It’s that time of year where we shake the snow globe and look into its crystal depths for predictions about data and analytics for the next year. While breakthrough technologies capture headlines, the industry’s foundation remains remarkably stable – built on proven technologies that continue to adapt and evolve. This analysis explores key trends shaping the future of data infrastructure, analytics, and AI integration.
The data industry’s strength lies in its pragmatic approach to innovation. SQL’s five-decade reign as the lingua franca of data shows no signs of weakening, even as new query languages like PRQL and Malloy advance the conversation around developer ergonomics. Postgres continues its ascent as the preferred open-source operational database, with its wire protocol increasingly adopted across modern data platforms. These technologies exemplify the industry’s preference for reliable, battle-tested solutions that evolve thoughtfully rather than revolutionary shifts.
The Engineering Evolution of Analytics
The analytics domain is undergoing a fundamental transformation by adopting software engineering practices that have proven their value over decades of enterprise software development. This isn’t merely a trend – it’s the application of battle-tested methodologies that have transformed software delivery since the early 2000s. Version control, code review, continuous integration, and systematic testing have consistently demonstrated their ability to improve code quality and team collaboration across industries.
Data teams are now applying these established practices to analytical workflows:
- Version control systems that have managed mission-critical software for decades now track analytical code changes
- Code review processes, refined through years of software development, ensure analytical quality
- Package management approaches, proven through years of software distribution, now organize analytical assets
- Automated testing frameworks, standard in software engineering since the early 2000s, validate analytical outputs
This convergence creates hybrid roles that combine deep analytical expertise with robust engineering principles, leading to more maintainable, collaborative, and reliable analytical systems. The distinction between data analysts and engineers continues to blur, reflecting a maturity model seen previously in software engineering’s evolution.
Enterprise Data Catalogs Still Searching for Traction
Enterprise data catalogs face a critical inflection point. Despite their essential role in data governance, traditional standalone catalogs struggle with adoption due to their isolation from daily workflows. Because catalogs are not where people actually do their day-to-day work, they force users to “pivot” to a separate interface just to access metadata. Yet, up-to-date metadata is critical for data practitioners. Users need it embedded in their existing workflows to see details like data freshness, lineage, and quality. Because metadata is the real black, watch for more platforms to build out lightweight, embedded catalogs of their own—starting with data orchestration tools. By integrating metadata directly where decisions are made, these new catalogs aim to overcome the adoption challenges faced by traditional, standalone solutions.
Open Table Formats on the Rise
Open Table Formats aren’t new, but they had a major breakout this year:
- Snowflake and BigQuery both announced support for reading and writing to Apache Iceberg tables.
- Databricks acquired Tabular, the commercial offering behind Apache Iceberg, signaling even deeper investment in open formats.
- Amazon unveiled a new S3 bucket type specifically tied to Iceberg, laying the groundwork for more straightforward enterprise adoption.
Looking ahead to 2025, expect deeper adoption as more large organizations move workloads onto Iceberg tables on object storage. This will be driven by a growing ecosystem of data loaders, orchestration engines, and BI tools that can read and write Iceberg tables directly. As Iceberg becomes the de facto standard for table-level transactions, schema evolution, and ACID compliance on the data lake, it will help unify data environments across different platforms—especially as companies continue to embrace a “lakehouse” approach.
Apache Arrow ADBC: The Next-Gen Connectivity Standard
Database connectivity standards like ODBC and JDBC date back to the 1990s, when transactional, row-based systems were the norm. Today’s analytics platforms, however, predominantly use columnar storage formats—think Snowflake, Parquet, and others—which makes row-wise data transfers inefficient. Apache Arrow ADBC (Arrow Database Connectivity) solves this by eliminating the costly step of serializing and deserializing data between formats. Instead, Arrow ADBC keeps data in its native columnar form, drastically improving performance and efficiency when moving data between systems.
Already adopted by forward-leaning platforms like Snowflake and DuckDB, Arrow ADBC is poised to make further inroads in 2025 as new platforms embrace its high-speed, columnar-friendly approach. Arrow itself underpins many modern BI tools (e.g., GoodData, Rill) and dataops platforms (e.g., Coginiti), meaning the entire data ecosystem—ingestion, transformation, and analytics—can benefit from faster, more seamless data transfer. Expect Arrow ADBC to become a key part of the analytical data stack in the coming year.
Commoditization of Large Language Models
This year, we saw the commodification of large language models (LLMs), with a number of companies introducing alternatives to OpenAI’s GPT-4 and Anthropic’s Claude. Examples include Google Gemini, Meta Llama, Amazon Nova, IBM Granite, and LG EXAONE—demonstrating that with enough money and a bit of talent, training foundation models is within reach for many organizations. However, OpenAI’s latest “o” series of reasoning models proves that new architectures can bring fresh advances beyond simply scaling existing models, which appears to be hitting a practical limit. Moving into 2025, expect fast followers to adopt these new reasoning-focused approaches while also working to reduce the extraordinary cost of inference—a critical factor for broader enterprise adoption.
The Promise of Model Context Protocol
Anthropic’s proposal for the Model Context Protocol (MCP) represents an early but significant step toward standardizing model interactions. While still in development, this initiative aims to address critical enterprise needs:
- Standardized interfaces for model interaction
- Persistent context management across requests
- Simplified integration with existing tools and services
- Enhanced security and governance capabilities
As more data tools adopt MCP, developers can seamlessly plug in new LLM services with minimal overhead, ultimately speeding up development cycles and lowering the cost of integration.
Proliferation of Smaller Models
Another development to watch in the coming year is the rise of smaller models:
- Faster Inference: Models like Amazon Nova Micro and Google Gemini Flash are designed for near-real-time responses, making them ideal for latency-sensitive use cases (e.g., in database analytics).
- On-Device and Offline: Even tinier variants, like Google’s Gemini Nano and Meta’s Llama-edge, can run directly on devices or within local applications. This opens the door to offline functionality and local data processing—especially relevant in privacy-conscious industries like healthcare and finance.
- Domain-Specific Specialization: As smaller models become cheaper to train and deploy, we’ll see more domain-specific fine-tuning for tasks like real-time translation, code completion, and specialized analytics. Businesses can handpick the best-fit model for their exact needs.
Looking Ahead
As we move into 2025, the data and analytics industry continues to demonstrate its characteristic blend of innovation and pragmatism. The successful integration of emerging technologies – from open table formats to specialized AI models – will depend on their ability to deliver practical value while integrating smoothly with existing workflows and infrastructure.
Organizations should focus on strategic adoption of these technologies, prioritizing solutions that enhance existing capabilities while maintaining operational stability. The key to success lies not in chasing every new innovation, but in thoughtfully selecting and implementing technologies that align with long-term organizational objectives.
Here’s to an innovative and productive 2025!
