In the previous parts of this series, we explored the foundational layers of the modern data stack, the pivotal role of Iceberg, and the importance of seamless data sharing across different environments. We also delved into how solutions like Vendia IceBlock can unlock true data democratization by enhancing security, governance, and real-time synchronization.
But the journey doesn’t stop there. The modern data stack is not a static entity; it’s constantly evolving, with new technologies and trends emerging all the time. As we look ahead to 2026 and beyond, several key trends are poised to shape the future of data management, sharing, and democratization.
1. The Rise of AI/ML
Artificial intelligence and machine learning are rapidly transforming the data landscape. From automating data integration and analysis to powering new data-driven products and services, AI/ML is becoming increasingly essential for organizations looking to gain a competitive edge.
- AI-powered data discovery and governance: AI/ML can automate the process of discovering, classifying, and governing data, making it easier for organizations to ensure data quality, privacy, and compliance.
- Intelligent data sharing: AI/ML can be used to optimize data sharing by automatically identifying and recommending relevant data sets, predicting data needs, and automating data delivery.
- Data-driven innovation: AI/ML enables organizations to unlock new insights from their data, leading to the development of innovative products, services, and business models.
AI and ML are also key drivers of the modern data stack, because they are creating new (or greatly amplifying existing) demands on data infrastructure. Suddenly, the provenance and lineage of information is taking on new importance, as enterprises fight against “hallucinations” and accidental exposure of PII or PHI through AI mechanisms. Data sharing is also more important than ever, because no single organization is likely to host all the information needed by GenAI models itself, and will intrinsically rely on others to augment models, RAG, prompt engineering, and other approaches when building AI-based solutions.
2. The Decline of ETL
Traditional ETL (Extract, Transform, Load) processes are becoming increasingly outdated in the modern data stack. The rise of Zero ETL and real-time data sharing solutions is eliminating the need for complex data pipelines, reducing latency, and improving data accessibility.
- Zero ETL for seamless integration: Zero ETL allows data to be accessed and analyzed directly in its source systems, eliminating the need for costly and time-consuming data movement.
- Real-time data sharing for agility: Real-time data sharing enables organizations to respond quickly to changing business needs and customer demands, improving agility and competitiveness.
While still very necessary in organizations, ETL is increasingly going to be “SaaSified”, with data storage increasingly being something that tools and applications provide as an outcome rather than a DIY challenge to purchasers.
3. The rise of no-code solutions
No-code solutions are empowering business users to access, analyze, and share data without needing specialized technical skills. This trend is democratizing data access and enabling organizations to become more data-driven.
- No-code data exploration and visualization: No-code tools allow business users to easily explore and visualize data, gaining insights without writing code.
- No-code data sharing and collaboration: No-code platforms enable business users to share data with colleagues and partners, fostering collaboration and data-driven decision-making.
The goal of simplifying data management and giving more users more access to data has been around since long before computers were invented. But recent improvements in GenAI and data sharing have vastly accelerated these trends – suddenly, the idea that non-technical professionals can transform, combine, analyze, and utilize complex datasets from inside and outside an organization feels not just achievable, but probable. AI-generated queries and ease of data sharing are powering this incredible revolution.
4. Self-managed and table-optimized data infrastructure
It’s not just information workers and managers who are reaping the benefits of easier-to-use platforms and tools. Amazon’s new “Iceberg-aware” storage comes with optional features such as automatically compacting files for improved performance and easier sharing in the background, without a data operator needing to schedule, monitor, or manage the compaction jobs, let alone the underlying servers, storage devices, or other infrastructure. It’s a sort of “serverless on steroids” approach that delivers a higher level of abstraction for Icerberg tables. While currently limited primarily to working with other AWS services, we can expect that both ecosystem partners and the other clouds will eventually follow suit and over time this “rising tide” of self-managed data infrastructure will make Iceberg feel less like a collection of open source libraries that a data operator needs to master and more like a refrigerator that “just knows” how to keep the ice bucket filled and ready to use.
- Iceberg-aware storage: Amazon Web Services took an interesting innovative step by offering storage solutions (S3 buckets) specifically tuned for Iceberg table storage and with Iceberg-based convenience features like background compaction built right in.
- Iceberg improvements: The Iceberg community isn’t standing still. New versions of the Iceberg format, tools, utilities (and of course, matching managed service offerings from public clouds, data lake vendors, and other Iceberg providers) will start to take advantage of an expanded set of data types, improved deletion performance, and standardized catalog APIs.
- Hybrid operational/analytical infrastructure: One of the exciting “up and coming” news items from the 2025 Iceberg conference in San Francisco was eventual support for efficiently storing, transferring, and cataloging small-sized data tables. Along with improved deletion and insertion performance, this starts to make Iceberg – originally conceived as purely an analytical solution – a viable infrastructure choice for so-called “hybrid” scenarios where operational data needs to be collected, analyzed, and acted on in real time. These use cases demand infrastructure solutions that embody both classic operational capabilities like real-time performance along with the space efficiency, catalog solutions, and heterogeneous integration and sharing options associated with analytical tables. Stay tuned for more from the Iceberg committers on this and related topics in the v4 release.
5. The data mesh
The data mesh is an emerging architectural paradigm that promotes decentralized data ownership and management. This approach enables organizations to scale their data infrastructure and improve agility while ensuring data quality and consistency.
- Domain-oriented data ownership: The data mesh empowers domain teams to own and manage their data, improving data quality and relevance.
- Self-serve data infrastructure: The data mesh provides a self-serve platform for data access and analysis, enabling users to discover and use data independently.
- Federated governance: The data mesh promotes a federated approach to data governance, ensuring data quality and compliance while allowing for domain autonomy.
6. Easier data product creation and distribution
Organizations have been attempting to take advantage of their data assets for years, but turning in-house data into a marketable resource that can be effectively commercialized has long remained out of reach for most companies. While there are numerous challenges involved, one of the most obvious and limiting reagents has been the historical difficulty of data distribution – sharing a large, complex, ever-evolving data set with multiple parties who operate in different clouds, lakes, and geographic regions.
Advances in data sharing, especially heterogeneous data sharing, through common formats like Iceberg, governance approaches like Polaris, and safety and security mechanisms like Vendia IceBlock are quickly removing the historical challenges to data product distribution. Companies looking to derive more value from their data can increasingly do so with the security, fidelity, and ease of sharing that they experience with in-house data distribution.
Vendia: shaping the future of data
Vendia is at the forefront of these trends, developing innovative solutions that are shaping the future of the modern data stack. By providing a secure, scalable, and easy-to-use platform for data sharing and collaboration, Vendia is helping organizations embrace the future of data and unlock new possibilities for data-driven innovation.
The future of data: Empowering your organization
The modern data stack is constantly evolving, and the trends discussed above are just a glimpse of what’s to come. By staying ahead of these trends and embracing innovative solutions like Vendia, organizations can navigate the complexities of the data landscape and unlock the full potential of their data assets.