Data Observability

4 Reasons Why Every Data Team on Snowflake Should Invest in Data Observability

Snowflake is a gamechanger for your data strategy. With the right approach to data 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.

Data Lineage is Broken – Here Are 5 Ways to Fix It

Data lineage is a must-have feature of the modern data stack, yet we're struggling to derive value from it. Here's…

Is DataOps the Future Of the Modern Data Stack?

As data needs scale, teams need to start prioritizing reliability. Here’s why DataOps might be the answer—and how you can…

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 Pillars You Need To Know

Reliable data increases trust. Learn the 5 pillars of Data Observability-freshness, distribution, volume, schema, lineage-to fully understand your data health.

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: 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 Builds Data Reliability 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 Incident Management for Data Teams

4 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.

3 Strategies to Prevent Bad Data in Your Perfectly Good Pipelines

Your data broke. Now what? Here's how some of the best data teams prevent data downtime and, in the process,…

Why Testing Your Data Is Insufficient

Data is a dynamic, ever-evolving entity. So why aren't we treating it like one? Here's why a hybrid approach to…

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.

Data Quality Metrics  — You’re Measuring It Wrong.

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?…