Feb 16, 2024
Step-by-Step Guide to Selecting a Data Quality Monitoring Solution in 2024
- ML Monitoring
Feb 16, 2024
In a case that Unity reported, data quality issues were responsible for an estimated $110 million loss in 2022, underscoring the importance of thorough data quality monitoring for even the most successful companies. This highlights a fundamental truth today: bad data is bad business. Whether it's misleading analytics, flawed customer insights, or operational snags, poor data quality can derail business strategies and erode trust.
This article series is dedicated to helping you choose an ideal data quality monitoring tool. As we've explored previously, the key to maintaining high-quality data is continuous monitoring for issues across your data ecosystem. The focus here is not just on any tool but on those developed explicitly for data quality monitoring.
By the end of this article, you'll gain insights into:
- What an ideal data quality monitoring solution looks like: Characteristics that define top-tier data quality tools.
- Exploring tools: Learn about several open-source and SaaS data quality monitoring tools and understand their pros and cons.
- Decision-making: Guidance on building or buying a data quality monitoring tool tailored to your requirements.
With many tools available on the market, let's navigate the landscape of data quality monitoring solutions to empower you with the knowledge to make informed decisions. But first, what does an ideal solution look like?
Dimensions of an Ideal Data Quality Monitoring Solution
What should an ideal monitoring solution have? First, take a page from the DevOps criteria for good observability tools.
Certain characteristics are essential for an optimal data quality monitoring solution:
Self-serve
It must be user-friendly, with smart defaults and ML-enhanced suggestions. The ideal tool minimizes setup and maintenance burdens while empowering your team with intuitive interfaces and automated service discovery.
Dynamic
Scalability and adaptability are key. The tool must scale to increasing data volume and cardinality, supporting whatever architecture your stack is built on, and enabling custom data quality indicators (DQIs) for unique business needs.
Collaborative
The tool should be collaborative because it provides insights for data, ML teams, and organizations—not just data engineers. You should be able to share data, insights, dashboards, and reports with others via Slack channels (or other team chats), accurate alerting, and coordinated action across roles.
Holistic
The tool should provide a comprehensive view of data lineage and health, from the data producer to the data transformation process and the consumer. You should also be able to observe the health of the overall data stack to pinpoint failure modes that may affect data quality.
Automatable
The tool should support extensive automation and workflows to transform insights into immediate action through scripts and real-time analysis with little to no manual intervention.
Privacy-preserving
With robust security measures and compliance with global standards, the tool should safeguard data integrity and confidentiality against external attacks to protect your data for trust and compliance.
Change-aware
The tool should be adaptive to the evolving data landscape, capturing snapshots of your data's statistical profile pre- and post-transformation—source control management. For example, what was the schema before a transformation change at a specific time?
Now that you have understood what an ideal solution should be, let’s explore considerations to assess the build vs. buy strategy so that the subsequent tooling sections are meaningful and easier to navigate.
Assessing Your Build vs. Buy Strategy for Data Quality Monitoring Tools
Deciding whether to build in-house or purchase a data quality monitoring tool is a significant decision that hinges on understanding your organization's unique needs, capabilities, and constraints. Consider the following to guide your choice:
- Business requirements:
- Evaluate the complexity of your business problems against the capabilities of existing solutions. Assess how well they align with your data SLAs, SLOs, and SLIs.
- Technical requirements:
- Gauge your technical infrastructure and team's capabilities. Consider building custom solutions if off-the-shelf tools don't integrate well or meet compliance standards.
- Organization size:
- Larger organizations might have the resources to build bespoke solutions, while smaller ones might benefit more from buying, using open-source tooling, or adopting hybrid solutions.
- Cost-benefit analysis:
- A thorough cost-benefit analysis will help quantify the decision financially. This analysis should consider not just the immediate costs but also the ongoing costs (maintenance vs. recurring subscription fees), intangible costs (longer development time vs. external dependencies), and benefits (customization vs. quick implementation).
- Calculate the ROI for both scenarios by estimating the value the tool will bring to your organization regarding increased efficiency, reduced errors, and other benefits. Compare this to the total cost over a set period (usually a few years).
- Implementation timeline:
- Factor in the urgency of implementation. Existing solutions can offer quicker deployment while building custom solutions might provide a better long-term fit.
- Risk assessment:
- Assess the associated risks with both building and buying, including potential obsolescence, ongoing support and maintenance challenges, integration complexities, or the possibility that the tool may not fully meet all evolving business requirements.
- Consider the long-term implications of each approach to resource allocation, adaptability to change, and the ability to stay ahead in technology and compliance standards.
Before deciding, consider seeking case studies or consulting with peers who have faced similar decisions. Understand the total cost of ownership (TCO) for both options, including long-term maintenance and support. Risk assessment and a clear market landscape view will also inform a well-rounded decision.
Whether you build, buy, or combine both approaches, consider your requirements as we explore open-source and paid options that may align with your strategic objectives, operational capabilities, and growth trajectory.
Data Quality Monitoring Tools
This section covers open-source and software-as-a-service (SaaS) tools specifically tailored for data quality monitoring, including solutions that extend into profiling, logging, and testing.
Data Quality Monitoring Open-Source Tooling
- whylogs: Lightweight tool for logging and understanding profiles of different data types.
- Pandera: Specializes in statistical data validation to define, enforce, and document data quality expectations.
- Great Expectations: Comes with a comprehensive suite of features for data testing, documentation, and profiling through a declarative framework.
- Deequ: Built on top of Apache Spark for defining 'unit tests' for data, particularly effective in large-scale data processing scenarios.
- Elementary OSS: Focused on continuous data observability to provide automated insights into data quality and anomaly detection.
Each tool offers unique advantages depending on your data environment, strategy, and quality objectives, whether you're looking to deploy on-premises or leverage cloud offerings.
Let’s take a closer look at these solutions.
whylogs
whylogs stands out as a tool for logging, testing, and monitoring data or ML applications, all while ensuring data privacy within your environment. It ensures comprehensive yet efficient data understanding by creating statistical summaries of datasets called profiles.
Key properties of whylogs profiles
whylogs profiles have three properties that make them ideal for data logging and monitoring:
- Descriptive: Provides a detailed statistical summary of your data for deeper insights.
- Lightweight: Ensures minimal memory usage, scaling elegantly with the input features.
- Mergeable: Profiles can be combined to aggregate statistics across datasets and timeframes.
Key features of whylogs
- Accurate data profiling: With 100% data consideration, it offers precise statistical calculations of your data distributions without sampling.
- Minimal runtime impact: Uses approximate statistics to maintain a small memory footprint—essential for large-scale or feature-rich datasets.
- Universal compatibility: Adapts to any architecture and scales from local setups to extensive multi-node clusters, supporting batch and streaming data.
- Configuration-free: Automatically infers data schema for immediate, configuration-free setup.
- Compact storage: Efficiently reduces data to statistical fingerprints, 0-100MB uncompressed, which saves on storage while retaining critical information.
- Extensive metrics: It collects comprehensive metrics from structured and unstructured data to provide extensive statistical visualizations.
Supported by Python and Spark APIs, whylogs integrates effortlessly into various environments so that teams can adopt it with minimal disruption. For a hands-on understanding, check the examples folder.
Pandera: Dataframe validation and testing
Pandera is a dataframe data validation and testing tool that is lightweight and adaptable for projects of any scale. It allows you to keep track of the quality of your data by monitoring it regularly and running statistical validation tests.
import pandera as pa
from pandera import Column, Check, DataFrameSchema
price_check = pa.DataFrameSchema ({
“price”: Column(pa.Float,Check.in_range(min_value=5,max_value=10)),
})
price_check.validate(df)
Key features of Pandera
- Self-service and lightweight: Designed for immediate use with a user-friendly approach to data validation.
- Dynamic and flexible API: Supports various DataFrame types, including Dask and Koalas for scaling to any project size. Its integration with a rich ecosystem of Python tools such as Pydantic, Fastapi, and MyPy expands its functionality and adaptability.
- Automatable: Integrates with existing data pipelines for automated validation checks through function decorators to improve reliability.
- Customizable checks: Beyond common data validation checks, you can register custom checks tailored to your specific data scenarios for a comprehensive and bespoke validation process.
Explore more features in the documentation.
Great Expectations (GX)
Great Expectations is a comprehensive solution for validating, documenting, and profiling your data to ensure quality. With a robust and scalable design, GX is perfect for larger projects and complex data systems.
GX allows you to write declarative data tests based on what you expect from the data, get validation results from those tests, and create a report that documents the current state of your data.
Key features of Great Expectations
- Self-service and production-ready: With smart defaults and a focus on ready-to-use validation, GX integrates into your data pipelines to reduce the learning curve.
- Dynamic and interoperable: GX is designed to be interoperable with various data tools and stacks. Writing assertions (known as Expectations) becomes an intuitive process to validate the quality of your data and detect when there are issues.
- Collaborative documentation: Unique to GX, transform your data quality tests into comprehensive documentation, bridging communication gaps and aligning team understanding of data health and standards.
Learn more about Great Expectations by using the documentation.
Deequ
Deequ is a library built on Apache Spark, enabling robust "unit tests for data" in large datasets. It is originally an innovative creation from Amazon Labs. whylogs is based on a lot of the work done by the Deequ team at AWS. To use Deequ with Python, PyDeequ provides an open-source Python wrapper over Deequ.
Key features of Deequ
- Extensive range of data quality indicators: Deequ simplifies implementing data quality checks with an extensive selection of indicators to make it easier to measure and maintain the data quality in Spark and Python pipelines.
- Dynamic scalability: Deequ is purpose-built for monitoring and testing data quality issues at scale even as data volumes grow.
- Holistic quality reports: It generates comprehensive reports detailing the status of each data quality constraint for visibility into your data's health.
- Automatable with PyDeequ: Automate your data quality processes with core APIs for efficient workflows.
Elementary
A tool architected to streamline and improve the ability of data and analytics engineers to monitor and manage data pipelines directly within their dbt projects. It provides an integrated experience, combining the power of dbt with advanced observability to ensure data health and performance.
Key features of Elementary
- Self-service: The pre-built dashboards offer immediate insights, while Configuration-as-Code via YAML facilitates easy tracking and changes for accessible user experience.
- Dynamic and dbt-native: Natively integrates with your dbt projects and offers versatile data source support, including Snowflake, BigQuery, Redshift, Databricks, and Postgres. This ensures frictionless support for your data management workflow. There are two deployment options: the open-source Elementary CLI for self-hosted deployment and the Elementary Cloud service for a managed solution.
- Rich data lineage visualization: The data lineage features offer detailed insights into data sources, flows, and impacts to improve tracing and troubleshooting capabilities.
- Holistic observability: Shows you the column-level lineage and enriched data issue insights. It can monitor data pipelines, detect issues, send alerts, and provide a comprehensive dashboard of your data health, performance, and quality.
Here’s a guideline showing how to install the Elementary dbt package.
Open Source Data Quality Monitoring Tools Comparison (2024)
Data Quality Monitoring Software-as-a-Service (SaaS) Tooling
The SaaS solutions vary in pricing and instrumentation of the data system—how they get data for monitoring, including metrics they can monitor.
- WhyLabs: AI observability platform for monitoring data pipelines and ML applications
- Metaplane: End-to-end data observability platform.
- Monte Carlo: Scalable data reliability platform.
- Soda Cloud: Platform to test and monitor data as-code in CI/CD and data pipelines.
- IBM® Databand®: Data observability within IBM Cloud.
WhyLabs
WhyLabs is an observability platform that monitors data pipelines and ML applications for data quality regressions, data drift, and model performance degradation. It is built on top of whylogs. Once whylogs profiles your data, the library outputs can be used to test, monitor, and debug data on the WhyLabs data health monitoring platform.
Key features of WhyLabs
- Rapid self-service setup: Get started in minutes with quick implementation and minimal learning curve.
- Comprehensive data profiling: Upload data profiles to WhyLabs for centralized monitoring and alerting of model inputs, outputs, and performance metrics—a thorough oversight of your data health.
- Scalable and dynamic: Efficiently handles large-scale data. It integrates smoothly with both batch and streaming data pipelines, maintaining low compute requirements while scaling with your data needs.
- Enhanced collaboration: Share insights and receive real-time alerts on data quality issues through a rich, collaborative dashboard. Authorized team members can access controlled data views for a unified approach to data quality management.
- Holistic insights and data lineage: Trace the lineage and health of your data for a comprehensive understanding of your data ecosystem.
- Flexible automation: Engage with WhyLabs programmatically through an API to automate interactions and integrate with your existing data stack.
- Privacy-preserving: Prioritizes data privacy, capturing only statistical properties and ensuring that sensitive raw data remains within your environment with SOC 2 Type 2 compliance.
Cost analysis
WhyLabs provides three primary pricing plans:
- Starter (free) - Ideal for individuals and small teams with limited data volume and basic monitoring needs.
- Expert ($125/month) - Ideal for small and growing teams with moderate data volume and increased monitoring needs.
- Enterprise (Custom Pricing) - Ideal for large enterprises with high data volumes and complex monitoring requirements.
WhyLabs is built around monitoring data in motion, which sets it apart from solutions focused on static data. Try the platform for free with sample datasets through the sandbox.
Metaplane
Metaplane collects metrics, metadata, lineage, and logs on your data, trains anomaly detection models on historical values, and then sends you alerts for outliers with the opportunity to provide model feedback in cases of false positives.
Key features of Metaplane
- Self-service: User-friendly setup process with no-code data validation tests.
- Dynamic integration: Integrates with every part of the modern data stack, from data warehouses to visualization tools.
- Enhanced collaboration: Comes with real-time alerts on data issues through popular channels like Slack, PagerDuty, and email to address and resolve data quality issues quickly.
- Holistic data view: Links the intricate web between your data sources and the dashboards stakeholders rely on for a clear, actionable view of data health.
Cost analysis
Metaplane pricing plans:
- Free plan - Ideal for individuals or small teams, starting with data quality monitoring.
- Pro ($1,249/month) - Ideal for growing teams or startups with moderate data volume and need for basic data quality insights.
- Enterprise (custom pricing) - Ideal for data teams in the critical path who require enterprise-calibre support.
Learn more about Metaplane in the documentation.
Monte Carlo
Monte Carlo provides monitoring and altering solutions for data quality issues affecting your data system. It’s one of the most feature-rich data observability tools on the market.
Key features of Monte Carlo
- Self-service setup: Quickly configure and start with smart defaults and ML-powered incident monitoring.
- Dynamic: Grows and adapts with your data stack for various organizational sizes and types.
- Collaboration: Code-free integration with data stacks and collaboration across teams.
- Holistic: Total oversight of your data assets.
- Automatability: Use programmatic interfaces, including APIs, SDKs, CLIs, and custom YAML monitors for workflow automation.
- Robust privacy protection: Sensitive data remains secure and private because data is mapped at rest.
Cost analysis
Monte Carlo's pricing is not publicly available and requires requesting a custom quote based on your specific needs. However, Monte Carlo offers three pricing plans:
- Start (pay per table up to 1,000 tables and 10,000 API calls per day) - Ideal for a small team of up to 10 users.
- Scale (pay per table and 50,000 API calls per day) - Ideal for teams of any size and scale.
- Enterprise (pay per table and 100,000 API calls per day) - Ideal for teams of any size and scale with unlimited users and scaling and a 24 hour support SLA.
Check out the developer hub to learn more.
Soda Cloud
Soda Cloud leverages Soda SQL, a free, open-source command-line program and Python module that uses user-defined inputs to generate SQL queries that analyze datasets in a data source for data quality issues, including incorrect, missing, or unexpected data.
Key features of Soda Cloud
- Self-service: Although working with YAML configurations in Soda SQL may present a learning curve, the overall setup experience is designed for ease.
- Dynamic: Soda Cloud scales to accommodate large datasets and integrates with various data stacks.
- Collaboration: Foster a collaborative environment with role-based alerts and monitoring capabilities to enable your team to manage data quality.
- Holistic: Gain a holistic view of your data's health with visualizations, historical measurements, and timely alerts.
- Automatable: Automatically helps you detect anomalies, and with YAML configs, you can automate data validation with code.
Cost analysis
Soda.io offers only one paid plan, with a free trial to fully test their platform for 45 days.
Learn more about how Soda Cloud can align with your data strategy by visiting their documentation.
IBM® Databand®
Databand is an IBM Data Observability Platform that works even when you can’t control your sources. You can orchestrate your data pipelines with this solution and self-host them. It is an offering from IBM Cloud.
Key features of IBM® Databand®
- Self-service and collaboration: Configure and collaborate without hassle with no-code and programmatic options for tracking data assets and working with teams.
- Dynamic: Integrates with standard data tools.
- Holistic: Tracks data lineage end-to-end, giving you a holistic view of your data.
- Dedicated to data privacy: Exercise control over what data assets are monitored, ensuring sensitive data remains protected.
Costs analysis
The prices are in three tiers (Growth, Pro, and Enterprise), requiring you to request a quote to get the actual price.
- Growth plan (monitor <100 pipelines and hundreds of tables) - Ideal for small teams or projects.
- Pro plan (monitor hundreds of pipelines and thousands of tables) - Ideal for larger teams or projects.
- Enterprise plan (monitor thousands of pipelines and tables) - Ideal for enterprise-level teams or projects.
Check out the website to learn more about how you can get started.
SaaS Data Quality Monitoring Tools Comparison (2024)
Choosing the Right Data Quality Monitoring Solution: Key Takeaways
Selecting the right data quality monitoring solution can be overwhelming, but this guide has highlighted the critical factors to help with decision-making. Here are a few practices to remember:
- Find a solution that matches your needs with features that address your unique data challenges, such as real-time anomaly detection, lineage tracking, or seamless integration, is essential.
- Be proactive in monitoring data quality—data monitoring should start when you source your data and continue when you deploy your models.
These practices will unlock new data confidence for more robust data systems and improved model performance.
Next steps? Identify critical data needs and pain points, evaluate options using the provided framework, engage with vendors, ask questions, and demand tailored solutions, as well as pilot and iterate for a data-driven approach. Remember, quality data is a continuous journey, not a destination.
Other links
- Try WhyLabs’ free self-serve data monitoring platform— https://whylabs.ai/free.
- Join the WhyLabs Slack community to discuss ideas and share data logging and observability feedback.
References and Resources
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