We’re improving DataFramed, and we need your help! We want to hear what you have to say about the show, and how we can make it more enjoyable for you—find out more here. We’re often caught chasing the dream of “self-serve” data—a place where data empowers stakeholders to answer their questions without a data expert at every turn. But what does it take to reach that point? How do you shape tools that empower teams to explore and act on data without the usual bottlenecks? And with the growing presence of natural language tools and AI, is true self-service within reach, or is there still more to the journey? Sameer Al-Sakran is the CEO at Metabase, a low-code self-service analytics company. Sameer has a background in both data science and data engineering so he's got a practitioner's perspective as well as executive insight. Previously, he was CTO at Expa and Blackjet, and the founder of SimpleHadoop and Adopilot. In the episode, Richie and Sameer explore self-serve analytics, the evolution of data tools, GenAI vs AI agents, semantic layers, the challenges of implementing self-serve analytics, the problem with data-driven culture, encouraging efficiency in data teams, the parallels between UX and data projects, exciting trends in analytics, and much more. Links Mentioned in the Show: MetabaseConnect with SameerArticles from Metabase on jargon, information budgets, analytics mistakes, and data model mistakesCourse: Introduction to Data CultureRelated Episode: Towards Self-Service Data Engineering with Taylor Brown, Co-Founder and COO at FivetranRewatch Sessions from RADAR: Forward Edition New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

Summary The reason for collecting, cleaning, and organizing data is to make it usable by the organization. One of the most common and widely used methods of access is through a business intelligence dashboard. Superset is an open source option that has been gaining popularity due to its flexibility and extensible feature set. In this episode Maxime Beauchemin discusses how data engineers can use Superset to provide self service access to data and deliver analytics. He digs into how it integrates with your data stack, how you can extend it to fit your use case, and why open source systems are a good choice for your business intelligence. If you haven’t already tried out Superset then this conversation is well worth your time. Give it a listen and then take it for a test drive today.

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! 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. Once you sign up and create an alert in Datafold for your company data, they will send you a cool water flask. RudderStack’s smart customer data pipeline is warehouse-first. It builds your customer data warehouse and your identity graph on your data warehouse, with support for Snowflake, Google BigQuery, Amazon Redshift, and more. Their SDKs and plugins make event streaming easy, and their integrations with cloud applications like Salesforce and ZenDesk help you go beyond event streaming. With RudderStack you can use all of your customer data to answer more difficult questions and then send those insights to your whole customer data stack. Sign up free at dataengineeringpodcast.com/rudder today. Your host is Tobias Macey and today I’m interviewing Max Beauchemin about Superset, an open source platform for data exploration, dashboards, and business intelligence

Interview

Introduction How did you get involved in the area of data management? Can you start by describing what Superset is? Superset is becoming part of the reference architecture for a modern data stack. What are the factors that have contributed to its popularity over other tools such as Redash, Metabase, Looker, etc.? Where do dashboarding and exploration tools like Superset fit in the responsibilities and workflow of a data engineer? What are some of the challenges that Superset faces in being performant when working with large data sources?

Which data sources have you found to be the most challenging to work with?

What are some anti-patterns that users of Superset mig

Summary

Business Intelligence software is often cumbersome and requires specialized knowledge of the tools and data to be able to ask and answer questions about the state of the organization. Metabase is a tool built with the goal of making the act of discovering information and asking questions of an organizations data easy and self-service for non-technical users. In this episode the CEO of Metabase, Sameer Al-Sakran, discusses how and why the project got started, the ways that it can be used to build and share useful reports, some of the useful features planned for future releases, and how to get it set up to start using it in your environment.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. For complete visibility into the health of your pipeline, including deployment tracking, and powerful alerting driven by machine-learning, DataDog has got you covered. With their monitoring, metrics, and log collection agent, including extensive integrations and distributed tracing, you’ll have everything you need to find and fix performance bottlenecks in no time. Go to dataengineeringpodcast.com/datadog today to start your free 14 day trial and get a sweet new T-Shirt. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch. Your host is Tobias Macey and today I’m interviewing Sameer Al-Sakran about Metabase, a free and open source tool for self service business intelligence

Interview

Introduction How did you get involved in the area of data management? The current goal for most companies is to be “data driven”. How would you define that concept?

How does Metabase assist in that endeavor?

What is the ratio of users that take advantage of the GUI query builder as opposed to writing raw SQL?

What level of complexity is possible with the query builder?

What have you found to be the typical use cases for Metabase in the context of an organization? How do you manage scaling for large or complex queries? What was the motivation for using Clojure as the language for implementing Metabase? What is involved in adding support for a new data source? What are the differentiating features of Metabase that would lead someone to choose it for their organization? What have been the most challenging aspects of building and growing Metabase, both from a technical and business perspective? What do you have planned for the future of Metabase?

Contact Info

Sameer

salsakran on GitHub @sameer_alsakran on Twitter LinkedIn

Metabase

Website @metabase on Twitter metabase on GitHub

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

Expa Metabase Blackjet Hadoop Imeem Maslow’s Hierarchy of Data Needs 2 Sided Marketplace Honeycomb Interview Excel Tableau Go-JEK Clojure React Python Scala JVM Redash How To Lie With Data Stripe Braintree Payments

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast