Google Cloud Datalab
An easy-to-use interactive tool for data exploration, analysis, visualization, and machine learning. Cloud Datalab is a powerful interactive tool created to explore, analyze, transform, and visualize data and build machine learning models on Google Cloud Platform. It runs on Compute Engine and connects to multiple cloud services easily so you can focus on your data science tasks. Cloud Datalab is built on Jupyter (formerly IPython), which boasts a thriving ecosystem of modules and a robust knowledge base. Cloud Datalab enables analysis of your data on BigQuery, AI Platform, Compute Engine, and Cloud Storage using Python, SQL, and JavaScript (for BigQuery user-defined functions). Whether you're analyzing megabytes or terabytes, Cloud Datalab has you covered. Query terabytes of data in BigQuery, run local analysis on sampled data, and run training jobs on terabytes of data in AI Platform seamlessly.
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Domo
Domo’s AI and data platform business is now part of Progress Software. Domo's cloud-native AI data readiness platform complements and significantly broadens Progress' data platform offerings, creating powerful synergies to deliver innovative, secure and scalable AI data readiness solutions worldwide. Together, these capabilities will help customers turn fragmented enterprise data and knowledge into governed, AI-ready intelligence, improving the security, governance and cost of AI-powered initiatives.
Domo is the agentic platform for the intelligent enterprise, helping organizations connect, govern, activate, and distribute data and AI. Domo works with cloud data platforms including Snowflake, BigQuery, and Databricks to turn governed data into AI agents, apps, workflows, dashboards, and analytics. Its data foundation, activation, and distribution layers help teams build and deliver intelligence where work happens, with governance, security, and access controls across data and AI.
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Dataform
Dataform enables data analysts and data engineers to develop and operationalize scalable data transformation pipelines in BigQuery using only SQL from a single, unified environment. Its open source core language lets teams define table schemas, configure dependencies, add column descriptions, and set up data quality assertions within a shared code repository while applying software development best practices, version control, environments, testing, and documentation. A fully managed, serverless orchestration layer automatically handles workflow dependencies, tracks lineage, and executes SQL pipelines on demand or via schedules in Cloud Composer, Workflows, BigQuery Studio, or third-party services. In the browser-based development interface, users get real-time error feedback, visualize dependency graphs, connect to GitHub or GitLab for commits and code reviews, and launch production-grade pipelines in minutes without leaving BigQuery Studio.
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Data Quality Validator (DQV)
DQV is Kumaran Systems' data quality and testing platform for teams who move, mask, or validate large volumes of data. It compares source and target datasets field by field, flags drift, and generates a mismatch report instead of manual spreadsheet checks.
It covers five areas: field-level comparison with drift detection, migration mapping between schemas, deterministic PII masking, record- and table-level validation with on-the-fly correction, and synthetic data generation for teams without production data to test against.
It connects to SQL Server, Oracle, MySQL, PostgreSQL, AWS, Azure, GCP, flat files, JSON, XML, and REST APIs, and plugs into Informatica, Databricks, and CI/CD pipelines, or runs standalone as a library or CLI tool.
In production, DQV has validated 26.6 million bank records in under 22 minutes. A free trial is available, alongside individual, enterprise, and on-premises licensing.
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