The Data Downtime Blog
Build vs Buy Your Data Warehouse, Lake, or Lakehouse
There's no one-size fits all answer to building or buying your data platform. In this piece we discuss the decision to build vs buy data warehouse, data lake, and data lakehouse solutions.
Implementing Data Contracts in the Data Warehouse
Getting started with data contracts? Here's how to implement data contracts in your data warehouse across your critical production tables.
What is a Data Mesh — and How Not to Mesh it Up
A beginner’s guide to implementing the latest industry trend: a data mesh.
Data Observability
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…
Data Platforms
Build vs Buy Your Data Warehouse, Lake, or Lakehouse
There's no one-size fits all answer to building or buying your data platform. In this piece we discuss the decision…
Implementing Data Contracts in the Data Warehouse
Getting started with data contracts? Here's how to implement data contracts in your data warehouse across your critical production tables.
Data Fabric vs. Data Mesh: Everything You Need to Know
Building a data fabric? Here's everything you need to know about this emerging architecture taking the data world by storm.
Data Culture
Why Data Governance Matters, Best Practices, and How to Build a Strategy
Building a data governance strategy? Here's everything you need to know.
Meaningful Product Experimentation: 5 Impactful Data Projects for Building Better Products
How data teams and product leaders can do product experimentation right and other impactful data projects for building better products.
The Future of Data Warehousing
Data warehouses are at an exciting point of expansion and evolution. As every company becomes a data company, here's how…
Data Reliability
7 Data Quality Checks in ETL Every Data Engineer Should Know
With the right data quality checks in ETL pipelines, you can identify and fix issues in near real-time and build…
Meaningful Product Experimentation: 5 Impactful Data Projects for Building Better Products
How data teams and product leaders can do product experimentation right and other impactful data projects for building better products.
Top 5 Data Engineering Deep Dives in 2022
How do you engineer field-level lineage, data anomaly monitors, Spark lineage, or data pipeline circuit breakers? We’re glad you asked. …
Case Studies
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…
Announcements
Monte Carlo Recognized as Winter 2023 Data Observability Leader by G2
Monte Carlo was recognized as a Winter 2023 Data Observability leader by G2, a leading peer-to-peer review site.
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…
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.