The article outlines key predictions for data infrastructure in 2026, highlighting significant shifts in the industry. One major point is the increasing integration of analytical systems into operational applications. As these systems evolve, they support a broader range of functions, moving away from traditional analytics to include roles in AI and consumer applications. This transformation has led to a larger market for data infrastructure vendors, which is expected to accelerate as user demands grow. Another focus is on the Apache Arrow ecosystem, which is nearing its 10th anniversary. Arrow has become ubiquitous in data stacks, with the main Python library downloaded over 250 million times monthly. However, funding and maintenance challenges loom, especially as new contributors enter the scene. The article emphasizes the importance of initiatives like the Arctos Alliance to sustain Arrow's development amid rising usage and the strain on maintainers. ADBC (Arrow Database Connectivity) is gaining traction as a standard for database connectivity, with backing from major companies like Databricks and Snowflake. Its adoption is expected to expand, solidifying its role in analytic applications. Meanwhile, open table formats like Apache Iceberg are moving from hype to proven infrastructure. Iceberg's community is active, and its operational scale is growing, with over 400,000 tables hosted on Amazon S3 in its first year. Lastly, multi-engine data stacks are becoming mainstream as organizations seek faster innovation and better interoperability. The decoupling of storage and compute, facilitated by open formats like Iceberg, allows companies to avoid vendor lock-in and makes it easier to adapt to changing needs. The article suggests that these trends will shape the data landscape significantly in the coming year.