DataBuck
Big Data Quality must always be verified to ensure that data is safe, accurate, and complete. Data is moved through multiple IT platforms or stored in Data Lakes. The Big Data Challenge: Data often loses its trustworthiness because of (i) Undiscovered errors in incoming data (iii). Multiple data sources that get out-of-synchrony over time (iii). Structural changes to data in downstream processes not expected downstream and (iv) multiple IT platforms (Hadoop DW, Cloud). Unexpected errors can occur when data moves between systems, such as from a Data Warehouse to a Hadoop environment, NoSQL database, or the Cloud. Data can change unexpectedly due to poor processes, ad-hoc data policies, poor data storage and control, and lack of control over certain data sources (e.g., external providers). DataBuck is an autonomous, self-learning, Big Data Quality validation tool and Data Matching tool.
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Datagaps ETL Validator
DataOps ETL Validator, the most comprehensive ETL testing and data validation tool, is the most comprehensive ETL testing automation software. Comprehensive ETL/ELT Validation Tool to automate testing of data migration projects and data warehouses with an easy-to-use component-based user interface and low-code, zero-code test creation. ETL involves extracting data, transforming it according to operational needs, and then loading it into the target database or data store. ETL testing involves verifying accuracy, integrity and completeness of the data as it moves along the ETL process in order to ensure that it meets business requirements and rules. Automation of ETL testing is possible with tools that automate data validation, comparison, and transformation tests. This will speed up the testing cycle, reduce manual labor, and significantly accelerate the testing cycle. ETL Validator automates ETL tests by providing intuitive interfaces to create test cases without extensive programming.
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Datagaps DataOps Suite
Datagaps DataOps Suite, a comprehensive platform, automates and streamlines data validation processes throughout the entire data lifecycle. It offers end to end testing solutions for ETL projects (Extract Transform Load), data management, data integration and business intelligence (BI). The key features include automated data cleansing and validation, workflow automation and real-time monitoring, as well as advanced BI analytics. The suite supports multiple data sources including relational databases and NoSQL databases as well as cloud platforms and file-based systems. This ensures seamless integration and scalability. Datagaps DataOps Suite, which uses AI-powered data quality assessment and customizable test scenarios, improves data accuracy, consistency and reliability.
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SAP Master Data Governance
To simplify enterprise data management, improve data accuracy, reduce costs, and create a cohesive master data management strategy across all your domains, you need to establish a common and coordinated master data management strategy. With minimal barriers to entry, you can kick-start your corporate master information management initiative in the cloud. You also have the option to add master data governance scenarios at will. By combining SAP and third-party data sources, you can create a single source for truth and mass process additional bulk updates on large amounts of data. To confirm master data readiness and to analyze master data management performance, define, validate, monitor, and monitor established business rules. Facilitate collaborative workflow routing and notification so that different teams can have their own master data attributes and validated values for specific data points.
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