Then use the data catalog to register data sources. And finally, discover where various master data is located across multiple data stores in the distributed data landscape. You can map the physical data names of discovered master data to the common business vocabulary in Azure Purview. Then you'll be able to learn how to clean, match, and integrate the data discovered to create golden master data records stored in a central MDM system. Once created and stored centrally, master data can be synchronized with all other systems that need it to make sure they're consistent.
It's important to govern your master data maintenance. The challenge is identifying in which tasks of which business processes that maintenance happens.
This identification can be done using business process identification and create, read, update, or delete CRUD analysis. It's often a manual task to work out this governance. But now it's helped by the emergence of process mining and analyzing database log files.
Data use cases from advanced analytics to machine learning all require similar data preparation processes and attention. During these processes, governance must ensure data control and privacy protection with clear ownership, full traceability, and an audit trail of data origins, processing, and use. The CluedIn platform encapsulates these data management processes and pillars into a coherent, consistent, end-to-end Master Data Management MDM solution.
CluedIn uses a data integration technique called eventual connectivity that yields better results than classic extract, transform, load ETL or extract, load, transform ELT models. Eventual connectivity uses GraphQL queries to blend data seamlessly from across many siloed data sources.
With eventual connectivity, data isn't joined or blended upon entry or loading into other systems. Instead, CluedIn loads the data as is, and tags records using metadata. Eventually, records with the same tags merge or build a relationship in the graph. This sophisticated data merging technique provides a foundation for data-driven solutions.
The CluedIn Data Fabric integrates data into a pipeline that cleans, prepares, models, governs, enriches, deduplicates, and catalogs data to make it easily available and accessible for business uses. CluedIn provides businesses with metrics about the quality of data it ingests, intelligently detecting dirty data and preparing it for cleaning by data engineers and data stewards. Proprietary fuzzy logic machine learning algorithms help business users and curators label data, and teach the system to identify, correct, and prevent data quality issues over time.
CluedIn includes enterprise-grade governance, for assurance that you can use your data safely and confidently. Native support for autoscaling leverages the power of Azure to provide a scalable environment for the biggest data workloads. A combination of. NET Core microservice applications handles distinct functions like data ingestion, streaming data processing, queuing, and user interface.
The enterprise service bus connects through ports and for admin endpoints. Crawlers send data to the bus, and the processing layer consumes data from the bus, over port In the persistence layer, databases consume data from the transaction log and persist it to provide eventual consistency across the different data stores. All the stores run in high-availability HA mode. Unlike with data virtualization, the CluedIn persistence layer ingests parts of the source data and preserves the highest fidelity version of data and its structure.
This high fidelity means that the CluedIn Data Fabric can serve business requests for data in any format or model. The data abstraction layer connects to the different data stores through the ports for each store. All communication from the browser into the application uses a set of ingress definitions, which require only a single public IP address. In a production environment, all communication is over secure socket layer SSL. The system backs up and stores all data in SQL or Redis databases.
CluedIn runs on Azure Kubernetes Service AKS , a highly available, secure, and fully managed Kubernetes service for deploying and managing containerized applications. CluedIn takes automatic daily database backups and keeps them in long-term storage for 30 days by default. The entire platform is built on redundant, fault tolerant stacks that maintain backups for all subsystems.
Round the clock monitoring systems ensure that services are as untainted as possible. It brings together the best of the SQL technologies used in enterprise data warehousing, Spark technologies used in big data analytics, and Pipelines to orchestrate activities and data movement.
Azure Databricks : Azure Databricks is an Apache Spark-based analytics platform optimized for the Microsoft Azure cloud services platform. Designed with the founders of Apache Spark, Databricks is integrated with Azure to provide one-click setup, streamlined workflows, and an interactive workspace that enables collaboration between data scientists, data engineers, and business analysts. Please also see the big data architectures reference in the Azure Architecture Center , as there is additional MDM design considerations and approaches that are discussed.
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