Product

Ensuring Reliable Analytics with Bigeye Dependency Driven Monitoring | Webinar Replay

Catch the replay to learn how Bigeye Dependency Driven Monitoring helps data teams!

What is Data Quality?

Data quality is more than just a trend —it's a critical factor that can make or break organizations.

Product

Announcing Bigeye Dependency Driven Monitoring

Optimized monitoring for every column that matters, and none that don't.

Product

Announcing Bigeye Lineage Plus

Massive, complex, hybrid enterprise data environments have never looked so good.

Thought leadership

Building Strong Relationships with Business Stakeholders | Webinar (Featuring Nik Acheson)

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.

Product

How Zoom Prevents Three Outages A Month With Bigeye

By adopting Bigeye, Zoom has transformed its data operations, moving from reactive problem-solving to proactive data quality assurance.

Thought leadership

Data as a Value Center | Podcast (Featuring Katie Bauer)

Dive into the world of data team transformation in our latest podcast episode!

Thought leadership

Complete guide to understanding data observability in 2024

This is your one-stop shop for all things data observability. Learn what data observability is, who it’s for and who uses it, how it benefits organizations, how it works, key terms, use cases, and best practices.

Thought leadership

Three Essential Trends For Data Leaders in 2024: LLMs, Verticalization, and Consolidation

As we step into 2024, the data and analytics landscape continues to evolve, presenting data leaders new opportunities as well as new challenges.

Thought leadership

Building Strong Relationships with Business Stakeholders: A Data Leader's Guide

Data leaders can no longer focus only on the data itself, they must also build strong relationships with business stakeholders.

Thought leadership

Transforming Your Data Team: From Cost Center to Value Center

Data teams have often been perceived as cost centers, but does it have to be that way? We don't think so.

Thought leadership

Data Leadership 101: Tips and Tricks for New Leaders

For new leaders who were recently promoted or offered a new role, suddenly being responsible for the success of an entire team can be daunting.

Thought leadership

Navigating A Non-Modern Data Stack: Achieving Analytics Reliability and Data Observability

Unless you're a young startup, your data infrastructure is unlikely to be purely a modern data stack. So, how do you work with what you've got?

Thought leadership

The Five C's: A Blueprint for Building High-Scale Data Observability Platforms

Inspired by another CEO's discussion of "Four C's" in relation to their own observability company, I realized the importance of distilling our core architectural insights into our own set of guiding principles, the "Five C's.”

Thought leadership

The Business Impact Of Bad Data

Data inaccuracies are not merely technical glitches; they can lead to significant business risks, particularly when it comes to an organization's reputation.

Company

Enabling self-serve data quality with Bigeye

Is "self-serve" data quality possible? Sure it is. Take a spin through the ways Bigeye enables data teams to self-serve the data they need.

Company

Bigeye and dbt Labs partner to speed data issue detection and resolution

With the new partnership, Bigeye and dbt Labs help data teams build healthy, reliable data pipelines and find and fix data issues before they impact their business.

Thought leadership

A brief history of Databricks

Databricks has been a key innovator in data over the past decade. Here's a rundown of their history and impact on data engineering and ML.

Thought leadership

What is data ingestion?

Welcome to your quick primer on all things data ingestion. From components and patterns to the best practices for implementation, read on.

Thought leadership

20 data reliability use cases from real-life teams

How do modern data teams build and enforce data reliability? We researched 20 use cases from real-life data teams in order to find out.

Thought leadership

What is data discovery?

How do you break the cycle of confusing, unusable data and use your vast data stores to drive business value? "Data discovery" is key.

Thought leadership

Data vault modeling: Everything you need to know

What is data vault modeling? From common use cases to best practices to framework architecture, this post has you covered. Read on.

Thought leadership

A brief history of Snowflake

Here's a brief history of one of the powerhouse organizations bringing change and innovation to the data space: Snowflake.

Engineering

Why data lineage is mission-critical for businesses today

What is data lineage, what makes it difficult to do well, and how should organizations leverage it? In this post, we'll walk through it.

Thought leadership

ETL vs. ELT

What are the intricacies of ETL and ELT? Here are their key characteristics, advantages and drawbacks, plus some use cases where you'd use each.

Thought leadership

Tackling data correctness: Lessons from Stripe

How does Stripe ensure end-to-end data reliability? Here, we walk through some real-life lessons learned and best practices for doing just that.

Thought leadership

Data observability in the modern data stack

Over the past five years, there's been a paradigm shift in data engineering. Here's how data observability fits into a “modern” data stack today.

Engineering

Making sense of machine learning and artificial intelligence models by monitoring the training data

When you're training ML /AI models, the input data selection process matters. Build solid ML/AI model performance with data observability.

Thought leadership

Incident management for data teams

Software engineers use a framework for incident management to resolve issues. Here's how to apply a few core incident management concepts to data teams.

Thought leadership

Snowflake Summit 2023 and data observability: 5 key takeaways

This post dissects Snowflake Summit 2023, from a data observability perspective. What do the announcements and new features mean for data observability?

Product

Triaging data issues with Bigeye (Part 1)

Part 1 of a 2-part series where we walk through tips and tricks for triaging and managing data issues you discover in Bigeye.

Thought leadership

Creating trust in data: 10 examples from real life data teams

Here are ten rules for creating trust in data, taken from real-life case studies, straight from the mouths of battle-tested data practitioners.

Thought leadership

The data reliability engineering dictionary

Here is your go-to guide for the dense terminology related to data reliability engineering. Both seasoned pros and new practitioners, read on.

Thought leadership

The complete introduction to dataOps

This guide is an overview of dataOps: why it's important, how to set it up, and the tools involved. If you're looking to implement dataOps, read on.

Company

MLOps and data observability: What should you know?

How do MLOps and data observability interact and support each other? Here are nine things you should know about this partnership.

Thought leadership

Eight trends in data reliability over the next few years

The coming years will bring an evolution in data reliability from emerging technologies. Here are key trends shaping the near-term future of data.

Engineering

Navigating the data universe: lakes, warehouses, and lakehouses

Understanding data lakes, data warehouses, and how they can work together may bring substantial benefits to your organization.

Company

Bigeye is sharing data reliability strategies for scale at Snowflake Summit 2023

Here's what's happening at Snowflake Summit 2023. Bigeye will take the stage with executives and customers to talk about data reliability at scale.

Thought leadership

Six key use cases for data contracts

What is a data contract, and who uses it in the real world? This post explores six use cases across several industries where data contracts can help.

Engineering

Data in Practice: Interview with a CartaX engineer

We spoke with Andrew Nguonly, the first data engineer at CartaX, to learn his approach to data reliability and his from-the-trenches tips.

Product

Monitoring Stripe data with Bigeye

How do you make sure your Stripe data is being accurately monitored? With a data observability tool like Bigeye. Let's walk through it.

Product

Agent or agentless data observability? The choice is yours.

Bigeye is the only data observability platform that offers both agentless and agent-based models, allowing organizations to choose the deployment option that best fits their needs.

Product

New and Improved Deltas: Comparing tables is now even easier

Our latest Deltas update adds the ability to compare against multiple tables, send notifications, and more.

Engineering

Data mesh: A people challenge, not a technical challenge

Data mesh is fundamentally NOT a technical issue. Figure out your people and your processes; you data governance comes together from there. Let's explore.

Thought leadership

Can ChatGPT identify and handle data quality issues?

Can a data scientist's job be automated? In this post, we'll explore the sophistication of AI as it relates to processing and managing messy data.

Thought leadership

The complete guide to understanding data SLAs

An SLA is a "service level agreement." If you have questions around the SLA meaning, purpose, or components, this post will walk you through it.

Thought leadership

The complete guide to understanding data reliability

Your one-stop shop for all things data reliability. Learn what data reliability is, who uses it, how it works, use cases, and best practices.

Thought leadership

Data reliability: All along the pipeline

What does data reliability look like at each stage of the pipeline? In this post, we explore.

Product

Efficiency with issue monitoring states

In this post, we'll take a general look at the lifecycle of an issue, from triage to close, using Bigeye.

Product

Bigconfig continues to raise the bar for data monitoring as code

We're excited to announce new updates and improvements to Bigconfig, Bigeye’s industry-leading data monitoring as code solution.

Thought leadership

Data pipeline monitoring vs. data quality monitoring: What's the difference?

Why does the difference between data pipeline monitoring and data quality monitoring matter? In this post, we'll define both, and explain what the difference means.

Thought leadership

9 common signs it's the right time for data reliability

There's never a bad time to invest in data reliability. But if your business model depends heavily on data for decision-making, it's absolutely vital. Without it, you may not find the path to streamlined customer experience and revenue generation. So here are some common signs it's time to invest in data reliability.

Product

Streamlined data reliability engineering notifications with Bigeye

Introducing many new Slack-related features! In this post, we'll walk through them.

Product

The difference between data observability and legacy data quality platforms

The data quality products of yore originated in the on-prem era. Modern data observability is a whole different ballgame. From complexity to scale to the cloud, here's the difference between data quality systems of today and those of yesterday.

Product

ELT vs. ETL: What's the difference?

ELT, ETL - what's the difference, and where is it important to integrate data observability? We explore.

Thought leadership

Data Reliability Engineering versus Site Reliability Engineering

What does the nascent field of Data Reliability Engineering have to do with its more-established older sibling, Site Reliability Engineering? Here, we walk through some of the key similarities and differences.

Thought leadership

5 misconceptions about data observability

Think data observability isn't for you? Maybe you're right. But make sure you aren't operating under some of these common misconceptions.

Engineering

Why everyone tries to reinvent SQL (and why it never works)

Inventing a better SQL is easier said than done, even though many have tried. Here's why, in our opinion, SQL is here to stay.

Company

5 reasons to attend DRE Con 2023

In April, DRE Con will bring together many of the world's leading data reliability engineering experts. Why should you attend this virtual conference? We'll tell you 5 great reasons.

Product

How Bigeye detected issues that our dbt CI jobs missed

This is a story about broken data, and how Bigeye pinpointed the issue, minimizing the impact to internal analytics. Crisis, averted. Read on to learn more.

Thought leadership

Data observability doesn't fix anything

Working on data quality used to mean simply "fixing the issues." Today, the understanding is more nuanced. Data quality techniques don't actually fix anything. Why not? Read on.

Engineering

Data lineage: Buy it or build it?

Data lineage is a core component of observability. But can you build your own lineage solution, or buy one off-the-shelf? We explore, and make some recommendations.

Thought leadership

Data in Practice: Systematizing data quality at Uber-scale

In the "Data in Practice" series, we talk to real data engineers who have put data reliability engineering concepts into practice, learning from their challenges and successes in the real world.

Product

Using Bigeye Collections to organize and scale your monitoring

There are four different ways to use Bigeye Collections to organize and scale your monitoring. Let's explore each one.

Product

Intro to Bigeye Collections

How do you manage data quality metrics at enterprise scale? In this post, we'll walk through it.

Thought leadership

Data observability catalyzes your digital transformation goals

At the heart of every digital transformation effort, organizations are betting on new digital technologies to create an organizational revolution. Without data observability, this revolution is more of a pipe dream than a reality for most organizations.

Product

Bigeye + Alation: Announcing the Open Data Quality Initiative Integration

We're excited to announce our updated Alation integration using Alation's Open Data Quality Framework to bring combined data governance and quality to the enterprise.

Thought leadership

Business, backed by data observability

Business runs better when its backed by data observability. But in the interest of "show, don't tell," let's point out some fairly recent examples.

Engineering

Easy SQL tricks to clean messy data

Cleaning messy data can be time-consuming and tedious. In this tutorial, we will go over some easy SQL tricks to help you with the most common data-cleaning tasks.

Company

Bigeye is now on the Google Cloud Marketplace

We are thrilled to announce that Bigeye is now available on the Google Cloud Platform (GCP) Marketplace.

Engineering

So you've implemented dbt tests/great expectations, now what?

In this blog post, we will cover what you do and don’t get from SQL checks like those in dbt tests and Great Expectations, and when you should make the shift to a data observability solution.

Thought leadership

Data in Practice: Building out a data quality infrastructure with Brex

In the "Data in Practice" series, we talk to real data engineers who have put data reliability engineering concepts into practice, learning from their challenges and successes in the real world.

Product

So you've implemented metadata metrics...now what?

Metadata metrics are a great first step in your data observability journey, but they are not the end of it.

Thought leadership

A gentle introduction to data contracts

Production services are no longer just generating data as a byproduct. Instead, data is the product and should be treated as such. This is the argument that data contracts make.

Product

Data observability at scale: monitoring a 50,000 table data lake with Bigeye

You never know if what you theorize will work in practice! Recently, the Bigeye platform was put to the test when a partner connected a data warehouse with thousands of schemas and 50,000 tables—more than 10 times what we usually expect.

Engineering

Which scheduler should I use for dbt jobs?

As an analyst or data engineer, you’ve probably used dbt ad-hoc, launched the dbt shell and run commands against a data warehouse. This works during the development and testing stages. What happens when you need to move your dbt jobs to production?

Product

Alation + Bigeye: Enabling Everyone to Understand the Health of Their Data

Bigeye enables data teams to operate increasingly complex data ecosystems with the assurance that if something goes wrong, they will know before the business is affected.

Thought leadership

Top data trends for 2023

In 2022, the data space matured by leaps and bounds. What's in store for 2023? Here are nine top data trends we think we'll see in the coming year.

Thought leadership

Prevent data engineering burnout

The most effective data engineering teams are those who successfully shift from a toil culture— a series of manual one-off tasks, firefights, and moments of heroism—to a reliability culture with planned, repeatable, and scalable processes.

Product

Automatically quarantining bad data with Bigeye and dbt

Let’s look at how I built an automatic data quarantining workflow by leveraging Bigeye’s automatic anomaly detection and dbt’s data transformation capabilities.

Product

The five most common questions we get about anomaly detection

Users often have questions about anomaly detection. We sat down with our Sales team to discuss the most frequently-occurring anomaly detection questions they get. In this blog post, we answer five of the most common.

Thought leadership

Data in Practice: Anomaly detection for data quality at Netflix

Netflix is the streaming service we all know and love. They deliver delivers over six petabytes of data to customers daily. This post covers some of Laura Pruitt's insights into how Netflix maintains the quality of their core dataset.

Product

Using Bigeye data lineage for actionable root cause and impact analysis

In this post, we’ll explore how data engineers use Bigeye’s built-in data lineage features to see where data problems are impacting critical downstream tables and identify the upstream root cause for faster, easier resolution.

Thought leadership

20 questions for data observability readiness

Is your company ready to implement data observability? How will you do it? Get started by asking yourself these 20 important questions that will help you orient yourself in the data observability universe.

Thought leadership

Data Practitioner Spotlight: Laura Dawson of EDO on data taxonomies

In the "Data Practitioner Spotlight" series, we interview data practitioners from the field. In this conversation, we dive deep into data taxonomies and using ad data to make better decisions with Laura Dawson of EDO.

Thought leadership

The onion, the carrot, and the “alert budget”

When you introduce a data observability product to your stack, be thoughtful about how you choose to roll it out. The rollout will impact how your team’s attention is directed, and therefore how your budget is allocated.

Engineering

How to evaluate data observability platforms (RFI)

So you’re considering a data observability platform, but want to make sure you’re choosing the best one for your business. We’ve got some tips to help you run a thorough evaluation process.

Engineering

Does your data team hate NoSQL? It doesn't have to be that way!

NoSQL data stores were invented by engineers. For engineers, by engineers. So what’s the problem? Data teams bear the burden of any issues happening

Company

Kit: Why I joined Bigeye

In this series, we interview members of Bigeye about why they joined the team. Here, we talk to Bigeye's VP of Sales, Kit Wetzler.

Engineering

Real "Oh, damn!" moments from data engineers

Here are six specific stories from engineers and product managers at marquee companies about a moment when the importance of data quality really hit home.

Product

Bigconfig empowers data teams to implement data reliability at scale

Bigeye has partnered with dozens of top data teams to create Bigconfig, a declarative, YAML-based monitoring-as-code solution, that allows teams to continue this convention and integrate data monitoring as code into their workflows.

Company

Announcing Bigconfig and developer tools for enterprise data monitoring as code

We’re thrilled to announce the general availability of Bigconfig, a powerful new way for Bigeye customers to define data monitoring as code and automatically apply it across their enterprise data warehouse.

Thought leadership

The data observability dictionary

We’ve compiled this dictionary based on common terms we encounter in our day-to-day work. Hopefully it can help you understand some key definitions, differentiations, and components of data observability.

Thought leadership

Data observability: Build or buy?

Companies looking to invest in data infrastructure tooling often face an age-old conundrum: do we build it or do we buy it? Let's explore.

Thought leadership

Data anomaly detection requires hindsight and foresight

While anomaly detection comes in many forms, there are two major approaches: ex ante and post hoc. Ex ante means before the event, which is a prediction of the future. Post hoc means after this, which is reasoning after all events have already occurred.

Engineering

7 ways to level up your dbt tests

Dbt tests are a great first step for organizations that want to improve data quality and reliability. After a time, a second follow-up step becomes necessary. That's when observability comes in.

Company

Morgan: Why I joined Bigeye

In this series, we interview members of Bigeye about why they joined the team. Here, we talk to brand designer Morgan.

Thought leadership

Getting started with dbt tests

Increase data quality by validating your dbt transformations.

Engineering

Data in Practice: Data reliability tips from a former Airbnb data engineer

We spoke with Dzmitry Kishylau, a former member of Airbnb’s Trust and Safety team, to learn how they approached data reliability and get from-the-trenches tips.

Engineering

Webinar transcript: Taking the pain out of the backfilling process for your data

When you have billions of datapoints, making retroactive changes can be seriously painful. Here's how to remove some of that pain, from our CTO Egor Gryaznov.