Blog
No Python, No SQL Templates, No YAML: Why Your Open Source Data Quality Tool Should Generate 80% Of Your Data Quality Tests Automatically
The reality is that 80% of data quality tests can be generated automatically, eliminating the need for tedious manual coding. Learn how to do it today.
Summary of the Gartner Presentation: “How Can You Leverage Technologies to Solve Data Quality Challenges?”
Summary of the Melody Chien from Gartner Presentation: “How Can You Leverage Technologies to Solve Data Quality Challenges?”
Navigating the Storm: How Data Engineering Teams Can Overcome a Data Quality Crisis
Navigating the Storm: How Data Engineering Teams Can Overcome a Data Quality Crisis Ah, the data quality crisis. It's that moment when your carefully crafted data pipelines start spewing out numbers that make as much sense as a cat trying to bark. You know you're in...
How Three Small Pharma Companies Used DataKitchen to Achieve Commercial Launch Success and Skyrocket to $100 Billion in Acquisition Value
Why Did Three Pharmaceutical Companies Preparing for Their Commercial Launch (That Eventually Sold for $100 Billion) Choose DataKitchen? Three pharma companies recently made headlines by securing successful exits totaling $100 billion. What’s their common denominator?...
Data Observability and Data Quality Testing Certification Series
Data Observability and Data Quality Testing Certification Series We are excited to invite you to a free four-part webinar series that will elevate your understanding and skills in Data Observation and Data Quality Testing. This series is crafted for professionals...
The Five Use Cases in Data Observability: Ensuring Accuracy in Data Migration
The Five Use Cases in Data Observability: Accuracy in Data Migration (#5) Data migration projects, such as moving from on-premises infrastructure to the cloud, are critical and complex projects that involve transferring data across different systems while...
The Five Use Cases in Data Observability: Fast, Safe Development and Deployment
The Five Use Cases in Data Observability: Fast, Safe Development & Deployment (#4) The integrity and functionality of new code, tools, and configurations during the development and deployment stages are crucial. This blog post delves into the third critical...
The Five Use Cases in Data Observability: Mastering Data Production
The Five Use Cases in Data Observability: Mastering Data Production (#3) Introduction Managing the production phase of data analytics is a daunting challenge. Overseeing multi-tool, multi-dataset, and multi-hop data processes ensures high-quality outputs. This blog...
The Five Use Cases in Data Observability: Effective Data Anomaly Monitoring
The Five Use Cases in Data Observability: Effective Data Anomaly Monitoring (#2) Ensuring the accuracy and timeliness of data ingestion is a cornerstone for maintaining the integrity of data systems. Data ingestion monitoring, a critical aspect of Data...
The Five Use Cases in Data Observability: Data Quality in New Data Sources
The Five Use Cases in Data Observability: Data Quality in New Data Sources (#1) Ensuring their quality and integrity before incorporating new data sources into production is paramount. Data evaluation serves as a safeguard, ensuring that only cleansed and...
The Five Use Cases in Data Observability: Overview
Harnessing Data Observability Across Five Key Use Cases The ability to monitor, validate, and ensure data accuracy across its lifecycle is not just a luxury—it’s a necessity. Data observability extends beyond simple anomaly checking, offering deep insights into...
DataOps and Data Observability Education And Certification Offerings From DataKitchen
DataKitchen Training And Certification Offerings For Individual contributors with a background in Data Analytics/Science/Engineering Overall Ideas and Principles of DataOps DataOps Cookbook (200 page book over 30,000 readers, free): DataOps Certification (3...
Webinar Summary: Introducing Open Source Data Observability
Last week's webinar, presented by Christopher Bergh, CEO and Head Chef at DataKitchen, explored the impact of newly released open-source software tools on data operations and analytics. The event was an informative session that dove deep into the functionalities and...
Why We Open-Sourced Our Data Observability Products
Introducing DataKitchen’s Open Source Data Observability Software Today, we announce that we have open-sourced two complete, feature-rich products that solve the data observability problem: DataOps Observervability and DataOps TestGen. With these two products, you...
