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Webinar
Using Bigeye to find and fix data anomalies before they break your business
Bad data means bad business. Join Bigeye Solutions Engineer, Paul Bennett, to see how you can use Bigeye's powerful data quality and pipeline monitoring, anomaly detection, and root cause analysis to identify unknown changes in your business and resolve them before they impact your customers.
Webinar
Data outages: Why CIOs can't afford to ignore data quality
After a car company incorrectly recorded vehicle data that led to police reports and arrests, it led to a catastrophe that involved them owing $168 million in restitution. When another company's data quality errors were uncovered by the FDA, it went all the way to Congress.
Webinar
Webinar: Automating data monitoring as code with Bigeye
Data engineering teams need to ensure the business is fed a steady stream of reliable data while simultaneously managing complexity in their pipelines. To meet these challenges, data teams are employing CI/CD best practices and Terraform-like “as code” solutions.
Webinar
Techvent2022: Data reliability engineering: balancing speed and reliability for data platform teams
Data platform teams face a challenge in balancing speed and reliability. But one big factor can make a huge difference: data reliability engineering. What is it, and how can it break down speed and reliability challenges? Why now? Bigeye's CEO - Kyle Kirwan - dives in to explain how data platform teams really can have it all; velocity, reliability, and accuracy. Check out his talk from TechVent 2022.
Webinar
Taking the pain out of the backfill process
As your datasets grow to billions of records and encompass more complex logic, having a strategy for backfilling becomes more business-critical. Join us for a discussion on taking the pain out of the backfilling process. Egor Gryaznov, Bigeye co-founder and CTO, will show us ways to make your life easier whenever you need to reprocess large segments of your data. Watch this video to learn:
Webinar
Defining reliability: SLAs for data platform teams
Reliable and trustworthy data is critical for any data-driven company, but it can be a big challenge when there are multiple teams each producing, transforming, and consuming data from one another simultaneously. Data teams are often caught in the middle, struggling to keep everyone happy. SLAs can remedy this challenge, serving as a powerful tool for aligning on the definition of quality and who’s responsible for addressing issues when they arise. Watch this video to learn:
Webinar
Data Reliability Engineering—Why AI and ML Pipelines Fail: The Exploratory Data Analysis Problem
The automation that AI and ML provide has been widely seen as a solution to dealing with the complex nature of real-world data. Companies have rushed to take advantage of AI and ML to supercharge their businesses. Yet, most AI and ML initiatives fail. Why? Because data scientists aren’t effectively exploring the data.
Webinar
O'Reilly Superstream Series: Accuracy in Analytics—trusted data in a self-service world
From the Strata Data virtual conference, Kyle Kirwan, CEO and co-founder of Bigeye, discusses how companies can evolve their data quality practices to ensure high-quality decision-making throughout each stage of the self-service journey.
Webinar
How to tackle data quality: a three-phase approach
Data quality incidents slow analysis, damage dashboards, break applications, and impact machine learning model performance. But data quality is a big challenge and attempting to tackle it all at once can make it hard to show meaningful results.