Is your data setting you up to scale... or stumble?

Struggling with scattered metrics, outdated views, and time-consuming reporting?

Watch the webinar replay where we recap the latest insights and trends from the Snowflake & Databricks conference.

Learn how Bigeye is bridging the gap between modern data platforms like Snowflake and legacy systems.

Watch the recap of 2024's Gartner Data UK Conference, where we highlight key insights and trends discussed at the event.

Whether you're a daily Tableau user or a data leader looking to enhance your analytics reliability, this webinar is for you.

Join us to learn how Bigeye Dependency Driven Monitoring ensures reliable analytics.

Join Egor and Nik in this insightful webinar as they delve into the importance of fostering strong relationships with business stakeholders in the realm of data leadership.

Discover how to lead your data team out of the "cost center" sterotyope.

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.

How can data engineers prevent issues with pipelines from reaching data consumers like data science teams, and end-users like executives or customers—without adding a ton of extra work?

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.

Learn how Bigeye uses data-driven impact and root cause analysis to provide complete upstream and downstream context into issues for effective prioritization and fast remediation.

The "modern data stack" promises agility, scale, and modularity for data teams. But what is the current reality of the data specialist? With so many options, is there a right way to “do” data?

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.

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.

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:

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:

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.

Whether it’s for an executive dashboard or an ML model, reliable data is critical for the applications that make data-driven companies tick. But when it comes to creating reliable data pipelines, where do you start and what tools and processes do you need in place?

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.

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.