Data Analytics in 2025: Trends and Predictions
The Rise of Data Lakehouses
The world has seen all sorts of patterns for analytics: data lakes, data warehouses, in-memory analytics, and embedded analytics. But in 2025, the standard for analytics will be the data lakehouse, says Emmanuel Darras, CEO and Co-founder of Kestra, developer of an open-source orchestration platform.
"By 2025, over half of all analytics workloads are expected to run on lakehouse architectures, driven by the cost savings and flexibility they offer," Darras says. "Currently, companies are shifting from cloud data warehouses to lakehouses, not just to save money but to simplify data access patterns and reduce the need for duplicate data storage. Large organizations have reported savings of over 50%, a major win for those with significant data processing needs."
Standardization of Open Data Formats
One of the big drivers of the data lakehouse is the standardization of open data formats. That is a trend that will continue to build in 2025, predicts Adam Bellemare, principal technologist in the Technology Strategy Group at Confluent.
"Next year we will see a widespread standardization of open data formats, such as Apache Iceberg, Delta Lake, and Apache Hudi," says Bellemare. "This will be driven by a greater demand for interoperability, with enterprises looking to seamlessly combine data across different platforms, partners, and vendors. As enterprises prioritize access to timely, high-quality data, open data formats will no longer be optional but imperative for businesses to succeed. Those who fail to embrace these open standards risk losing a competitive advantage, and those who adopt them will be able to deliver a high-quality offering and real-time, cross-platform data insights."
Federated IT and Hybrid Ecosystems
Two of the biggest backers of the data lakehouse are Snowflake and Databricks. But in 2025, people will tire of the Snowflake/Databrick War and look to federated IT for an evolved data architecture, says Andrew Madson, a technical evangelist at Dremio and professor of data and analytics at Southern New Hampshire and Grand Canyon universities.
"Central IT teams will continue decentralizing responsibilities to business units, creating more federated operating models," Madson says. "Meanwhile, monolithic architectures from major vendors like Snowflake and Databricks will integrate additional tools aimed at improving cost-efficiency and performance, creating hybrid ecosystems that balance innovation and practicality."
Data Modeling and AI
Data modeling has wallowed in relative obscurity for years. In 2025, the practice will have its moment in the sun, says Adi Polak, Confluent’s director of advocacy and developer experience engineering.
"Data modeling has long been the domain of DBAs (database administrators), but with the increased adoption of open table formats like Apache Iceberg, data modeling is a skill that more engineers need to master," Polak says. "For application development, engineers are increasingly tasked with creating reusable data products, supporting both real-time and batch workloads while anticipating downstream consumption patterns. To build these data products effectively, engineers must understand how data will be used and design the right structure, or model, that’s suitable for consumption, early on. That’s why data modeling will be an essential skill for engineers to master in the coming year."
AI’s impact will be felt everywhere, including the data analytics stack, says Christian Buckner, SVP of analytics and IoT at Altair.
"Today, many business leaders struggle with knowing what questions to ask their data or where to find the answers," Buckner says. "AI agents are changing that by automatically delivering insights and recommendations, without the need for anyone to ask. This level of automation will be crucial for helping organizations unlock deeper understanding and connections within their data and empowering them to make more strategic decisions for business advantage. it’s important for businesses to establish guardrails to control AI-driven suggestions and maintain trust in the results."
Embedded Analytics and Small Data
When you said "analytics," it used to conjure images of someone firing up a desktop BI tool to work with a slice of data from the warehouse. My, times have changed. According to Sisense CEO Ariel Katz, 2025 will bring about the demise of traditional BI, which will be replaced with API-first and GenAI-integrated analytics in every app.
"In 2025, traditional BI tools will become obsolete, as API-first architectures and GenAI seamlessly embed real-time analytics into every application," Katz says. "Data insights will flow directly into CRMs, productivity platforms, and customer tools, empowering employees at all levels to make data-driven decisions instantly–no technical expertise needed. Companies that embrace this shift will unlock unprecedented productivity and customer experiences, leaving static dashboards and siloed systems in the dust."
Big data was big because–well, it just was (trust us). But in 2025, the big data movement will open a new chapter by welcoming a relative of big data called small data, predicts Francois Ajenstat, the Chief Product Officer at Amplitude.
"The past few years have seen a rise in data volumes, but 2025 will bring the focus from ‘big data’ to ‘small data,’" Ajenstat says. "We’re already seeing this mindset shift with large language models giving way to small language models. Organizations are realizing they don’t need to bring all their data to solve a problem or complete an initiative–they need to bring the right data. The overwhelming abundance of data, often referred to as the ‘data swamp,’ has made it harder to extract meaningful insights. By focusing on more targeted, higher-quality data–or the ‘data pond’–organizations can ensure data trust and precision. This shift towards smaller, more relevant data will help speed up analysis timelines, get more people using data, and drive greater ROI from data investments."
Conclusion
In conclusion, 2025 will be a transformative year for data analytics. With the rise of data lakehouses, standardization of open data formats, federated IT, data modeling, AI, embedded analytics, and small data, organizations will have the tools and strategies they need to unlock the full potential of their data. Whether you’re a data scientist, business leader, or IT professional, it’s essential to stay ahead of the curve and adapt to these emerging trends.
FAQs
Q: What is a data lakehouse?
A: A data lakehouse is a data architecture that combines the benefits of data lakes and data warehouses.
Q: What is open data format standardization?
A: Open data format standardization refers to the widespread adoption of open standards for data formats, such as Apache Iceberg, Delta Lake, and Apache Hudi.
Q: What is federated IT?
A: Federated IT refers to a decentralized approach to IT, where responsibilities are decentralized to business units and monolithic architectures are replaced with hybrid ecosystems.
Q: What is data modeling?
A: Data modeling is the process of designing and structuring data to support application development and data consumption.
Q: What is AI’s impact on data analytics?
A: AI’s impact on data analytics is the ability to automatically deliver insights and recommendations, without the need for human intervention.
Q: What is embedded analytics?
A: Embedded analytics refers to the integration of real-time analytics into every application, enabling employees at all levels to make data-driven decisions instantly.
Q: What is small data?
A: Small data refers to targeted, higher-quality data that is more relevant and easier to work with, rather than the overwhelming abundance of data often referred to as the ‘data swamp’.
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