RightData RightSight vs Soda: Continuous Data Health Checks & Data Quality Compared

If you are comparing RightData RightSight vs Soda for continuous data health checks, you are choosing between two very different products: an enterprise, no-code platform and a code-first, open source core with a cloud layer on top. Both monitor data quality; they suit different teams.

This comparison covers what each tool is, how checks are written, continuous monitoring features, pricing model, and which one fits your team. At the end: what it means for testers building a career in data quality.

Quick Verdict

What Is RightData RightSight?

RightData sells a data quality and governance platform made of several products. DataTrust handles data validation and reconciliation (the classic ETL testing job: compare source and target, flag mismatches). RightSight is the observability layer: it watches data health continuously and alerts on freshness delays, volume drops, schema or distribution drift and anomalies, and it adds metadata and lineage context. Rules for completeness, accuracy, consistency, timeliness and validity can be picked from a library or built in the UI.

• Strengths: no-code, one vendor for testing + observability + governance, history of every rule run kept for audit.
• Limits: commercial licence only, enterprise sales cycle, less natural to keep checks as code in Git.

What Is Soda?

Soda Core is an open source command-line tool that runs data quality checks written in SodaCL, a readable YAML language. Soda Cloud adds a web UI, scheduling, anomaly detection, alerting, dashboards and data contracts on top, with a free tier and paid plans.

checks for dim_customer:
  - row_count > 0
  - missing_count(email) = 0
  - duplicate_count(customer_id) = 0
  - invalid_percent(phone) < 1%:
      valid format: phone number
  - freshness(updated_at) < 1d

• Strengths: free to start, checks live in Git next to the pipeline, easy to run from Airflow, dbt or CI/CD, readable by non-developers.
• Limits: many monitoring features need Soda Cloud; cross-database reconciliation is not its main job, so ETL teams often pair it with SQL or a reconciliation tool.

Continuous Data Health Checks: Head to Head

"Continuous data health checks" means checks that run on every load or on a schedule, without someone remembering to run them. The five checks every data health setup needs, and how each tool handles them:

The checks are the same ideas in both tools. A tester who can write them in SQL can configure either product in a day. Example SQL for each: data quality checks.

The same five health checks in plain SQL

This is what both tools run underneath. Each query should return no rows (or a value within the threshold); schedule them after every load and you have a basic continuous health check with no licence at all.

-- 1. Freshness: did today's load arrive?
SELECT MAX(updated_at) AS last_load
FROM dim_customer
HAVING MAX(updated_at) < CURRENT_TIMESTAMP - INTERVAL '1' DAY;

-- 2. Volume: today's rows vs the 7-day average
SELECT today.cnt, avg7.cnt
FROM (SELECT COUNT(*) cnt FROM fact_orders
      WHERE load_date = CURRENT_DATE) today,
     (SELECT COUNT(*) / 7.0 cnt FROM fact_orders
      WHERE load_date >= CURRENT_DATE - 7
        AND load_date < CURRENT_DATE) avg7
WHERE today.cnt < avg7.cnt * 0.5;

-- 3. Completeness
SELECT customer_id FROM dim_customer WHERE email IS NULL;

-- 4. Uniqueness
SELECT customer_id FROM dim_customer
GROUP BY customer_id HAVING COUNT(*) > 1;

-- 5. Schema drift: compare columns with yesterday's snapshot
SELECT column_name, data_type FROM information_schema.columns
WHERE table_name = 'dim_customer'
EXCEPT
SELECT column_name, data_type FROM schema_snapshot
WHERE table_name = 'dim_customer';

The date syntax differs slightly between databases (Snowflake, BigQuery, SQL Server). What RightSight and Soda Cloud add on top is history, smarter anomaly thresholds, dashboards and alert routing. More queries: 15 SQL queries for ETL testing.

How Each Tool Fits Into a Data Pipeline

Soda sits inside the pipeline. A typical Airflow DAG runs: extract → load → dbt transform → soda scan → publish. If a blocking check fails, the scan returns a failure and the publish step never runs, so bad data does not reach dashboards. The check files are reviewed in pull requests like any other code.

RightData RightSight sits beside the pipeline. It connects to your warehouses and lakes, profiles the data, learns normal patterns and monitors continuously, alerting owners when freshness, volume or distributions drift. Rules are managed in the platform, and results feed governance reporting. DataTrust can run reconciliation jobs between source and target as a separate step.

In practice: Soda is closer to "tests in CI/CD", RightSight is closer to "monitoring with a data quality dashboard". Many enterprises run both styles, test gates in the pipeline plus observability across the estate.

Setting Up a Soda Health Check: Quick Walkthrough

To see how quickly Soda gets running, here is the basic Soda Core flow (v3 syntax; check Soda's docs for the latest version and connector names):

1. Install the package for your database, for example pip install soda-core-postgres.
2. Configure the connection in configuration.yml (host, database, user; keep the password in an environment variable).
3. Write checks in checks.yml using SodaCL, like the example above.
4. Run a scan: soda scan -d my_datasource -c configuration.yml checks.yml. The exit code tells your scheduler or CI job whether checks passed.
5. Schedule it after each load in Airflow, Azure Data Factory or a cron job, and optionally connect Soda Cloud for history and alerts.

RightData has no equivalent self-service install; setup goes through the vendor, usually starting with a demo and a proof of concept on your own data.

RightData vs Soda for Data Quality

• Who writes the checks: RightData targets business analysts, data stewards and QA teams through a UI. Soda targets engineers and analytics engineers through code.
• Where results live: RightData keeps rule history and audit evidence in the platform. Soda Core prints results; Soda Cloud stores history and dashboards.
• Reconciliation: if your main job is proving source equals target after a migration, RightData (DataTrust) or a tool like QuerySurge is closer to the task. Soda is stronger at monitoring a dataset's own quality.
• Cost to try: Soda can be running in an hour for free. RightData needs a vendor demo.

Pros and Cons at a Glance

Neither is "better" in general. The deciding factors are who will write and maintain the checks, whether you need reconciliation between systems or monitoring of one system, and your budget.

Which One Should You Choose?

• Choose RightData RightSight if you are an enterprise with governance requirements, many non-technical users, and budget for a single platform covering testing, observability and cataloguing.
• Choose Soda if your team already uses Git, Airflow or dbt, wants to start free, and is comfortable keeping checks as code.
• Use both ideas without either tool if you are learning: SQL + pytest reproduces every check above for free. See 10 data testing tools compared.

Questions to Ask in a Vendor Demo

Whichever tool you shortlist, these questions separate a good fit from a nice demo:

• Can it compare data across two different databases (for example Oracle source vs Snowflake target), or only check one dataset?
• Can checks be stored in Git and promoted from dev to production?
• How are anomaly thresholds set, and how do we stop false alerts on weekends and month-end?
• Which checks run inside our warehouse (pushdown SQL) and which copy data out? This affects cost and security.
• How does it fail a pipeline: API, exit code, Airflow operator?
• What does pricing scale with: users, tables, data sources or rows scanned?

Alternatives to RightData and Soda

Full comparison: 10 data testing tools · ETL-focused: ETL automation testing tools.

What This Means for Testers

Job descriptions for ETL testers and data quality analysts increasingly mention observability tools like these. Recruiters rarely expect you to know a specific vendor; they test whether you know what to check and can prove it with SQL. Learn the method (reconciliation, data quality dimensions, defect reporting) and any of these tools becomes a configuration exercise.

Related: data testing tools · ETL testing tools · Great Expectations tutorial · data reconciliation · Start Here

Frequently Asked Questions

What is the difference between RightData RightSight and Soda?

RightSight is an enterprise, no-code data observability product from RightData that monitors freshness, volume, drift and anomalies. Soda is an open source data quality tool (Soda Core) where checks are written in YAML, with Soda Cloud adding monitoring, alerts and dashboards. RightData suits governance-heavy enterprises; Soda suits engineering teams who keep checks as code.

Is Soda free?

Soda Core is free and open source. Soda Cloud has a free tier and paid plans; features such as dashboards, alerting and anomaly detection are part of Soda Cloud. Check Soda's pricing page for current plans.

What are continuous data health checks?

They are data quality checks that run automatically on every load or on a schedule: freshness, volume, completeness, uniqueness and schema or distribution drift. Failures trigger alerts so teams find bad data before users do.

Can RightData or Soda replace ETL testing?

Not fully. They automate data quality and monitoring checks, but source-to-target reconciliation, transformation logic testing and defect analysis still need SQL skills and a tester who understands the business rules.

Do I need to learn RightData or Soda to get an ETL testing job?

No. Most ETL testing jobs ask for SQL, data warehouse concepts and testing method first. Tool knowledge is a bonus and can be learned on the job in days.

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Written by

Asim Noaman Lodhi

Certified Google Partner · QA Consultant · 12+ Years IT

QA consultant specializing in ETL testing and data quality. Trained 913+ students to transition into data testing roles through hands-on, real-world instruction.

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