To meet new and unforeseen demands for data, enterprises are rethinking their data architectures. Adopting data warehouses to store structured data for analysis worked well as a first attempt at bringing together disparate data. However, this approach to data management has reached its limits for many organizations and many find themselves with multiple data warehouses - which is driving up Total Cost of Ownership and increasing compliance complexity.

In the Gartner Chief Data and Analytics Officer Agenda Survey for 2024, 78% of respondents indicated they are making changes or overhauling their approach to data and analytics architectures and design patterns.

Prioritizing interoperability, ease of use, and scalability in the data architecture enables data leaders to improve data management, streamline self-service access to trusted data, and build an agile, resilient enterprise data platform. 

The future of modern data management: the data ecosystem

In order to improve data management, companies tried to incorporate strategies that would provide them the agility and responsiveness needed to scale data efforts. One such popular approach, the data fabric, combines every facet of modern data management—such as metadata analysis, data integration, semantics, knowledge graphs, and machine learning—into a powerful, flexible architecture that offers centralized visibility and access. However, this approach did not account for globally distributed data nor varying deployment options such as multi-cloud or hybrid that require interoperability between platforms.

To address the shortcomings of the data fabric, an emerging data architecture design, the data ecosystem, promises to help enterprises to new efficiencies, streamline data access, and optimize analytics workflows.  Built on a data fabric framework, the data ecosystem takes management of all data workloads into account in order to better position the organization’s architecture to meet existing and future data requirements such as operational and transactional data, exploratory data science, production data warehousing, and AI. 

A data ecosystem encompasses all the tools, processes, and governance frameworks needed and has the potential to address common data management challenges. Gartner predicts that by 2028, data management markets will converge into “a single market” around data ecosystems enabled by data fabric and GenAI, reducing technology complexity. This shift signals the importance of interoperability and automation to drive business value from data.

As enterprises move toward this vision, they must consider key architectural principles that ensure their data ecosystem is future-proof and adaptable.

Evaluating your architecture for the data ecosystem

To support the transition to a future-proof data ecosystem effectively, a successful data ecosystem should be:

• Interoperable – Ensuring frictionless data movement and compatibility across platforms, formats, and clouds.
• Customizable – Allowing flexibility to support varied use cases, governance policies, and business needs.
• Scalable – Enabling growth by supporting increasing data volumes, workloads, and analytical demands.

The central role of data lakes and lakehouses

The governed data lake, or data lakehouse, is the centerpiece of a data fabric, serving as a comprehensive repository for an organization’s data. To make all data visible and accessible, a lakehouse must incorporate data integration powered by comprehensive metadata and AI-assisted discovery. It is critical to standardize data integration and processes across the organization. By unifying data, metadata, and data pipelines from the start, data management leaders can future-proof their lakehouse and facilitate its evolution within the broader data ecosystem.

However, achieving this level of integration and visibility requires a modern approach to data movement and data lake management. This is where solutions like the Fivetran Managed Data Lake Service come into play.

Fivetran Managed Data Lake Service supports the data fabric by automatically converting data into open-table formats ensuring ACID compliance and consistent governance, and enabling cross-platform interoperability. Fivetran helps enterprises create a dynamic, self-service data environment by centralizing data from disparate sources and adapting to schema changes. This approach ensures that data remains accessible, well-governed, and optimized for real-time analytics, reinforcing the core principles of a robust data fabric.

As the centerpiece of an agile, resilient data fabric, Fivetran’s Managed Data Lake Service offers the following benefits:

• Interoperability – Fivetran’s automated data movement across all major data lakes,open table formats, and catalogs ensures flexibility for evolving architectures and multi-cloud strategies.
• Lower Total Cost of Ownership – Reduce total cost of ownership by reducing ingest query costs, optimizing storage with data lakes, and delivering query-ready data to minimize manual engineering efforts.
• Better compliance – With open table formats, catalog integrations, hybrid deployment for self-hosted pipelines, and built-in data masking, Fivetran enhances data lake governance for easier compliance.

Building a full-featured data ecosystem is an ongoing journey, but making architecture choices optimized for interoperability, performance and security will enable enterprises to quickly adapt to technology changes. By investing in scalable, flexible architectures, data leaders can future-proof their organizations and unlock the full potential of their data assets.

A recent example of this comes from Interloop, a data and infrastructure firm that helps its customers unify, analyze, and operationalize their data. Interloop leveraged Fivetran Managed Data Lake Service to streamline data operations and drive real-time AI insights in Microsoft OneLake. By reducing pipeline maintenance from 20–30% of analyst time to less than 5% and cutting analytics project turnaround time by over 50%, they achieved faster responses to business needs. Additionally, they lowered data extraction costs and storage overhead while enabling AI-driven automation for customer categorization and trend identification, empowering clients with real-time actions and personalized experiences.

Ready to learn more about landing fully managed, query-ready data in your lake of choice with Fivetran? Get more information here.