Generative AI (GenAI) and Large Language Models (LLMs) are changing a wide range of industries, and data analytics is no exception. In the quest for richer insights from data, emerging AI and data analytics trends are at the forefront of discussion. From agentic AI that automates entire workflows to synthetic data that fills critical gaps, these trends can help bring to light new opportunities for organizations to stay ahead.
In this article, we’ll go through some of the key developments in AI shaping the big data and analytics landscape in 2025.
Agentic AI is expected to transform the workplace and become a major trend in 2025 and beyond. Gartner predicts a surge in enterprise adoption, from under 1% in 2024 to 33% by 2028.
In data analytics and data science, agentic AI could help automate data preparation and ETL flows and fine-tune models. It will also allow employees to develop and manage complex, technical projects and enable complex automation through natural language interfaces.
Building AI agents can be complex, especially for organizations with limited experience in AI technologies. The development of such agents requires meticulous design and the implementation of a robust, strategically conceived system architecture to ensure reliability, scalability, and alignment with organizational objectives. What’s most important is to research and recognize your business goal, then find a partner with experience in building such agents.
Filling gaps in your missing or incomplete data through synthetic data generation is a significant advancement in addressing data scarcity challenges and it's becoming increasingly easier to generate it through AI. Many organizations combine real data with either partially or fully synthetic data, with the former being more commonly used.
However, with 51% of surveyed organizations mentioning in a Gartner study that they didn't have enough real-world source data and 41% mentioning low quality real-world source data, challenges remain in this area.
As in the past years, data quality remains a key aspect in data analytics, especially along with the onset of Agentic AI and the need to generate synthetic data.
Giving AI agents more power means that you should also give them high-quality data if you wish to be able to trust their decisions, and having high-quality real data means high-quality synthetic data as well.
Synthetic data improves model accuracy and efficiency while helping mitigate data privacy concerns.
Great metadata management is a combo between technical metadata (data type, schema) and business metadata (owner, definition, usage) for better context. By making use of metadata management solutions, organizations can create enhanced data catalogs and data lineage. According to Gartner, enterprises without a metadata-driven modernization strategy could face data management expenses that are up to 40% greater.
Metadata is essential for organizing, understanding, and managing data across an organization, while a data catalog helps further in organizing and exposing metadata. A data catalog collects, indexes, and makes metadata accessible, including information about data lineage, ownership, quality, and relationships. A component that can also be installed in our k8s-based data platform, KubeLake, is OpenMetadata, which is an open-source data inventory platform, holding data and information such as who the owner is, how data is updated and by whom, which other tables they are interacting with, and so on.
At our AI (every)Day event, Lucian Neghina, Head of Big Data, presented the KubeLake Astrology project: an integrated AI-based analytics solution designed to optimize decision-making. He developed this PoC to streamline data exploration and identify new business opportunities and KPIs by analyzing metadata structures within the data lake. The tool generates clear and intuitive visualizations that translate metadata into actionable insights that empower teams to collaborate effectively and drive data-focused strategies across the enterprise.
While LLMs dominate headlines, in data analytics, the use of small, task-specific models might offer more accurate and better contextual outputs in various industries. According to Gartner, organizations might use small task-specific AI models three times more than general-purpose LLMs.
Trained on domain-specific datasets (or subsets of data), these compact models use fewer parameters and offer more precise, context-aware outputs. They use NLP for specific tasks and reduce the risk of inaccurate results.
An AI appliance is a dedicated hardware system tailored specifically for running AI and ML tasks. It accelerates AI workloads, improving performance and energy efficiency for tasks like research, video conferencing, content creation, and more. Since AI processing happens locally, it offers enhanced privacy and reduces reliance on other services.
AI appliances powered by Blackwell GPUs promise to deliver faster processing, real-time inference, and predictable performance for high-volume workloads. Major vendors like NVIDIA, Dell, Asus, or HP are already releasing or announcing AI appliances for private, on-premises use, targeting generative AI, computer vision, data analytics, and other demanding workloads.
Privacy concerns are the main driver for these devices: by running AI on-prem, not connected to mainstream services, users can benefit from enhanced security. In addition, keeping AI infrastructure on-premises helps reduce data transfer costs. While this setup may involve a greater up-front investment, it will prove more cost-effective over time.
AI appliances have the potential to become a major technology trend by year-end.
With these trends in mind, let’s look at how data platforms are evolving to support them. Modular, scalable, and interoperable platforms are emerging to help organizations integrate, process, and analyze vast data volumes in real time. Our Kubernetes-based data platform, KubeLake, delivers a tailored solution that evolves alongside technological advancements, ensuring businesses can benefit without compromising flexibility or scalability. The various modules each serve a distinct role: from data ingestion and data storage to data visualization and AI/ML capabilities.
Following the discussion of key AI trends, we now turn to the AI and ML capabilities of KubeLake. The AI and ML component is designed to help you build and train predictive models to better understand customer behavior, identify market trends, and make better-informed decisions in real time. KubeLake’s AI/ML module is more than a traditional machine learning toolkit, supporting advanced use cases such as scientific research as well.
When it comes to data-intensive research, vectorial databases and vectorial search become invaluable. These tools effectively handle large data volumes and accommodate high query rates, proving essential for both academic inquiries and enterprise solutions. Our partners at Vespa.ai offer a robust platform tailored for developing and operating large-scale AI-driven applications that you can easily integrate with KubeLake. These include capabilities in search, recommendation, personalization, and real-time Retrieval-Augmented Generation (RAG), highlighting how vectorial search not only complements research but also enhances data accessibility and analytic precision.
Kubeflow, for example, is an open-source, Kubernetes-native AI pipeline orchestrator designed to simplify, automate, and scale the entire ML lifecycle (from data exploration and pipeline orchestration to model training, hyperparameter tuning, and model serving) and can be easily integrated with KubeLake. Kubeflow abstracts much of the complexity of Kubernetes, bringing together a suite of modular tools and components that address each stage of ML development, making it easier for data scientists, ML engineers, and researchers to build, deploy, and manage ML workflows on a Kubernetes infrastructure.
The convergence of GenAI, LLMs, agentic AI, and synthetic data is reshaping data analytics, but some fundamental principles remain: high-quality, well-governed data in a single source of truth is essential for success. KubeLake can help organizations like yours integrate, manage, and analyze data while staying flexible and future-ready:
As these AI trends accelerate, platforms like KubeLake will become key enablers in bridging the gap between innovation and implementation in data-driven enterprises.
An enthusiastic writing and communication specialist, Andreea Jakab is keen on technology and enjoys writing about cloud platforms, big data, infrastructure, gaming, and more. In her role as Social Media & Content Strategist at eSolutions.tech, she focuses on creating content and developing marketing strategies for the eSolutions’ blog and social media platforms.
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