SAS Studio
SAS Studio provides a web browser-based programming environment, so writing and interacting with SAS code is easier and faster, wherever you are. It helps teams build efficient data pipelines with a data engineering experience designed for seamless collaboration, low-code work, and open source integration. SAS Studio connects to leading cloud data platforms such as AWS Redshift and S3, Google BigQuery and Cloud Storage, and Azure Data Lake Storage, as well as relational and nonrelational databases, including Oracle, Snowflake, Teradata, SingleStore, MongoDB, and other sources. It also works with file formats such as Excel, text, Parquet, and ORC. Users can choose no code, low code, or code by creating end-to-end data pipelines with drag-and-drop steps, developing Python and SAS code assets in SAS Studio or another IDE, and embedding them into SAS Studio flows for secure, centralized access to data sources and governed execution. SAS Studio supports ELT and ETL approaches.
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REGRESSwise
REGRESSwise is an enterprise-grade regression testing platform built natively for Google BigQuery. Developed by iQspeaks Limited, it enables data engineering and QA teams to automate validation of high-scale data pipelines with fast and reliable before-and-after comparison testing.
The platform supports schema validation, row-level comparison, and aggregate-level validation to identify data inconsistencies, schema drift, and transformation issues before production deployment. REGRESSwise integrates directly into existing CI/CD workflows and complements dbt testing by validating output data rather than transformation logic.
Built for enterprise environments, REGRESSwise processes millions of data points efficiently while ensuring data integrity, auditability, and low BigQuery compute costs. The platform is ideal for organizations using GCP and BigQuery for analytics and large-scale data transformation workloads.
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Google Cloud Managed Service for Apache Airflow
Managed Service for Apache Airflow is a fully managed workflow orchestration platform from Google Cloud built on the open-source Apache Airflow project. It allows users to author, schedule, and monitor data pipelines using Python-based workflows known as DAGs. The platform eliminates the need to manage infrastructure, enabling teams to focus on building and running pipelines. It integrates seamlessly with Google Cloud services such as BigQuery, Dataflow, and Managed Service for Apache Spark. It also supports hybrid and multi-cloud environments, allowing workflows to span across different systems. Users benefit from built-in monitoring, logging, and troubleshooting tools for reliability. The service is designed to simplify complex data workflows, including ETL, MLOps, and automation tasks. Overall, it provides a scalable and flexible solution for orchestrating modern data pipelines.
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Y42
Y42 is the first fully managed Modern DataOps Cloud. It is purpose-built to help companies easily design production-ready data pipelines on top of their Google BigQuery or Snowflake cloud data warehouse.
Y42 provides native integration of best-of-breed open-source data tools, comprehensive data governance, and better collaboration for data teams. With Y42, organizations enjoy increased accessibility to data and can make data-driven decisions quickly and efficiently.
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