Top 10 Soda Alternatives & Competitors

(55)4.4 out of 5

The Data Observability Software solutions below are the most common alternatives that users and reviewers compare with Soda. Data Observability Software is a widely used technology, and many people are seeking sophisticated, productive software solutions with ai text generation. Other important factors to consider when researching alternatives to Soda include integration and user interface. The best overall Soda alternative is Monte Carlo. Other similar apps like Soda are Great Expectations, dbt, Demandbase One, and SAS Viya. Soda alternatives can be found in Data Observability Software but may also be in Account Data Management Software or Data Quality Tools.

Show Less

Best Paid & Free Alternatives to Soda

Monte Carlo
Great Expectations
dbt
Demandbase One
SAS Viya
Planhat
HubSpot Data Hub
ZoomInfo Operations

Top 10 Alternatives to Soda Recently Reviewed By G2 Community

Browse options below. Based on reviewer data, you can see how Soda stacks up to the competition, check reviews from current & previous users in industries like Information Technology and Services, Financial Services, and Online Media, and find the best product for your business.

Monte Carlo

By Monte Carlo

4.4/5

(462)

Product Description

Reviewers say compared to Soda, Monte Carlo is:

Slower to reach roi

Easier to do business with

Better at support

Categories in common with Soda:

Data Quality

Data Observability

Great Expectations

By Great Expectations

4.5/5

(11)

Product Description

Great Expectations (GX is an open-source data validation framework designed to help data teams ensure the quality and reliability of their data. By defining "Expectations"—verifiable assertions about data—GX enables automated testing and documentation, fostering confidence in data pipelines and facilitating collaboration between technical and non-technical stakeholders. Key Features and Functionality: - Expectations: Define clear, human-readable assertions about your data, such as value ranges or data types, to validate data quality. - Automated Data Profiling: Analyze and summarize data characteristics automatically, aiding in the quick identification of potential quality issues. - Data Validation: Apply defined Expectations to data batches to verify compliance, receiving detailed reports on validation outcomes. - Data Docs: Generate comprehensive, human-readable documentation of Expectations and validation results, serving as an up-to-date data quality report. - Integration with Various Data Sources: Support for multiple data sources, including Pandas DataFrames, Spark DataFrames, and SQL databases, allowing flexibility in data validation processes. - Checkpoints: Create reusable validation workflows that specify which Expectations to run against which data assets, streamlining the validation process. Primary Value and Problem Solved: Great Expectations addresses the critical need for data quality assurance in modern data pipelines. By automating data validation and providing clear documentation, GX reduces manual effort, minimizes errors, and ensures that data meets predefined standards. This leads to more reliable data for analysis and decision-making, enhances collaboration between data teams and business stakeholders, and fosters a culture of data confidence within organizations.

Reviewers say compared to Soda, Great Expectations is:

More expensive

Easier to set up

More usable

Categories in common with Soda:

Data Quality

Data Observability