Agility and decentralization are having a disruptive impact on the architecture of analytical systems. A modern analytics system is expected to have the following characteristics: Flexibility, elasticity, automation and self-service, decoupling of individual applications, and real-time capabilities. The cloud and container-based architectures offer new opportunities.
Agility and decentralization are having a disruptive impact on the architecture of analytical systems. A modern analytics system is expected to have the following characteristics: Flexibility, elasticity, automation and self-service, decoupling of individual applications, and real-time capabilities. The cloud and container-based architectures offer new opportunities.
The most important drivers of modern decentralized architecture concepts are domain-driven design (DDD) and microservice architectures. DDD provides a decentralized architecture consisting of domains (often IT systems) with clearly defined domains of application (bounded context) and comprehensible visualized dependencies and interactions (context maps). A uniform, ubiquitous language that can be understood by everyone is used. Microservice architectures, which include methods of decomposition, decoupling, and isolation of individual services, are changing and redefining software development.
The possible manifestations of modern architectures are many and varied. Architecutre follows Use Case.
Classic data warehouses with data marts based on Inmon or Kimball principles for structured data optimally support clearly defined business use cases with top-down methods. Data Lakes with additional storage for semi-structured and unstructured data are used by Data Scientists for the exploration and creation of bottom-up use cases.
Data Mesh applies the idea of decentralized domains with their own data ownership and architecture. Data is created as the product of a domain through microservices and offered to other domains with self-service data infrastructure. A cross-domain governance organization provides the global decisions, defines the global ubiquitous language and domain boundaries.
Data Fabric is a data architecture that connects multiple domains using different technologies and services. Data can be exchanged based on the intelligent metadata-driven pipelines. Users can easily access and consume all the data at will through self-service. AI and ML support data governance, data quality and data preparation.
synvert saracus supports the development and modernization of data architectures with different technologies and in different environments (on-premises, cloud, hybrid and multi-cloud).
When developing a data architecture, it is important to define clear use cases. synvert saracus supports you in identifying necessary data from internal and external domains, in choosing the right technologies, and in project management with goal-oriented roadmap planning.
Depending on how the new architecture will be integrated into your existing systems, a decision must be made whether to design it on-premises, in the cloud, or hybrid. By conducting PoCs and pilots, including assistance with tool selection, system integration, and development of the logical architecture, synvert saracus guarantees that your new systems will fit seamlessly within existing ones.
The implementation of data governance supports you in maintaining the standards of your data architecture. A clear assignment of roles and responsibilities ensures that business processes run in a standardized, workflow-supported manner and that your employees maintain a clear understanding through business glossaries and data catalogs, thus improving their data literacy.
Through generic industry and use case models, saracus can provide you with agile data modeling concepts. With ready-made data marts and data product structures for a wide range of use cases, including data vault and anchor modeling, saracus ensures that your new data architecture embodies best practices and meets industry standards.
In the course of developing a data architecture, taking a look at data quality and data preparation is important as well. The definition of data quality KPIs, as well as their measurement and visualization, helps you to maintain a high level of data quality. The design and implementation of automated data pipelines, as well as various integration patterns (CDC, synchronous, asynchronous, bulk, ETL/ELT, streaming, etc.) ensures that these standards are met.
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