A recommendation engine starts to recommend irrelevant products. A fraud model permits transactions that it ought to have blocked. The model itself was not altered by anyone. The upstream data that supplies the model changed, and the problem went unnoticed until customers were impacted.
Bigeye data observability is a cloud-native platform that profiles your warehouse tables and columns to determine normal behaviour, and then flags freshness, volume, schema, and distribution anomalies before they appear in a dashboard or model. It fits data teams using Snowflake, BigQuery, Redshift, or Databricks that require broad monitoring without hand-writing rules for each table.
Most AI projects fail due to the gap between "the pipeline ran" and "the pipeline ran correctly".
Data quality problems typically arise from unannounced schema changes in upstream systems rather than from errors in modelling or architecture diagrams. Data observability platforms aim to monitor these gaps, and Bigeye stands out as a prominent tool in this category. This guide describes Bigeye's functionality, its position relative to competitors, and its suitability for organisations building AI applications on their existing data infrastructure.
Shipshape Data constructs data foundations for AI. The pattern outlined above occurs often in our work.
Teams allocate significant resources to models and dashboards, only to discover that the underlying data is unreliable. A tool such as Bigeye cannot fix a flawed architecture. Nevertheless, it will alert you as soon as an issue arises within a functional system, addressing a major portion of the problem.
Why data quality failures stay invisible until they're expensive
Most artificial intelligence failures are not apparent. A pipeline does not halt with an error message. Instead, it starts delivering incorrect data to a model. For example, a currency field could change from GBP to USD partway through a table. A join might start losing four per cent of its rows due to a change in an upstream schema. A scheduled data load could fail on a Friday night without anyone noticing until the dashboard shows incorrect data on Monday. These issues do not raise exceptions. They gradually degrade the quality of downstream results until a person spots a business problem instead of the technical cause.
Costs rise by the time these issues are identified. Engineers spend several days tracing metrics across multiple transformation steps to pinpoint the source of a failure. The users who were expected to rely on the AI feature lose confidence, and rebuilding that trust takes more time than fixing the data pipeline. If the data concerns regulated sectors such as finance or healthcare, you may need to explain to a compliance team why a report was inaccurate for three weeks. These consequences are not accounted for in project plans. Instead, they emerge later as the reasons why the original project plan failed.
Data observability tools aim to identify issues earlier. These platforms monitor data directly instead of waiting for a user to report a faulty dashboard. They track metrics like freshness, volume, schema, and distribution to alert users when data deviates from established patterns. Bigeye is a well-known platform in this category. It works alongside other tools that focus on the related task of converting raw data into reliable information for business use.
What Bigeye is
Bigeye is a cloud-native data observability platform. You point it at your warehouse, Snowflake, BigQuery, Redshift, Databricks, and it starts profiling tables and columns to learn what normal looks like for each one: how often a table usually refreshes, roughly how many rows land each day, what the null rate on a given column has looked like for the last six months. Once it has a baseline, it flags the moments that fall outside it.
Bigeye differs from traditional data quality tools because users do not need to write validation rules for every table to protect them. Manual rule-based checking is effective for small sets of data, but it becomes difficult to maintain once a company has more than a few hundred tables. Bigeye uses machine learning to set thresholds automatically. These thresholds update as data patterns change. This automation is necessary for organisations that monitor thousands of tables rather than a small number.
Who it's built for
Bigeye is designed for data engineering and analytics teams that require continuous monitoring but lack the staff to build and maintain their own systems. The platform serves small data teams that manage expanding warehouses. Instead of writing validation suites for each table, users connect their warehouse and allow the platform to report its findings.
This software is a less effective choice if your organisation has a dedicated data quality engineering team that requires exact control over every check. In this situation, the automation provided by Bigeye may obstruct your workflow instead of improving it. A tool focused on manual rules provides more direct control for these users.
The maturity of a team affects the results of this process. A team without any existing monitoring will see a significant increase in visibility immediately, as any tool provides more information than they currently have. A team that already uses a refined, custom monitoring system built over several years may find that Bigeye's automation covers areas they have already addressed. For these teams, the primary benefit is reducing the time spent on maintenance rather than gaining new coverage.
Daily detection operations
Bigeye runs scheduled queries on your data warehouse to compare current data against a learned baseline. It sends an alert when data exceeds a threshold that you define. The system categorises issues by severity. This ensures that a small change in row counts does not trigger the same notification level as a table that has failed to update for three days. You can receive alerts through Slack, email, or PagerDuty. This allows your team to monitor data within their existing workflows. Users do not need to check a separate dashboard unless an issue occurs.
Important features
Bigeye provides three main functions: automatic data monitoring, lineage tracking to show the impact of issues, and manual checks for specific requirements. While other tools offer these features individually, Bigeye integrates all three into a single product.
Automated anomaly detection
Bigeye tracks data quality across every table you connect. It checks for freshness (whether data arrived as expected), volume (whether the number of rows is normal), schema (whether columns have disappeared or changed type), and distribution (whether the data's characteristics have shifted). You can set sensitivity levels for each of these checks. This prevents tables with normal fluctuations from generating too many alerts, while ensuring that sudden changes in stable tables are quickly flagged. Monitoring operates at both the table and column levels. This is important because many data problems affect only one field, not the entire dataset. We don't want to simply tell you "something in this forty-column table is wrong" without more specific information.
Column-level profiling and catching problems before they cascade
Column-level profiling takes place beneath table-level checks. The process involves monitoring null rates, unique value counts, and distribution shapes to spot problems early. For example, when a categorical field that normally has five values suddenly displays a sixth, it usually signals an undocumented upstream change. A null rate rising from two per cent to fifteen per cent over two weeks will seldom trigger a volume check since the row count stays stable. Nevertheless, this gradual issue degrades model training data and often remains unnoticed until accuracy drops. Column-level detail separates "the table updated fine" from "the table updated fine and something inside it's quietly wrong," offering a layer of oversight that manual spot-checks typically miss. Most people lack the time to examine every column on every table each day.
Data lineage and working out what a failure touches
When an alert is triggered, Bigeye's lineage mapping shows which downstream tables, dashboards, and models depend on it. This separates receiving the notice "table X looks odd" from knowing that "table X feeds the pricing model your sales team uses every morning, fix this first." Without this map, incident prioritisation relies on assumptions. With it, you can decide within minutes whether an anomaly is a small issue or demands immediate, widespread attention.
Identifying an incorrect number matters only when you know the person who uses that information.
Custom SQL metrics and dbt integration
Automated detection covers most requirements, yet some checks rely on specific business logic. These cover totals that cannot be negative, status fields limited to five specific values, or exact reconciliations between two systems. You can handle these cases by writing custom SQL metrics in Bigeye. These metrics execute on the same schedule as automated checks. If your team uses dbt for data tests, you can integrate them into Bigeye. This lets you manage alerting and incident tracking in a single location instead of using two separate tools.
How to run it in your stack
The setup takes hours rather than weeks, which is one of the truly useful aspects of the platform. You create a read-only service account for each warehouse you wish to monitor; Bigeye's documentation details the exact permissions required for Snowflake, BigQuery, Redshift, and the others, and after authentication the platform begins discovering tables and profiling data automatically. You do not rebuild pipelines or modify anything upstream. It reads the existing data.
What to connect first
Do not try to monitor every table right away. Start with the tables that support customer-facing features or supply data for AI models. You can broaden your coverage once your team understands how the alerts work. Tables that refresh hourly need different monitoring frequencies than those that refresh weekly. Bigeye lets you set these schedules for each individual table instead of applying one schedule to the whole system.
Adjusting sensitivity to maintain alert utility
You will typically need to tune a new source for two or three weeks after connecting it. Bigeye's baselines improve as the system monitors a table over time, so early alerts may be less accurate than later ones. You should not reduce the sensitivity right after a false positive. Give the system two weeks to learn the data patterns before making adjustments. Teams that skip this process often end up ignoring the platform or receiving too many alerts, both of which diminish the tool's value.
Where alerts should land
Send alerts to the channels your team already monitors rather than to a dashboard that needs manual checking. Integration with Slack and PagerDuty ensures anomalies appear alongside other incidents. These alerts contain information on severity, affected lineage, and history. This approach prevents data from remaining in a separate system where it could be overlooked.
How Bigeye compares to other options
There are many options in the data observability market. The answer to "which is best" depends on the existing skills of your team. Monte Carlo, Datafold, and Great Expectations are the competitors most frequently compared to Bigeye. Each of these tools requires different trade-offs.
Machine learning compared to explicit rules
Bigeye is based on the premise that automated baselines are more effective than manual rules when managing more than a few hundred tables. Great Expectations uses a different method where you write specific tests to define correctness for each dataset. This approach provides a level of precision that automation cannot achieve, but it requires you to write and update those tests as your schema changes. Automation is typically more effective for small teams managing a large number of tables. If you have enough engineers to maintain rules and you require specific control over alert triggers, Great Expectations is a viable alternative.
Scope and the focus of each tool
Monte Carlo covers more ground outside the warehouse, data lakes, streaming sources, BI tools, a broader net generally. Datafold's focus is narrower and sharper: it's built specifically for catching differences during deployments and migrations, comparing what a change actually did to your data before and after. Bigeye sits closer to the warehouse-first middle, strong on Snowflake, BigQuery, Redshift, and Databricks, solid dbt integration, less reach into streaming or lake-based sources than Monte Carlo offers. If your data lives mostly in a modern cloud warehouse, that's not a limitation you'll notice. If it doesn't, check the connector list before you commit.
What Bigeye costs
Bigeye does not provide a public price list. This is a standard practice for this type of software and does not indicate a problem with the product.
Pricing depends on the number of tables you monitor and the number of data sources you connect. A pilot programme involving fifty tables will cost significantly less than a full rollout of five thousand tables. Most vendors offer tiered plans where the basic level covers one data warehouse and a small number of important tables. Enterprise pricing applies when you require wider coverage, advanced lineage features, or dedicated support.
You should not create your budget based on marketing materials. Determine how many tables actually require monitoring instead of including every table in your system. You should obtain a quote based on that specific number before you finalise your rollout plan. Teams that do not follow this process often fail to provide enough resources and face shortages mid-year, or they pay for coverage on tables that no one uses.
Common reviewer feedback
User reviews of Bigeye show a consistent pattern. Customers frequently praise the product for the reasons mentioned in its marketing. Teams appreciate that they do not have to write rules immediately. They also report that alerts become more accurate as the platform learns the typical behaviour of each table. Reviews often mention positive experiences with support during the setup process. This is significant because a data observability tool is only useful if it is configured correctly.
Complaints regarding the platform show a consistent pattern. New users frequently notice that the system produces more noise than anticipated during the initial weeks as sensitivity levels are calibrated. This represents a tuning requirement, not a defect, though it may surprise teams that are unprepared. Pricing is also a common concern because costs scale with table count and can rise rapidly in large environments. You should request a quote based on your actual table count rather than budgeting using demo figures. Some reviewers from teams that use legacy or niche data sources have also noted that support is less comprehensive beyond the major data warehouses. These issues do not constitute disqualifying factors. They reflect the typical trade-offs for a platform that prioritises automation over manual configuration.
Target audience
These methods do not replace the fundamental tasks required.
A monitoring platform detects errors, yet it does not design your pipeline, define your business rules, or fix an unstable architecture. We spend most of our time at Shipshape Data making sure that the foundation monitored by a tool such as Bigeye is reliable before monitoring starts.
If you are evaluating data observability as part of a larger artificial intelligence project and need a clear assessment of whether your current data infrastructure can support it, talk to us before making any commitments.
If your team is seriously weighing this, our data consultancy will plainly tell you what it depends on and where to begin.
Frequently asked questions
What is Bigeye?
Bigeye is a cloud-based data observability platform. It connects to your data warehouse, such as Snowflake, BigQuery, Redshift, or Databricks, and analyses tables and columns to define normal data behaviour. Once this baseline is set, it detects anomalies in data freshness, volume, schema, and distribution. This occurs automatically, so you do not need to create validation rules for each table individually.
What features does Bigeye offer for data observability?
Bigeye's core features include automated anomaly detection for freshness, volume, schema, and distribution, column-level profiling that catches gradual issues such as a creeping null rate, lineage mapping that reveals downstream impacts of a failure, and custom SQL metrics for checks that are truly business-specific. Alerts are sent to Slack, email, or PagerDuty, rather than to a separate dashboard that is rarely checked.
How does Bigeye compare to Monte Carlo and Great Expectations?
Bigeye works on the premise that automated baselines are more effective than manual rules when handling more than a few hundred tables. In contrast, Great Expectations offers higher precision but demands that users write and maintain explicit tests. Monte Carlo monitors a broader range of infrastructure beyond the warehouse, whereas Datafold focuses on detecting differences during deployments and migrations. The suitable tool depends on where your team currently directs its effort.
How much does Bigeye cost?
Bigeye does not publish a standard price list. The cost varies with the number of tables you monitor and the number of sources you connect. A pilot programme for fifty tables will be priced differently than a rollout for five thousand tables. You should identify how many tables need monitoring before requesting a quote from the sales team. Do not base your budget on marketing materials.