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What this blog covers:

  • Definition and importance of data architecture
  • Evolution of data architecture with the rise of big data
  • Essentials to build a modern data architecture
  • How Kyvos revolutionizes data architecture with its Gen AI-powered semantic layer?

Organizations often accumulate vast amounts of unstructured and raw data from diverse data sources. However, traditional architectures can’t meet the momentum, agility and innovation needed to store, manage and analyze this data. Then, there are questions like: What measures are implemented to ensure data integrity and reliability? Are they able to use this data to make strategic decisions? Answers to these questions hold the key to how organizations structure their data. A well-defined data architecture can provide a 360-degree view of an organization’s data, standardize its management and storage. It works as a blueprint to set the ways in which data flows through the enterprise.

What is Data Architecture?

Data architecture is a pathway to align business requirements with IT systems and processes, establishing a robust framework for a data-driven culture within the organization. It plays a pivotal role in standardizing data collection, transformation, usage and accessibility across the enterprise. Integrating with diverse data sources, it helps ensure data security, accessibility and accuracy for smooth functioning. Ideally, the architecture should have the capacity to handle unpredictable data volumes and enable high scalability.

Modern architectures are designed to facilitate democratized data access to authorized users and empower them with self-serve analysis capabilities. With data democratization, every domain user has the power to make strategic decisions based on validated facts, not an assumption. This helps promote data-driven innovation, accurate decision-making and optimization of management processes.

The Rise of Big Data has Reshaped the Journey of Data Architecture

Companies can build an ecosystem that comprises various components such as databases, data warehouses and data lakes. In the 1970s, data volume was easily manageable and data architectures were less complicated than they are now. E.F. Codd proposed a relational database that would organize data into tables of rows and columns. Relational databases became the backbone of organizations for decades but had some limitations like schema rigidity, scalability limitations and performance degradation when used with operational applications that worked with large volumes of complex data structures.

In response to the limitations posed by relational databases, a relational data warehouse (RDW) was introduced with a partitioned table structure to store massive datasets and massively parallel processing to solve scalability issues. However, with time and ever-increasing data volumes, RDWs became more expensive, offering limited scalability, poor performance and complex maintenance.

In the late 2000s and early 2010s, data lakes gained popularity for handling massive data volumes and overcoming the challenges of data warehouses, enabling organizations to store data in its original format. With rapid technological advancements, though, the technology fell short on data quality and query performance while analyzing raw data. With the challenges data lakes posed, organizations began to adopt a hybrid approach called modern data warehouse (MDW) that combined the strengths of RDW and early data lake implementations. MDWs killed two birds with one stone; schema definition took care of data quality while offering flexibility to scale for some data types. While MDWs were a big step forward from RDW systems, they also posed some significant challenges that hindered organizations’ growth, such as data silos, vendor lock-in, restricted data integration of diverse data sources and scalability limitations while handling data explosion and spikes in user demand.

The concept of data fabric extended the capabilities of MDWs and provided organizations with a distributed data management architecture that empowers enterprise-wide users with democratized data access and allows them to integrate, manage and analyze data from diverse sources while mitigating the risks of data silos and inconsistent decisions. Over time organizations realized that centralized data architecture also poses challenges like lack of ownership, single point of failure and limited data accessibility. Decentralized architecture of data mesh evolved next to address these challenges with domain-oriented ownership, using data as a product, self-serve infrastructure and implementation of federated governance. These principles enforce a common data language on all the data products and standardize business definitions, metadata, standards and policies among all the business domains to ensure data is effectively integrated and analyzed.

Organizations today opt for a system—or a combination of systems—that aligns best with their requirements, based on critical factors such as data volume, scalability, reliability, security and governance, analytical requirements and organizational structure.

How to Build a Modern Data Architecture that Drives Innovation

When creating the roadmap for the future, most enterprises want to leverage AI-based applications and IoT-driven data workloads. A modern data architecture must be built to deal with voluminous data generated by these workloads while improving the quality of data used to train Gen AI models for implementing them. A few characteristics that form the foundation of a robust, secure and scalable modern data architecture are:

Cloud-Native Architecture

Traditional on-premises infrastructures are often incapable of scaling up and down quickly to meet the ever-growing data volumes and processing demands. Cloud platforms offer storage and processing resources with limitless scalability that can be provisioned and de-provisioned easily based on demand. Technologies like containers and microservices can be quickly deployed on an organization’s infrastructure. In addition, with pay-as-you-go pricing models, cloud platforms allow companies to update their data stack and adapt to new technologies easily without incurring additional infrastructure costs.

Seamless Data Integration

As data complexity and volumes increase, it becomes increasingly important to stop the fragmentation of data across multiple sources as it can lead to confusion and erosion of data trust arising from multiple versions of the same information. To provide one comprehensive view of enterprise data and to create a single, trusted source of truth, organizations need to unify data into a single semantic model. This way, users across the organization work with the latest version of data which accelerates the consumption of unified data and reduces the load of maintaining multiple data sources.

Robust Security

Strict security measures are essential to protect sensitive master data against vulnerability, breaches and leaks. Organizations can’t afford to lose sight of data at any point in their lifecycle. This means that tracking every action and user/groups accessing this data becomes critical. Defining rules, standards and policies for data protection is the first step in this direction. A structured approach with strict authentication, authorization and access controls helps prevent unauthorized and improper usage.

Standardized Business Definitions

Organizations should focus on using common semantics and vocabulary to enable users to understand and analyze data consistently. For example, one department in an organization can define ‘users’ as all registered users, whereas another may interpret the term as all active users. Everyone in the organization must use the same definitions and terms for data elements. Multiple interpretations of the same data can result in conflicting analysis and time wasted in reporting reconciliation, eventually resulting in inaccurate and delayed decision-making. A universal semantic layer adds effectiveness by translating raw data into a common business language and ensuring data consistency, regardless of the underlying source or complexity.

Building a modern data architecture requires a precise strategy and an organization’s decision-makers must take some critical considerations, keeping business goals in mind while prioritizing technology shifts. Building such stacks in-house or hiring experts requires time, resources, effort and expense that can further compound challenges. What if there was a platform that can help enterprises build a modern data architecture that adheres to all these principles and more?

Introducing Kyvos

Kyvos is a Gen AI powered semantic layer that organizes, simplifies and accelerates the consumption of enterprise data. It supercharges data and AI initiatives through sub-second querying on massive datasets, while optimizing analytics costs. The layer bridges the gap between business users and complex data sources and truly democratizes data access through conversational analytics. Let’s look at Kyvos’ architecture:

GenAI Powered High-Speed Universal Semantic Layer

Kyvos’ data storage layer supports cloud or on-premises data lakes, OLTP systems as well as streaming sources. In addition. Kyvos eliminates the need for enterprises to maintain separate data warehouses for storing aggregated and raw data, enabling much faster querying at a lower cost. The built-in analytical warehouse can store both raw data and aggregated data, improving the speed of analytics and provides a price-performant alternative to cloud data warehouses.

At the heart of the architecture is the Gen AI-powered semantic layer, which sits between the data and consumption layers. Using this layer, organizations can connect to multiple underlying data sources for data modeling and then build a unified business view for consumption across all the BI, AI and analytics tools used across the enterprise. Kyvos offers SQL, DAX, MDX, Rest and JAVA APIs to connect an enterprise’s BI and data science applications to a unified data source. Kyvos’ LangChain connector also enables AI and Gen AI developers to use Kyvos as a trusted data source.

On the data consumption layer, Kyvos includes state-of-the-art BI and reporting capability, Kyvos Viz, which helps build customizable and interactive 360° dashboards, no matter where the data comes from. It has an intuitive interface to drag-and-drop dimensions and measures for instant analysis on massive datasets and high-performance analytics for any number of concurrent users. In addition, Kyvos offers named connectors for Power BI, Tableau and MicroStrategy, seamless integration with all other popular BI tools and Excelerate, an add-in for high-performance analytics on Excel.

Organizations typically invest in state-of-the-art federated identity providers like Okta, Azure and LDAP, and Kyvos supports integration with all these popular IDPs. A three-tiered security architecture with row and column-level security at the group and user levels allows organizations to set up role-based access control to assign user privileges as per their authorizations. Kyvos’ AuthKey helps unlock data securely with data auditing, monitoring, versioning and lineage tracking. Also, integration with data catalog partners like Alation and Collibra allows data discovery using the interfaces provided by these tools.

Conclusion

With growing data volumes, data architecture will remain critical for organizations to succeed in a competitive market. Kyvos, with its high-performance analytical data store and AI-powered semantic layer, simplifies data modeling, offers unified data access, ensures limitless scalability and establishes robust data security and governance. With its innovative and future-oriented approach, organizations can navigate the complexities of advanced data analytics and unlock the full potential of data.

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