Business intelligence is often equated with a collection of dashboards that show various charts and graphs representing data for an organization. What is overlooked in that characterization is the level of complexity and effort that are required to collect and present that information, and the opportunities for providing those insights in other contexts. In this episode Telmo Silva explains how he co-founded ClicData to bring full featured business intelligence and reporting to every organization without having to build and maintain that capability on their own. This is a great conversation about the technical and organizational operations involved in building a comprehensive business intelligence system and the current state of the market.
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Have you ever woken up to a crisis because a number on a dashboard is broken and no one knows why? Or sent out frustrating slack messages trying to find the right data set? Or tried to understand what a column name means?
Our friends at Atlan started out as a data team themselves and faced all this collaboration chaos themselves, and started building Atlan as an internal tool for themselves. 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.
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Datafold is a data observability platform that helps companies prevent data catastrophes. It has a unique ability to identify, prioritize and investigate data quality issues proactively before they affect production. Datafold gives you visibility and confidence in the quality of your analytical data with fast dataset diffing, profiling, column-level lineage, and intelligent anomaly detection. Datafold integrates with all major data warehouses as well as frameworks such as Airflow & dbt and seamlessly plugs into CI, so in a few minutes you can get from 0 to automated testing of your analytical code.
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
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- 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.
- 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
- Your host is Tobias Macey and today I’m interviewing Telmo Silva about ClicData,
- How did you get involved in the area of data management?
- Can you describe what ClicData is and the story behind it?
- How would you characterize the current state of the market for business intelligence?
- What are the systems/capabilities that are required to run a full-featured BI system?
- What are the challenges that businesses face in developing in-house capacity for business intelligence?
- Can you describe how the ClicData platform is architected?
- How has it changed or evolved since you first began working on it?
- How are you approaching schema design and evolution in the storage layer?
- How do you handle questions of data security/privacy/regulations given that you are storing the information on behalf of the business?
- In your work with clients what are some of the challenges that businesses are facing when attempting to answer questions and gain insights from their data in a repeatable fashion?
- What are some strategies that you have found useful for structuring schemas or dashboards to make iterative exploration of data effective?
- What are the most interesting, innovative, or unexpected ways that you have seen ClicData used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on ClicData?
- When is ClicData the wrong choice?
- What do you have planned for the future of ClicData?
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
- Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used.
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