Databand.ai

Congrats to DARREL HASWELL Marcus Boone Ray Beharry for their leadership recognition in Data Intelligence! Check out the new Data Intelligence Buyers Guide Software Provider and Product Assessment?today.

Scott Moore ? tells it straight when it comes to the benefits of data observability. 1?? Detect earlier 2?? Resolve faster 3?? Deliver trusted data Check out Scott's new YouTube video, "Achieving Data Trust with Data Observability," as he walks through these benefits and a quick demo of?Databand.ai.

Check out Steve Sawyer and Eric Jones presentation at the 2024 Airflow Summit. Steve discusses a case study for how IBM Data Observability with Databand collects metadata to build historical baselines, detect anomalies, and triage alerts to remediate data quality issues for your data pipelines and warehouses. Additionally, he will provide a product perspective on the technologies IBM is building to meet the data observability needs across the enterprise and how they relate to our investments in AI and data fabric. https://lnkd.in/e29b4kir

In this Hero Talk episode, we explore data observability, which has become essential for Data Engineers dealing with complex data pipelines with Andreas Kretz. Ryan and Andreas Kretz delve into data observability and its significance for Data Engineers, addressing common challenges faced in monitoring and maintaining data pipelines. They explain how Databand helps monitor and improve data reliability, ensuring data flows smoothly from source to destination. Give it a watch! ?? ?? https://lnkd.in/e2k4ez7C

A quick Google search will show that data quality metrics involve all sorts of categories. Completeness, consistency, conformity, accuracy, integrity, timeliness, continuity, availability,?reliability, reproducibility, searchability, comparability, and probably ten other categories we forgot to mention all relate to data quality. So, what are the right metrics to track? Well, we’re glad you asked. Download The top data quality metrics you need to know today. https://lnkd.in/eKRbwCrq

IBM offers a holistic approach to data quality with an easy way to start small and expand your data quality program across the full enterprise data ecosystem. Check out the latest 2024 Gartner? Magic Quadrant? for Augmented Data Quality Solutions to see why IBM was named a leader and how Databand.ai provides data observability for 360 data quality. Download the Gartner report today. https://lnkd.in/eE54MFqK

Gartner estimates that by?“2026, 30% of enterprises implementing distributed data architectures will have adopted data observability techniques to improve visibility over the state of data landscape, up from less than 5% in 2023.” Check out this Gartner Innovation Insight: Data Observability Enables Proactive Data Quality by Melody Chien to learn about: - The 4 critical features of data observability - The 5 critical technologies to create a data awareness AI engine - The roles of data observability and data quality - The benefits and uses for data observability - Recommendations for data and analytics leaders looking to enhance data quality and governance practices with data observability https://lnkd.in/eTnG_HTv

Databand.ai has been named a 2024 Fall Momentum leader by G2 for data observability! Check out reviews like this one over at https://lnkd.in/eiaSMRTK. "IBM Databand helps in resolving data issues like pipeline failure or errors in warehouse data storage, ensuring high reliability. It allows connectivity with various cloud-based tools like Azure Data Factory and Apache Airflow. We have set up alerts in the data pipeline to ensure any data deviation is timely detected."

"Without an operational view such as Databand’s, it would be extremely hard to understand the overall health of our ML pipelines. The integration of availability tracking and aggregated metrics from Airflow has been super useful. I appreciate looking at Databand and seeing the Airflow data within one dashboard.” - Machine Learning (ML) Engineering Manager https://lnkd.in/eabWFZ2P

Historically, data engineers have often prioritized building data pipelines over comprehensive monitoring and alerting. Delivering projects on time and within budget often precedes long-term data health. If you're still struggling with long-term data health, data observability might be the missing piece in your data integration puzzle. Check out our latest blog on why prioritizing observability prevents bad data from derailing your data integration projects. https://lnkd.in/e2tumX_e