How to Achieve More Trustworthy Data Pipelines with the Prefect Integration for Monte Carlo

As more companies across industries make significant investments in data-driven decision-making, broken workflows and buggy pipelines can cause frustration, exhaust resources, and erode organizational trust in data. 

Data pipelines and workflows can break for a multitude of reasons, and bad data spares no one. As teams increasingly add new data sources to their ecosystem, so do opportunities for data downtime (defined as periods of time when data is missing, inaccurate, or otherwise erroneous). When one data source changes in an unexpected way, the data flowing through the organization’s broader data ecosystem can be compromised. 

The same phenomenon occurs when pipelines become more complex, adding layers of processing and dependencies as data moves between more tools within the data stack. And as data teams grow larger and more specialized, data silos can crop up, and lack of communication or unanticipated changes can cause complex systems to break. 

Altogether, the impact of data downtime costs teams nearly 50 percent of their valuable time and can lead to millions of dollars in wasted revenue

The bad news? These root causes of data downtime aren’t going away. As companies continue to invest in data, the number of data sources will continue to grow, pipelines will become increasingly complex, and data teams will scale in size and specialization. 

The good news? Monte Carlo’s new Premier partnership with dataflow automation platform Prefect gives teams the ability to automate complex workflows, monitor for data issues, and resolve incidents faster—saving time while ensuring data reliability across the entire data engineering lifecycle. 

How Monte Carlo and Prefect empower data teams to monitor complex workflows

Monte Carlo’s end-to-end field-level lineage gives data teams the ability to understand the effect of upstream changes in the warehouse, lake, or ETL on downstream dependencies in the BI layer. Image courtesy of Monte Carlo.

The Prefect platform automates data workflows for teams that manage complex pipelines. Scheduling, infrastructure, error handling, retries, logs, triggers, data serialization, caching, and more are handled by Prefect, reducing headaches and saving valuable engineering time. Prefect’s open source and SaaS offerings make it simple for engineers to build, test, run and monitor powerful dataflows. 

“Prefect’s mission is to eliminate negative engineering by providing a platform that automatically detects and handles pipeline task failures so data engineers don’t have to,” said Jeremiah Lowin, founder and CEO, Prefect. “With our Monte Carlo partnership, our users can now add another layer of failure detection in their dataflows: the data itself. This capability will not only save our users lots of time and headache, but also ensure that data teams can deliver the accurate, reliable data business users need to make informed decisions.”

Monte Carlo delivers monitoring and alerting for anomalies according to both custom rules and automated parameters generated through machine learning. Teams also use Monte Carlo to map data lineage from ingestion to analytics, making upstream and downstream dependencies visible and easily accessible for faster, centralized incident management when data issues do occur. This end-to-end data observability ensures that data can be trusted at every stage of the data lifecycle. 

“As systems become increasingly complex and companies ingest more and more data, the opportunity for data downtime only grows, costing organizations valuable time and resources that could otherwise be spent innovating. Monte Carlo and Prefect’s integration and partnership ensures that data teams avoid these operational issues and achieve unprecedented control and visibility into the health of their data, at each stage of the pipeline,” said Barr Moses, CEO and Co-Founder of Monte Carlo. “We couldn’t be more excited to join forces on this shared vision for data trust and reliability across the modern data stack.” 

With this new integration, mutual customers will be able to seamlessly manage the reliability of their data engineering workflows. This means organizations can gain confidence that the insights delivered by their data ecosystem are trustworthy and reliable, enabling even more robust data-driven decision-making.

What our customers have to say

Auto insurance provider Clearcover uses both Prefect and Monte Carlo to ensure their machine learning-driven platform leverages reliable data to drive smarter insurance choices for their customers. 

“With Monte Carlo’s ML-powered data observability and Prefect Cloud’s dataflow automation platform, our data team can effortlessly build and maintain more reliable data pipelines. This new partnership gives our data engineers end-to-end control and visibility into the health of our data at each stage of its lifecycle, from ingestion in the warehouse to ETL and analytics,” said Braun Reyes, Senior Manager, Data Engineering, Clearcover. “This integration will help us further automate data operations in a sustainable and scalable way as our stack grows to better support the needs of our users.”

Want to learn more about how data observability can help you trust your data? Reach out to Monte Carlo or check out the Data Downtime Blog.