The great challenge of data observability building Trust at scale

The cost of data cleansing is often beyond the comfort zone of companies inundated with potentially dirty data. That obstructs the paths to a reliable and compatible flow of corporate data.

Few companies have the resources to develop tools for challenges like data observability at scale, according to Kyle Kirwan, co-founder and CEO of data observability platform Bigeye. As a result, many companies are basically flying blind, reacting when something goes wrong rather than proactively addressing data quality.

Trust in data provides a legal framework to manage shared data. Promotes collaboration through common rules for data security, privacy, and confidentiality; and enables organizations to securely connect their data sources in a shared data repository.

Bigeye brings together data engineers, analysts, scientists, and stakeholders to build trust in data. Its platform helps companies automate monitoring and anomaly detection and create service level agreements to ensure data quality and reliable pipelines. With full API access, an easy-to-use interface, and automated yet flexible customization, data teams can monitor quality, proactively detect and resolve issues, and ensure data can be trusted by all users.

Uber data experience

Two early members of Uber’s data team, Egor Gryaznov, co-founder and CTO of Kirwan and Bigeye, set out to use what they learned from building Uber’s scale to create easier-to-implement SaaS tools for data engineers. .

Kirwan was one of Uber’s first data scientists and the first metadata product manager. Gryaznov was a staff-level engineer who managed Uber’s Vertica data warehouse and developed several internal data engineering tools and frameworks.

They realized that the tools their teams were building to manage Uber’s massive data lake and thousands of internal data users were far ahead of what was available to most data engineering teams.

Automatically monitoring and detecting reliability issues within thousands of tables in data warehouses is no easy task. Companies like Instacart, Udacity, Docker, and Clubhouse use Bigeye to keep their analytics and machine learning running continuously.

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