Stale Data Explained: Why It Kills Data-Driven Organizations
Dashboards that don’t refresh, machine learning applications that don’t learn, and other consequences of stale data.
How Best Egg Implemented a Reliable Data Mesh with Data Observability
See how the fast growing fintech marketplace has matured their data stack and driven increased levels of data quality, trust,…
How BlaBlaCar Built a Practical Data Mesh to Support Self-Service Analytics at Scale
See how BlaBlaCar reduced incidents and time to insights by enabling self service analytics and implementing data mesh.
Rise of the MLOps Engineer And 4 Critical ML Model Monitoring Techniques
MLOps engineers are automating ML model monitoring to quickly detect problems like pipeline issues, model drift, feature drift and more.
How Data Enablement Drives Sustainable Value at Upside
Upside leverages a model that emphasizes upfront investments in data enablement to create self-sustaining “data gardens.” Here’s how.
How Mercari Operationalizes Data Reliability Engineering at Scale
6 best practices from Mercari’s data reliability engineering team for ensuring high quality data..
5 Ways to Use Column Level Data Lineage
Dive deep into use cases and explore the connections between column and table-level lineage. Read on to master the art…
Introducing Table Health Dashboard, a Better Way to Track Data Quality Coverage at Scale
Monte Carlo’s Table Health Dashboard gives data teams visibility into the reliability and monitoring coverage of their most critical data…
Modern Data Quality Management: A Proven 6 Step Guide
This 6 step data quality management framework has helped hundreds of organizations achieve higher quality data across their modern data…
How Checkout.com Achieves Data Reliability at Scale with Monte Carlo
Learn how Checkout.com gained visibility into data across domains, scaled data quality checks, and achieved reliability at scale.
IMPACT 2022: The Data Observability Summit Videos Are Now Available On Demand
Missed IMPACT? Don't worry! All our 2022 sessions—including keynotes with Nate Silver, Jay Kreps, and more—are now available on demand.
The 31 Flavors of Data Lineage And Why Vanilla Doesn’t Cut It
4 critical reasons why your data observability solution needs to have data lineage.
How PepsiCo Achieved Data Quality at Scale with Monte Carlo
Learn how the data team at PepsiCo uses data observability through Monte Carlo to discover data incidents faster.
How Blend Scales the Impact of Reliable Data with dbt Cloud and Monte Carlo
Discover how Blend’s data team leverages Monte Carlo and dbt Cloud to reduce compute costs and deliver more reliable data…
Freshly’s Journey to Building Their 5-Layer Data Platform Architecture
How Freshly, a leading meal delivery service, built a more reliable data platform architecture with Snowflake, Fivetran, dbt, Looker, and…
Find and Solve Databricks Data Quality Issues with Monte Carlo
Monte Carlo “Sample Rows” and “Reproduce Anomalies” functionality gives the ability to sample impacted rows of an incident and reproduce…
How Collaborative Imaging Delivers Healthier Data Products with Monte Carlo
In healthcare, bad data can have severe implications. Here's how Collaborative Imaging uses Monte Carlo to drive data health at…
Our Top 5 Most Popular Data Engineering Articles In 2022
Data mesh, data observability, data contracts, data platforms and our other most popular data engineering articles.
Barr Moses: My Top 5 Articles of 2022
Covering 2023 predictions, data self-service, KPIs, big data egos, underestimating data issues and other issues that are top of mind…
Using Data Observability For Third-Party Data Validation
Third-party data validation and ingestion at scale is not easy. Here is one way to solve this challenge.
From Concept to Reality: Migrating to Data Mesh at BairesDev with Databricks and Monte Carlo
Migrating to data mesh? Learn how BairesDev, a leading Brazilian software development company, got started on this epic data journey.
How ELT Schedules Can Improve Root Cause Analysis For Data Engineers
Why Bayesian networks hold more promise segmentation analysis.
How BlaBlaCar Reduced Data Incident Time to Resolution by 100+ Hours Per Quarter with Monte Carlo
As part of their data mesh migration, the carpooling company’s data engineering team unlocked unprecedented levels of productivity through decentralization,…
How To Implement Data Mesh: Top Tips From 4 Data Leaders
Four data leaders from leading organizations give their practical advice on how to implement data mesh.
How SeatGeek Reduced Data Incidents to Zero with Data Observability
In this video, SeatGeek's Brian London and Kyle Shannon share how data observability helped their data team reduce data incidents…
Announcing Monte Carlo’s Data Reliability Dashboard, a Better Way Understand the Health of Your Data
Data Reliability Dashboard gives data engineers the tools necessary to measure data uptime, drive operational improvements, and scale reliability.
5 Steps To A Successful Data Warehouse Migration
Real lessons from recent data warehouse migrations like Qubole to AWS EMR andMySQL to AWS Redshift.
Monitoring for the dbt Semantic Layer and Beyond
Let’s talk about the dbt Semantic Layer as well as anomaly detection, resolution, and prevention across the data most important…
Why Data Cleaning is Failing Your ML Models – And What To Do About It
When it comes to achieving model accuracy, data cleaning alone is insufficient. Here’s why.
The Significance of O’Reilly’s Data Quality Fundamentals
O'Reilly Data Quality Fundamentals' is the publishing house’s first-ever book on data observability.
How Dr. Squatch Keeps Data Clean & Fresh with Monte Carlo
Data observability helps the groundbreaking men’s personal care product company maintain excellent data hygiene.
Big Data (Quality), Small Data Team: How Prefect Saved 20 Hours Per Week with Data Observability
Learn how Dylan Hughes and Prefect’s lean data team kept data reliability high and costs low with Monte Carlo.
How to Make Data Anomaly Resolution Less Cartoonish
Fixing broken data doesn’t have to be a game of whack-a-mole. Here’s how to speed up your data incident resolution…
New Feature Recap: Data Lakehouse Support, Anomalous Row Distribution Monitors, and More!
Highlighting Monte Carlo's latest product releases, including data lakehouse support, and anomalous row distribution monitors.
You Can’t Out-Architect Bad Data￼
Even with the most well-designed data platforms, systems will break. Without some measure of observability, you’re playing with fire.
Data Quality Monitoring – You’re Doing It Wrong
Monitoring just your “important” data only gets you so far. Here’s a better approach.
5 Steps to Operationalizing Data Observability with Monte Carlo￼
Driving early value with your new data observability platform doesn't have to be difficult. We share 5 tips for driving…
A Data Engineer’s Guide to Building Reliable Systems
Over the years, I’ve helped companies of all sizes build and maintain data systems—from my days as a data engineer…
The Future of Big Data Analytics & Data Science: 5 Trends of Tomorrow
What does the future of big data analytics hold? Will our analytical tools scale fast enough to provide real business…
Data Observability First, Data Catalog Second. Here’s Why.
You can’t realize the full value of a data catalog without observability. Here’s why.
How To Create Data Trust Within Your Organization
How to build data trust by preventing data incidents before they happen with the data uptime metric.
Data Engineers Spend Two Days Per Week Firefighting Bad Data, Data Quality Survey Says
Check out the results from our 2022 data quality survey and benchmark your data quality practices against 300 of your…
Data Contracts and 4 Other Ways to Overcome Schema Changes
There are virtually an unlimited number of ways data can break. It could be a bad JOIN statement, an untriggered…
Snowflake Data Mesh: Ensure Reliable Data with Data Observability
Here’s how Snowflake and Monte Carlo are working together to help data teams realize the potential of the data mesh…
Monte Carlo Achieves Snowflake Premier Partner Status to Help Companies Accelerate the Adoption of Reliable Data
With over 70 mutual customers, Monte Carlo becomes the first data observability provider to achieve Snowflake Premier Partner status.
Snowflake Observability and 4 Reasons Data Teams Should Invest In It
Snowflake is a gamechanger for your data strategy. With the right approach to Snowflake observability, you can unlock its full…
Building An External Data Product Is Different. Trust Me. (but read this anyway)
Developing an external data product is different, and let's face it harder, than serving internal customers. We dive into 5…
Building Spark Lineage For Data Lakes
Spark lineage has been a blindspot for the data engineering industry so we set off to engineer a solution. Here's…
How Monte Carlo and Snowflake Gave Vimeo a “Get Out Of Jail Free” Card For Data Fire Drills
See how Snowflake and Monte Carlo helped Vimeo achieve world-class data reliability on a massive scale.
The Ultimate Guide To Data Lineage
Data lineage is a must-have feature of the modern data stack, yet we're struggling to derive value from it. Here's…
DataOps Explained: How To Not Screw It Up
DataOps merges data engineering and data science teams to support an organization’s data needs, in a similar way to how…
5 Ways to Improve Data Quality with the New Monte Carlo Data Quality Trends Dashboard
The new Monte Carlo Dashboard incorporates data and visualization to provide actionable insights to users across data teams.
You Have More Data Quality Issues Than You Think
On average, companies experience one data issue for every 15 tables in their warehouse. Here are 8 reasons why and…
The Cost of Bad Data Has Gone Up. Here Are 8 Reasons Why.
The rising cost of bad data and poor data quality has nothing to do with inflation and everything to do…
Data Observability Doesn’t Just Create Savings – It Drives Revenue, Too
If you think the benefits of data observability stop at cost cutting or avoiding bad outcomes, you’re only looking at…
Treat Your Data Like An Engineering Problem: An Interview with Snowflake Director of Product Management Chris Child
Snowflake Director of Product Management Chris Child talks about the role of data observability solutions in the modern data stack…
What is Data Observability? 5 Key Pillars To Know
Data observability improves data quality with features like data monitoring, lineage, automated root cause analysis, and data health insights to…
Data Observability for Developers: Announcing Monte Carlo’s Python SDK
Our Python SDK gives data engineers programmatic access to Monte Carlo to augment our platform’s lineage, cataloging, and monitoring functionalities.
10 Quick Tips for Getting Started with Monte Carlo
Getting started with Monte Carlo and data observability? Here's how to use 10 of our most popular features.
Data Observability vs. Data Testing: Everything You Need to Know
You already test your data. Do you need observability, too?…
Stop Treating Your Data Engineer Like A Data Catalog
How to build a data certification program so everyone knows what to expect and what data to trust.
The Non-Engineer’s Guide to Bad Data
According to a recent study by HFS, 75 percent of executives don’t trust their data. Here’s why and what data-reliant…
Now Available: O’Reilly Data Quality Fundamentals, Chapter 3
Available today, Chapter 3 of O'Reilly's Data Quality Fundamentals outlines the tools and techniques necessary to build more resilient data…
Monte Carlo Announces dbt Core Integration to Help Companies Ship Reliable Data Faster
When it comes to achieving reliable data, Monte Carlo, the leading data observability platform and dbt, the data build tool,…
Data Observability: How Clearcover Increased Quality Coverage for ELT by 70 Percent
Learn how the data engineering team at Clearcover increased data quality coverage across their stack by 70 percent with Monte…
Reflections on tech, trust, and data adoption
A few quick thoughts on why trust, and not technology, is stopping today's leader's from driving adoption and impact with…
How to Achieve More Trustworthy Data Pipelines with the Prefect Integration for Monte Carlo
With Monte Carlo and Prefect’s strategic partnership and integration, data engineering teams can seamlessly manage the reliability of their data…
Monte Carlo Named to First-Ever Intelligent Apps Top 40 List
Monte Carlo was recognized as one of the first-ever companies named to the Intelligent Applications 40 list.
IMPACT 2021: The Data Observability Summit Videos Are Now Available On Demand
Missed IMPACT? Have no fear! Full recordings of our keynotes, panels, and fireside chats with Bob Muglia, DJ Patil, Zhamak…
How The Farmer’s Dog Achieves Self-Serve Data Observability with Monte Carlo
How the data team at The Farmer's Dog, a fresh dog food company, achieves reliable data pipelines with automated, end-to-end…
Monte Carlo Launches Insights to Help Data Teams Understand What Data Matters Most to Your Business
Monte Carlo Insights is the first solution on the market to offer customers operational analytics about their data environment.
Unicorns, data mesh, category creation, and more reasons to attend IMPACT: The Data Observability Summit
Five reasons why you should attend IMPACT, the world's first Data Observability summit on Wednesday, November 3, 2021.
Data Observability 101: Everything You Need to Know to Get Started
What is data observability and does it make sense for your stack? Here’s your go-to guide to starting on the…
The Future of Data Engineering as a Data Engineer
Is the data engineer still the "worst seat at the table?" Maxime Beauchemin, creator of Apache Airflow, weighs in on…
Announcing O’Reilly’s Data Quality Fundamentals
Available today, Data Quality Fundamental's press release chapters dive into how some of the best teams are architecting for data…
Monitors as Code: A New Way to Deploy Custom Data Quality Monitors From Your CI/CD Workflow
Monte Carlo releases Monitors as Code, allowing data engineers to easily configure new data quality monitors as part of their…
Data Observability: Five Quick Ways to Improve the Reliability of Your Data
Five common data observability use cases and how they can help your team improve data quality at scale and trust…
Bob Muglia, former Snowflake CEO, to Speak at IMPACT, the World’s First Data Observability Summit
Muglia will join the first Chief Data Scientist of the U.S., the founder of the data mesh, and the creator…
Data Anomaly Detection: Why Your Data Team Is Just Not That Into It
Delivering reliable data products doesn't have to be so painful. Introducing a more proactive approach to detecting data anomalies: the…
Reverse ETL and Data Observability: Solving Data’s “Last Mile” Problem
How Reverse ETL and Data Observability can help teams go the extra mile when it comes to trusting your data…
What is a Data Incident Commander?
How data teams can build more resilient incident workflows with DevOps best practices.
How Vimeo Achieved End-to-End Visibility in Snowflake and Looker with Monte Carlo
Learn why the the data engineering team at Vimeo chose to partner with Monte Carlo for data observability.
The Ultimate Data Observability Checklist
Here are the 5 things every data observability strategy needs to help companies achieve end-to-end data trust.
Getting Started: Automatic Detection and Alerting for Data Incidents with Monte Carlo
Here’s how data teams get up and running with Monte Carlo to automatically detect and alert on data incidents with…
Data Quality Solutions: Build or Buy? 4 Things To Know
Investing in a data quality solution? Here's everything you need to know.
Announcing Monte Carlo’s Incident IQ, a Root Cause Analysis Workflow for Data Teams
How to get started with Incident IQ, Monte Carlo's all-in-one solution for troubleshooting and preventing broken data pipelines.
The Ultimate Guide to Data Quality
What is data quality and why does it matter?…
Monte Carlo and PagerDuty Integration Brings DevOps to Data Pipelines with End-to-End Data Observability
Monte Carlo's PagerDuty integration helps data engineering teams achieve greater visibility into the end-to-end health of their data pipelines.
Beyond Monitoring: The Rise of ML Observability
Modern data and machine learning systems need both monitoring and observability. Here’s why.
How to Extract Snowflake Data Observability Metrics Using SQL in 5 Steps
Monitor the health of your Snowflake data pipelines with these 7 queries to extract Snowflake data observability metrics.
How to Conduct Data Incident Management for Data Teams
Conduct data incident management with 4 simple steps to identify, root cause, and fix data quality issues at scale.
The Right Way to Measure ROI on Data Quality
Introducing a better approach for measuring the cost of bad data to your business.
The Data Engineer & Scientist’s Guide To Root Cause Analysis for Data Quality Issues
Introducing a five-step engineering root cause analysis approach used by some of the best data engineering and data science teams…
5 Reasons Data Discovery Platforms Are Best For Data Lakes
Here are 5 reasons why using a data discovery platform is a better alternative to data catalogs to ensure your…
5 Things Every Data Engineer Needs to Know About Data Observability
With data observability, data engineers can think more strategically about tackling the "good pipelines, bad data" problem.
Data Observability in Practice: Data Monitoring at Scale with SQL and Machine Learning
How to make your own data observability monitors from scratch and leverage basic principles of machine learning to apply them…
The Ultimate Data Observability Checklist
For most teams, data observability is more than just setting up a bunch of pipeline tests and hoping for the…
The New Rules of Data Quality
Unit testing your data only gets you so far. Here’s a better way to manage data quality at scale.
Data Observability: How to Build Your Own Data Anomaly Detectors Using SQL
How to use metadata to understand the root cause of data anomalies and take your data quality testing to the…
Why You Need to Set SLAs for Your Data Pipelines
How to set expectations around data quality and reliability for your company…
Data Observability in Practice Using SQL
A step-by-step tutorial for creating your own data quality monitors to catch freshness and distribution anomalies in your data pipelines.
Data Pipeline Monitoring- 5 Strategies To Stop Bad Data
Your data broke. Now what? Here's how some of the best data teams prevent data downtime and, in the process,…
3 Reasons You Can’t Rely on Testing Data Pipelines to Find Quality Issues
Why aren't we treating data as the dynamic, ever-evolving entity it is? Here's why a hybrid approach to testing data…
How to Improve Data Engineering Workflows with End-to-End Data Observability
With data observability, data teams can now identify and prevent inaccurate, missing, or erroneous data from breaking your analytics dashboards,…
Incident Prevention for Data Teams: Introducing the 5 Pillars of Data Observability
The five pillars of data observability are: Freshness, Distribution, Volume, Schema, Lineage…
Metadata is Useless — Unless You Have a Use Case
Here's why having metadata and lineage without a clear business application is worse than having no metadata at all.
The Data Downtime Before Christmas
What happens when a freshness anomaly threatens to ruin Christmas? Turns out, even Santa Claus and his elves aren’t…
Data Catalogs Are Dead; Long Live Data Discovery
Data catalogs aren't cutting it any more when it comes to metadata management and data governance. Here's how data discovery…
A Summer at Monte Carlo: Improving Data Pipeline Observability at Scale
How I spent my summer internship on Monte Carlo’s software engineering team…
Bringing Reliable Data and AI to the Cloud: A Q&A with Databricks’ Matei Zaharia
An interview with Apache Spark creator Matei Zaharia on all things AI, the cloud, and data reliability…
Demystifying Data Observability
3 practical examples on how to get started with data observability…
Data Observability: How to Fix Your Broken Data Pipelines
In 2020, data is the new software. While software needs to be highly available, data needs to be highly reliable.
How to Solve the “You’re Using THAT Table?!” Problem
How to keep track of your data warehouse's most critical table and reports…
Data Observability Tools: Data Engineering’s Next Frontier
To keep pace with data’s clock speed of innovation, data engineers need to invest in data observability, the next frontier…
[VIDEO] Introducing Data Downtime: From Firefighting to Winning
During a 2019 Data Council meetup, Monte Carlo Co-founder & CEO Barr Moses discusses why data downtime matters to the…
How to Calculate the Cost of Data Downtime
Introducing a better way to measure the financial impact of bad data on your company…
How to Fix Your Data Quality Problem
Introducing a better way to prevent bad data.
What is Data Reliability?
And how to use it to start trusting your data.
How to Migrate to Snowflake Like a Boss
3 things you need to know for a smooth migration.
What We Got Wrong About Data Governance
And how we can make it right.
12 Data Quality Metrics That ACTUALLY Matter
How to improve your Data Quality Metrics and why it matters to your business.
Good Pipelines, Bad Data
How to start trusting data in your company.
Closing the Data Downtime Gap
How to get ahead of bad data.
What is Data Downtime?
Data downtime refers to periods of time when your data is partial, erroneous, missing or otherwise inaccurate.
[Video] What is Data Observability?
What is Data Observability?…