Soda

Soda is a SQL/YAML-based data quality and observability platform designed for teams that need real-time monitoring of production data with minimal setup.

Reviewed by 7wData

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Soda is a SQL/YAML-based data quality and observability platform designed for teams that need real-time monitoring of production data with minimal setup. It targets data engineers and analytics engineers who work in SQL-heavy environments and want to catch data incidents before they reach dashboards or downstream consumers. The tool is also built for collaborative workflows, allowing engineers to manage checks in Git while business users interact through a UI, making it suitable for organizations that need both technical rigor and business visibility.

Soda uses a declarative YAML framework called SodaCL (Soda Check Language) to define data quality rules in plain language, similar to writing a SQL WHERE clause. Setup requires just two YAML files: one for data source connections and another for checks (e.g., `row_count > 0`). Its AI-powered anomaly detection in Soda 4.0 reduces false positives by 70% compared to Facebook Prophet and can scan 1 billion rows in 64 seconds. The platform supports built-in alerts to Slack, Microsoft Teams, PagerDuty, and ServiceNow, and offers record-level anomaly detection, backfilling, and backtesting for historical analysis.

Soda competes directly with Great Expectations, Monte Carlo, Anomalo, Sifflet, and Bigeye. Unlike Great Expectations, which is Python-based and requires more setup for production monitoring, Soda is easier to implement for SQL-centric teams and provides real-time alerts out of the box. However, it offers less flexibility for custom validation logic compared to Great Expectations. Against Monte Carlo and Anomalo, Soda differentiates with its open-source core (Soda Core) and emphasis on collaborative data contracts, but lacks the end-to-end lineage and automated root-cause analysis that those platforms provide.

Key trade-offs: Soda is primarily SQL/YAML-based, which limits flexibility for teams that prefer Python-driven validation. Its custom validation capabilities are narrower than Great Expectations, making it less suitable for complex, multi-step data transformations. The real-time monitoring features require the cloud version (Soda Cloud), as the open-source Soda Core lacks built-in alerting and AI capabilities. Additionally, while Soda's AI reduces false positives, it may still require tuning for highly volatile datasets, and the platform's documentation is less extensive than Great Expectations' rich "Data Docs".

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How it works

  1. SodaCL YAML checks

    Define data quality rules in plain YAML using Soda Check Language, similar to writing a SQL WHERE clause, with checks like `row_count > 0`.

  2. AI anomaly detection

    Soda 4.0 uses AI algorithms that reduce false positives by 70% compared to Facebook Prophet and can scan 1 billion rows in 64 seconds.

  3. Real-time alerts

    Sends instant alerts to Slack, Microsoft Teams, PagerDuty, or ServiceNow when data quality issues are detected during scans.

  4. Two-file setup

    Requires only two YAML files: one for data source configuration and another for defining checks, with installation via a single pip command.

  5. Collaborative workflows

    Engineers manage checks in Git with version control, while business users interact through the UI, with proposals and diffs visible in both views.

  6. Record-level anomaly detection

    Detects anomalies at the row level with high precision, supported by peer-reviewed research published in NeurIPS, JAIR, and ACML.

  7. Backfilling and backtesting

    Analyzes historical data instantly to reveal patterns and trends, with built-in backfilling and backtesting capabilities for one year of lookback.

Strengths and trade-offs

Strengths

  • Soda reduces false positives by 70% compared to Facebook Prophet using its AI anomaly detection algorithms.
  • Setup requires only two YAML files and a single pip command, making it one of the fastest data quality tools to deploy.
  • Built-in alerts integrate directly with Slack, Microsoft Teams, PagerDuty, and ServiceNow without additional configuration.
  • Soda can scan 1 billion rows in 64 seconds, enabling real-time monitoring of large-scale production datasets.

Trade-offs

  • Custom validation logic is limited compared to Great Expectations, which offers extensive Python-based flexibility.
  • The tool is primarily SQL/YAML-based, which may frustrate teams that prefer Python-driven data validation workflows.
  • Real-time monitoring and AI features require the cloud version (Soda Cloud), as the open-source Soda Core lacks these capabilities.
  • Documentation is less comprehensive than Great Expectations' rich 'Data Docs', making it harder to generate detailed data lineage reports.

Pricing context

Pricing is not publicly listed; Soda offers a free open-source version (Soda Core) and a paid cloud tier (Soda Cloud) with additional features like AI anomaly detection and real-time alerts. Contact sales for enterprise pricing.

Getting started with Soda

  1. Install Soda Core

    Run `pip install soda-core` in your terminal to install the open-source Soda Core library. This command sets up the base tool for defining and running data quality checks on your data sources.

  2. Create configuration YAML

    Create a YAML file (e.g., `configuration.yml`) to define your data source connection. Include the type, host, port, database name, and credentials for your database, such as PostgreSQL or Snowflake.

  3. Write a SodaCL check

    Create a second YAML file (e.g., `checks.yml`) with a data quality rule using SodaCL. For example, add `checks for orders: - row_count > 0` to ensure the orders table has at least one row.

  4. Run your first scan

    Execute `soda scan -d your_datasource -c configuration.yml checks.yml` in your terminal. This command runs the defined checks against your data source and outputs pass/fail results for each rule.

  5. Set up real-time alerts

    Sign up for Soda Cloud and connect your data source. In the UI, configure alert integrations for Slack or PagerDuty, then schedule scans to run automatically and notify your team when checks fail.

Frequently Asked Questions

What is Soda and how does it help with data quality?

Soda is a SQL/YAML-based data quality and observability platform. It helps data engineers monitor production data in real time, catch incidents before they reach dashboards, and collaborate via Git and a UI. It uses declarative YAML checks called SodaCL.

How do you set up Soda for data quality monitoring?

Setup requires just two YAML files: one for data source connections and another for checks like row_count > 0. Installation is a single pip command. This makes Soda one of the fastest data quality tools to deploy for SQL-centric teams.

What is SodaCL and how does it work?

SodaCL is Soda's declarative YAML framework for defining data quality rules. You write checks in plain language similar to a SQL WHERE clause, such as row_count > 0. It allows engineers to manage checks in Git while business users view results in the UI.

How does Soda's AI anomaly detection reduce false positives?

Soda 4.0's AI algorithms reduce false positives by 70% compared to Facebook Prophet. It can scan 1 billion rows in 64 seconds and supports record-level anomaly detection with backfilling and backtesting for historical analysis, though tuning may be needed for volatile datasets.

What are the main differences between Soda Core and Soda Cloud?

Soda Core is a free open-source version with basic monitoring. Soda Cloud adds real-time alerts to Slack, Teams, PagerDuty, and ServiceNow, plus AI anomaly detection and collaborative workflows. Real-time monitoring and AI features require the cloud version.

How does Soda compare to Great Expectations and Monte Carlo?

Soda is easier for SQL-centric teams with YAML-based setup and real-time alerts, but offers less custom validation than Great Expectations. Against Monte Carlo, Soda has an open-source core and data contracts, but lacks end-to-end lineage and automated root-cause analysis.

Alternatives

How Soda compares

Direct head-to-head against 3 competitors. Picked by 7wData.

This tool

Soda

Pricing
Pricing is not publicly listed; Soda offers a free open-source version (Soda Core) and a paid cloud tier (Soda Cloud) with additional features like AI anomaly detection and real-time alerts. Contact sales for enterprise pricing.
Target
Soda is a SQL/YAML-based data quality and observability platform designed for teams that need real-time monitoring of production data with minimal setup.
Strength
Soda reduces false positives by 70% compared to Facebook Prophet using its AI anomaly detection algorithms.
Watch for
Custom validation logic is limited compared to Great Expectations, which offers extensive Python-based flexibility.

Olipop

Pricing
$2.99-$3.99 per can (12oz)
Target
Health-conscious consumers seeking gut-friendly sodas
Deployment
Retail, DTC
Strength
Prebiotic fiber formulations with clinically studied ingredients
Watch for
Limited flavor variety in some regions

Poppi

Pricing
$2.49-$2.99 per can (12oz)
Target
Millennials/Gen Z seeking functional beverages
Deployment
National retailers
Strength
Apple cider vinegar base with prebiotics
Watch for
Acquired by CAVU Venture Partners (2023)

Zevia

Pricing
$5.99-$6.99 per 6-pack (12oz)
Target
Zero-calorie soda alternative seekers
Deployment
Grocery/online
Strength
Stevia-sweetened with 40+ flavor options
Watch for
Aftertaste complaints in some flavors

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Sources

Reporting on this tool draws on these publicly available sources.

  1. www.dataexpert.io
  2. soda.io
  3. www.synq.io
  4. www.reddit.com