus on building human exoskeletons--like the Iron Man suit but made of rubber because it’s way more energy efficient and not a fictional concept ignoring proper scientific practices-- and algorithms for adaptive decision making. In 2015 Ahmed joined We Work, and over the next two years Ahmed built We Work’s standard data infrastructure and grew its data team from one to forty Data Engineers and Data Analysts. After implementing a single time series table data model at We Work and seeing the immediate results, Ahmed wanted to figure out a way to bring this new found knowledge to the world. Ahmed founded Narrator to allow startups to leverage this new approach, ask questions, understand customer behavior, and analyze data across all their systems from a simple Universal Data Model.  Show Notes 3:26 – Tell us about algorithms for autonomous cars 6:26 – Anything you can say around missile defense? 8:58 – Tell us about human exoskeletons 13:18 – What kind of data were you using to make decisions around the Iron Man suit? 16:02 – What did you learn at We Work? 25:00 – How we answer a question in the world of Narrator 32:24 – Does Narrator sit between the application and the database? 33:28 – Walk us through a Use Case Website: Narrator AI.com Books:  The Power of Bad Never Split The Difference Ahmed Elsamadisi - LinkedIn    Connect with the Team Producer Kate Brown - LinkedIn. Producer Steve Templeton - LinkedIn. Host Al Martin - LinkedIn and Twitter.  Want to be featured as a guest on Making Data Simple? Reach out to us at [email protected] and tell us why you should be next. The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun.