A Reflection On Data Observability As It Reaches Broader Adoption
Data Engineering Podcast
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Data Observability - The Biggest Change Ever
The traditional approach to data reliabilityis to add tests various places in your data stack. But monte carlo can actually send on th alert and provide the right tools to help identify the rute cause and debug et in a timely manner before there is impact downstream. The drive for data observability comes from the fact that there is much more outward facing, customer facing consequences to bad data. It's just that the huge complexity of data systems really forces you to really moner things at scale.
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Summary
Data observability is a product category that has seen massive growth and adoption in recent years. Monte Carlo is in the vanguard of companies who have been enabling data teams to observe and understand their complex data systems. In this episode founders Barr Moses and Lior Gavish rejoin the show to reflect on the evolution and adoption of data observability technologies and the capabilities that are being introduced as the broader ecosystem adopts the practices.
Announcements
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
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- The only thing worse than having bad data is not knowing that you have it. With Bigeye’s data observability platform, if there is an issue with your data or data pipelines you’ll know right away and can get it fixed before the business is impacted. Bigeye let’s data teams measure, improve, and communicate the quality of your data to company stakeholders. With complete API access, a user-friendly interface, and automated yet flexible alerting, you’ve got everything you need to establish and maintain trust in your data. Go to dataengineeringpodcast.com/bigeye today to sign up and start trusting your analyses.
- Your host is Tobias Macey and today I’m interviewing Barr Moses and Lior Gavish about the state of the market for data observability and their own work at Monte Carlo
Interview
- Introduction
- How did you get involved in the area of data management?
- Can you give the elevator pitch for Monte Carlo?
- What are the notable changes in the Monte Carlo product and business since our last conversation in October 2020?
- You were one of the early entrants in the market of data quality/data observability products. In your work to gain visibility and traction you invested substantially in content creation (blog posts, presentations, round table conversations, etc.). How would you summarize the focus of your initial efforts?
- Why do you think data observability has really taken off? A few years ago, the category barely existed – what’s changed?
- There’s a larger debate within the data engineering community regarding whether it makes sense to go deep or go broad when it comes to monitoring your data. In other words, do you start with a few important data sets, or do you attempt to cover the entire ecosystem. What is your take?
- For engineers and teams who are just now investigating and investing in observability/quality automation for their data, what are their motivations?
- How has the conversation around the value/motivating factors matured or changed over the past couple of years?
- In what way have the requirements and capabilities of data observability platforms shifted?
- What are the forces in the ecosystem that have driven those changes?
- How has the scope and vision for your work at Monte Carlo evolved as the understanding and impact of data quality have become more widespread?
- In what way have the requirements and capabilities of data observability platforms shifted?
- When teams invest in data quality/observability what are some of the ways that the insights gained influence their other priorities and design choices? (e.g. platform design, pipeline design, data usage, etc.)
- When it comes to selecting what parts of the data stack to invest in, how do data leaders prioritize? For instance, when does it make sense to build or buy a data catalog? A data observability platform?
- The adoption of any tool that adds constraints is a delicate balance. What have you found to be the predominant patterns for teams who are incorporating Monte Carlo? (e.g. maintaining delivery velocity and adding safety/trust)
- A corollary to the goal of data engineers for higher reliability and visibility is the need by the business/team leadership to identify "return on investment". How do you and your customers think about the useful metrics and measurement goals to justify the time spent on "non-functional" requirements?
- What are the most interesting, innovative, or unexpected ways that you have seen Monte Carlo used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on Monte Carlo?
- When is Monte Carlo the wrong choice?
- What do you have planned for the future of Monte Carlo?
Contact Info
- Barr
- @BM_DataDowntime on Twitter
- Lior
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.
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Links
- Monte Carlo
- App Dynamics
- Datadog
- New Relic
- Data Quality Fundamentals book
- State Of Data Quality Survey
- dbt
- Airflow
- Dagster
- Episode: Incident Management For Data Teams
- Databricks Delta
- Patch.tech Snowflake APIs
- Hightouch
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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