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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DataHub
DataHub is a versatile open-source metadata platform crafted to enhance data discovery, observability, and governance within various data environments. It empowers organizations to easily find reliable data, providing customized experiences for users while avoiding disruptions through precise lineage tracking at both the cross-platform and column levels. By offering a holistic view of business, operational, and technical contexts, DataHub instills trust in your data repository. The platform features automated data quality assessments along with AI-driven anomaly detection, alerting teams to emerging issues and consolidating incident management. With comprehensive lineage information, documentation, and ownership details, DataHub streamlines the resolution of problems. Furthermore, it automates governance processes by classifying evolving assets, significantly reducing manual effort with GenAI documentation, AI-based classification, and intelligent propagation mechanisms. Additionally, DataHub's flexible architecture accommodates more than 70 native integrations, making it a robust choice for organizations seeking to optimize their data ecosystems. This makes it an invaluable tool for any organization looking to enhance their data management capabilities.
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dbt
dbt Labs is redefining how data teams work with SQL. Instead of waiting on complex ETL processes, dbt lets data analysts and data engineers build production-ready transformations directly in the warehouse, using code, version control, and CI/CD. This community-driven approach puts power back in the hands of practitioners while maintaining governance and scalability for enterprise use.
With a rapidly growing open-source community and an enterprise-grade cloud platform, dbt is at the heart of the modern data stack. It’s the go-to solution for teams who want faster analytics, higher quality data, and the confidence that comes from transparent, testable transformations.
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netScope® Cloud
Easily access your WSI data through digital remote or tele-access, allowing for collaboration with others online if desired. Furthermore, you can streamline various processes by utilizing barcodes and QR codes, making it possible to share slides with clients without the need for manual distribution. Alternatively, leverage netScope Cloud for educational purposes to simultaneously demonstrate your points to a larger audience, enhancing the learning experience for everyone involved. This approach not only saves time but also fosters greater engagement during presentations.
Aside Data from ZEISS Axio Scan all kinds of CZI data can be displayed. ZEISS customers who still use older MIRAX slides in the previous MRXS format can also display those slides in the netScope. Similar to CZI, netScope also supports all channels of Perkin-Elmer Vectra/Akoya Biosciences QPTIFF files. DICOM, Big-tiff slides such as Philips tiff files, IBL (basic implementation; more sample files needed) Hamamtsu NDPI and those in the formats Aperio SVS, Aperio AFI, Leica SCN, Roche Vantana BIF, Sakura Virtual Slide SVSLIDE, NanoZoomer VMS, VMU and many more are also supported.
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