The GenAI revolution has raised expectations for what enterprises can do with data. But it has also exposed some serious shortcomings in how enterprises manage data. That’s the backdrop against which we will dig into this batch of big data management predictions.
Getting access to data has always been a challenge for analytics and AI. In 2025, the level to which organizations enable data access will determine their success with AI, predicts Haoyuan “HY” Li, the founder and CEO of Alluxio.
“In 2025, organizations will face increasing pressure to solve data access challenges as AI workloads become more demanding and distributed,” Li writes. “The explosion of data across multiple clouds, regions, and storage systems has created significant bottlenecks in data availability and movement, particularly for compute-intensive AI training. Organizations will need to efficiently manage data access across their distributed environments while minimizing data movement and duplication. We’ll see an increased focus on technologies that can provide fast, concurrent access to data regardless of its location while maintaining data locality for performance.”
Data archives are typically viewed as holding less interesting information. With the AI revolution in 2025, those troves of historical data will find new uses, predicts Lenley Hensarling, a technical advisor with NoSQL database maker Aerospike.
“Generative AI depends on a wide range of structured, unstructured, internal, and external data. Its potential relies on a strong data ecosystem that supports training, fine-tuning, and Retrieval-Augmented Generation (RAG),” Hensarling says. “For industry-specific models, organizations must retain large volumes of data over time. As the world changes, relevant data becomes apparent only in hindsight, revealing inefficiencies and opportunities. By retaining historical data and integrating it with real-time insights, businesses can turn AI from an experimental tool into a strategic asset, driving tangible value across the organization.”
Nice database you got there (Tee11/Shutterstock)
When organizations run through easily obtainable training data, they’ll often look to synthetic data to keep their models improving. In 2025, the use of synthetic data will go mainstream, says Susan Haller, senior director of advanced analytics at SAS.
“As more organizations discover the incredible potential of synthetic data—data that is statistically congruent with real-world data without resorting to manual collection or purchased third-party data —the perception of this technology will inevitably shift,” Haller says. “Making the generation of synthetic data more accessible across a range of industries, from healthcare to manufacturing, will prove to be a significant strategic advantage. The future possibilities for leveraging this type of data are endless.”
GPUs are the go-to accelerators for AI workloads. In 2025, organizations that master the data orchestration for GPUs will have a big advantage, says Molly Presley, SVP of global marketing for Hammerspace.
“As we head into 2025, one of the challenges in AI and machine learning (ML) architectures continues to be the efficient movement of data to and between GPUs, particularly remote GPUs,” Presley says. “Traditional data orchestration solutions, while valuable, are increasingly inadequate for the demands of GPU-accelerated computing. The bottleneck isn’t just about managing data flow—it’s specifically about optimizing data transport to GPUs, often to remote locations, to support high-performance computing (HPC) and advanced AI models. As a result, the industry will see a surge in innovation around GPU-centric data orchestration solutions. These new systems will minimize latency, maximize bandwidth, and ensure that data can seamlessly move across local and remote GPUs.”
Everyone shift left (no, your other left) (Aha-Soft/Shutterstock)
Instead of trying to solve data management issues as they occur in downstream systems, enterprises will try to address them soon in the workflow, says Confluent’s Adam Bellemare, the principal technologist in the company’s Technology Strategy Group.
“Organizations will adopt a ‘shift left’ approach to improve their data quality, reduce costs, and eliminate redundant processing,” Bellemare says. “Businesses will focus on processing workloads earlier in the data pipeline, allowing data to be cleaned, standardized, and processed before it lands in a data lake or cloud data warehouse. This shift will further decouple data from its storage, allowing for more flexibility in processing and utilizing data across different platforms, including for AI training and real-time inference. Businesses will not only lower costs by preventing redundant processing but also enable a more flexible and interoperable architecture where data can be plugged into multiple downstream systems without excessive duplication.”
Open table formats had a big year in 2024. In 2025, the momentum behind formats like Apache Iceberg and Delta Lake will keep building, says Emmanuel Darras, the CEO and co-Founder of Kestra, a developer of an open-source orchestration platform.
“Iceberg provides a standardized table format and integrates it with SQL engines like Spark, DuckDB, Trino, and Dremio, as well as with data platforms like Snowflake and Databricks, enabling SQL queries to run efficiently on both data lakes and data warehouses,” Darras says. “Relying on open table formats allows companies to manage and query large datasets without relying solely on traditional data warehouses. With organizations planning to adopt Iceberg over other formats like Delta Lake, its role in big data management is expected to expand, thanks to its strong focus on vendor-agnostic data access patterns, schema evolution, and interoperability.”
Do not fear Apache’s Iceberg (Romolo Tavani/Shutterstock)
Another big event in data management in 2024 was the emergence of technical metadata catalogs, such as Apache Polaris and Unity Catalog. The battle for technical metadata supremacy will get even more intense in 2025, predicts Alex Merced, a senior tech evangelist at Dremio.
“The competition to dominate the data catalog space will become a high-stakes showdown,” Merced tells BigDATAwire. “As hybrid and multi-cloud ecosystems grow, organizations will demand seamless interoperability, driving fierce innovation in governance, lineage, and user-defined functions (UDFs). Apache Iceberg will emerge as a key player, redefining standards for open table formats with its hybrid catalog capabilities. This race won’t just reshape data architecture—it will decide who controls the future of data portability.”
When your data growth curve hits a certain point on the cost curve, it can give your CFO heartburn. In 2025, new storage archive solutions will be needed to ensure your CFOs digestive health, says Arcitecta CEO Jason Lohrey.
“As data volumes grow, more efficient and cost-effective archival storage solutions have become critical,” Lohrey says. “Flash and disk-based storage options, while fast, come with high costs when scaling to large capacities. This has led to a resurgence in tape storage as a viable solution for modern needs, and the introduction of new, emerging technologies like storage on glass. Companies will look to aggregate smaller units into larger configurations that combine the scalability of tape with the flexibility of cloud standards. The renewed interest in tape and other archival storage solutions will continue to expand as the demands of modern data management evolve.”
GPUs can accelerate databases, too
GPUs are typically viewed as accelerators for HPC, AI, and graphics-heavy workloads (hence the name, graphical processing unit). But the potential for GPUs to accelerate database workloads will be something that becomes more clear in 2025, predicts Gopi Duddi, SVP of engineering at NoSQL database developer Couchbase.
“The AI revolution isn’t just transforming applications–it’s poised to fundamentally disrupt database architecture at its core. After half a century of CPU-based database design, the massive parallelism offered by GPUs is forcing a complete rethinking of how databases process and manage data,” Duddi says. “The potential for GPU-powered databases is staggering: operations that traditionally required complex CPU-based parallel processing could be executed across thousands of GPU threads simultaneously, potentially delivering ChatGPT-like performance for database operations.”
PostgreSQL has been the most popular database for the past few years. Don’t expect that trend to end any time soon, says Avthar Sewrathan, the AI product lead at Timescale, a time-series database that builds on PostgreSQL.
“In 2025, PostgreSQL will solidify its position as the go-to ‘everything database’- the first to fully integrate AI functionality like embeddings directly within its core ecosystem,” Sewrathan writes. “This will streamline data workflows, eliminate the need for external processing tools, and enable businesses to manage complex data types in one place. With its unique extension capabilities, PostgreSQL is leading the charge toward a future where companies no longer have to rely on standalone or specialized databases.”
It’s a bird! It’s a plane! It’s our Data Hero! (ktsdesign/Shutterstock)
The traditional divisions between data engineers, data analysts, and data scientists are breaking down, as modern data teams must increasingly handle end-to-end workflows with speed and autonomy. In 2025, we’ll see a new role will emerge, says Prat Moghe, the CEO of Promethium: the “data hero.”
“These versatile individuals will combine a solid level of technical skills with deep domain knowledge, enabling them to work seamlessly across data discovery, assembly, and product creation,” Moghen says. “Acting as the critical bridge between data and business, data heroes will drive greater alignment, faster insights, and more impactful decision-making in the coming year. However, to support this evolution, a new generation of data tools must emerge, tailored specifically to the needs of the data hero persona. Unlike legacy tools that cater to separate, disjointed roles, these modern platforms will unify capabilities and streamline cross-functional collaboration, empowering data heroes to unlock the true value of data in a rapidly changing landscape.”
Data fabric isn’t a new concept, but it also hasn’t gained the sort of traction that many big data observers expected it too. That will begin to change in 2025, as companies seek better management approaches to deal with the AI-induced big data deluge, predicts Dwaine Plauche, the snior manager of product marketing at Aspen Technology.
“As data management becomes more daunting for industrial companies, especially as they prioritize AI applications and digital transformation initiatives, we’ll see them turn to OT [operational] data fabrics to streamline thousands of IT and OT connections and make data more accessible and actionable throughout the business. OT data fabrics are capable of ingesting diverse data that connects people, machinery, plants, logistics and IT systems across the enterprise, so data can more easily scale to unlock the potential of new business opportunities, like AI, well into the future.”
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