Dear Databricks Community, After coming back from Summit 2026, one thought stayed in my mind.
Data engineering is changing again. Earlier, most of our work was around pipelines, tables, jobs, transformations, reports, and dashboards. All of that is still important. But now, data is moving much closer to decisions, applications, AI, and real-time actions.
A few topics stood out to me strongly: LTAP, Lakebase, Genie, and Lakehouse RT.
LTAP made me think about how analytics and transaction processing are coming closer. For a long time, applications handled transactions and analytics systems handled reporting. Then we moved data between them using batch, streaming, CDC, or ETL pipelines.
But today, users want fresh insights. Applications need faster intelligence. AI systems need current context. So the gap between operational data and analytical data needs to become smaller.
Lakebase also feels like an important step. To me, it is not just another database. It shows how database architecture is evolving toward the lakehouse. If operational data can work more closely with trusted lakehouse data, it can reduce duplication, simplify architecture, and open new patterns for Data + AI applications.
Genie feels very practical too. Dashboards are useful, but they usually answer only the questions we already planned for. Business users always have follow-up questions. Why did this number change? Which team, region, or process is driving it? What happened this week?
Natural language analytics can make this easier. But one thing is clear: AI can only answer well when the data foundation is strong. Clean data, trusted metrics, governance, permissions, and context still matter a lot.
Lakehouse RT is another exciting direction. Earlier, real-time data was needed only for special use cases. Now, many business problems need faster updates. Fraud, inventory, customer experience, monitoring, recommendations, operations, and AI agents all need fresh data.
The real question is not only, โCan we process the data?โ
It is, โCan we process it while it is still useful?โ
My biggest takeaway from Summit 2026 is simple.
The future data platform is becoming more connected. Transactions, analytics, AI, applications, and real-time data are coming closer.
This means data engineers need to think beyond pipelines and reports. We need to think about the full solution.
What problem are we solving?
Who will use this data?
How fast do they need the answer?
Can AI make this workflow easier?
Can real-time data improve the decision?
For me, this is the exciting part of working with Databricks. A small idea can become a POC. A POC can become a dashboard. A dashboard can become a real-time insight. A real-time insight can become an action.
That is how I see the next phase of data engineering. It is not only about moving data faster. It is about helping people make better decisions sooner.
After Summit 2026, which area are you most excited to explore with Databricks: LTAP, Lakebase, Genie, Lakehouse RT, or another Data + AI use case?
