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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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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eccenca Corporate Memory
eccenca Corporate Memory offers an all-encompassing platform that integrates various disciplines for the management of rules, constraints, capabilities, configurations, and data within a single application. By transcending the shortcomings of conventional application-focused data management approaches, its semantic knowledge graph is designed to be highly extensible and integrates seamlessly, allowing both machines and business users to interpret it effectively. This enterprise knowledge graph platform enhances global data transparency and promotes ownership across different business lines within a complex and ever-evolving data landscape. It empowers organizations to achieve greater agility, autonomy, and automation while maintaining the integrity of existing IT infrastructures. Corporate Memory efficiently consolidates and connects data from diverse sources into a unified knowledge graph, and users can navigate their comprehensive data environment using intuitive SPARQL queries and JSON-LD frames. The platform's data management is executed through the use of HTTP identifiers and accompanying metadata, ensuring a structured and efficient organization of information. Overall, eccenca Corporate Memory positions itself as a transformative solution for modern enterprises grappling with data complexities.
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ent
Introducing a Go entity framework that serves as a robust and straightforward ORM, perfect for both modeling and querying data. This framework offers a simple API that allows developers to represent any database schema as Go objects seamlessly. With the ability to execute queries, perform aggregations, and navigate complex graph structures effortlessly, it stands out for its user-friendly design. The API is entirely statically typed and features an explicit interface through code generation, ensuring clarity and reliability. The latest iteration of the Ent framework introduces a type-safe API that permits ordering based on fields and edges, with plans for this feature to be integrated into its GraphQL capabilities shortly. Additionally, users can easily generate an Entity Relationship Diagram (ERD) of their Ent schema with a single command, enhancing visualization. The framework further simplifies the incorporation of features like logging, tracing, caching, and soft deletion, all achievable with just 20 lines of code. Moreover, Ent supports GraphQL using the 99designs/gqlgen library and offers various integration options. It facilitates the generation of a GraphQL schema for nodes and edges defined within the Ent schema, while also addressing the N+1 problem through efficient field collection, eliminating the need for complex data loaders. This combination of features makes the Ent framework an invaluable tool for developers working with Go.
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