The core mission of data engineers is to provide the business with a way to ask and answer questions of their data. This often takes the form of business intelligence dashboards, machine learning models, or APIs on top of a cleaned and curated data set. Despite the rapid progression of impressive tools and products built to fulfill this mission, it is still an uphill battle to tie everything together into a cohesive and reliable platform. At Isima they decided to reimagine the entire ecosystem from the ground up and built a single unified platform to allow end-to-end self service workflows from data ingestion through to analysis. In this episode CEO and co-founder of Isima Darshan Rawal explains how the biOS platform is architected to enable ease of use, the challenges that were involved in building an entirely new system from scratch, and how it can integrate with the rest of your data platform to allow for incremental adoption. This was an interesting and contrarian take on the current state of the data management industry and is worth a listen to gain some additional perspective.
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- Hello and welcome to the Data Engineering Podcast, the show about modern data management
- What are the pieces of advice that you wish you had received early in your career of data engineering? If you hand a book to a new data engineer, what wisdom would you add to it? I’m working with O’Reilly on a project to collect the 97 things that every data engineer should know, and I need your help. Go to dataengineeringpodcast.com/97things to add your voice and share your hard-earned expertise.
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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. Follow go.datafold.com/dataengineeringpodcast 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.
- Are you bogged down by having to manually manage data access controls, repeatedly move and copy data, and create audit reports to prove compliance? How much time could you save if those tasks were automated across your cloud platforms? Immuta is an automated data governance solution that enables safe and easy data analytics in the cloud. Our comprehensive data-level security, auditing and de-identification features eliminate the need for time-consuming manual processes and our focus on data and compliance team collaboration empowers you to deliver quick and valuable data analytics on the most sensitive data to unlock the full potential of your cloud data platforms. Learn how we streamline and accelerate manual processes to help you derive real results from your data at dataengineeringpodcast.com/immuta.
- Your host is Tobias Macey and today I’m interviewing Darshan Rawal about Îsíma, a unified platform for building data applications
- How did you get involved in the area of data management?
- Can you start by giving an overview of what you are building at Îsíma?
- What was your motivation for creating a new platform for data applications?
- What is the story behind the name?
- What are the tradeoffs of a fully integrated platform vs a modular approach?
- What components of the data ecosystem does Isima replace, and which does it integrate with?
- What are the use cases that Isima enables which were previously impractical?
- Can you describe how Isima is architected?
- How has the design of the platform changed or evolved since you first began working on it?
- What were your initial ideas or assumptions that have been changed or invalidated as you worked through the problem you’re addressing?
- With a focus on the enterprise, how did you approach the user experience design to allow for organizational complexity?
- One of the biggest areas of difficulty that many data systems face is security and scaleable access control. How do you tackle that problem in your platform?
- How did you address the issue of geographical distribution of data and users?
- Can you talk through the overall lifecycle of data as it traverses the bi(OS) platform from ingestion through to presentation?
- What is the workflow for someone using bi(OS)?
- What are some of the most interesting, innovative, or unexpected ways that you have seen bi(OS) used?
- What have you found to be the most interesting, unexpected, or challenging aspects of building the bi(OS) platform?
- When is it the wrong choice?
- What do you have planned for the future of Isima and bi(OS)?
- 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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- ESB == Enterprise Service Bus
- ETL == Extract, Transform, Load
- EDW == Enterprise Data Warehouse
- BI == Business Intelligence