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What You'll Learn
Key takeaways
- AI agents act on data without adjusting for staleness — a stale read becomes a wrong action, not just an outdated report.
- Informatica (SuperPipe, Agentic Integration), Snowflake (Openflow, Snowpipe Streaming, Dynamic Tables), and Databricks (Lakeflow, Lakebase, Lakehouse//RT) have each rebuilt ingestion so real-time data lives in the same governed environment as everything else
- The decades-old split between operational and analytical systems is collapsing — agents apply operational access patterns to analytical data.
- Not everything needs streaming. Build real-time freshness only where a time-sensitive decision sits on the other end of the pipeline.
- Governance must run inline at streaming speed — otherwise real-time just delivers bad data faster.
Data infrastructure teams have been flagging the same failure pattern for quite some time now. Example: A fraud detection agent checks a customer’s billing address, but the change from five minutes ago hasn’t propagated through the pipeline yet. So, it sees the old address, flags a legitimate large purchase as anomalous, and blocks it. The agent here isn’t malfunctioning. It’s reading a snapshot and treating it as the present.
That’s the gap AI agents have exposed in enterprise data infrastructure. Agents don’t have judgment built in. They observe, decide, and act in a continuous loop, and the quality of that loop depends entirely on how current their inputs are. Closing that gap doesn’t mean rebuilding every pipeline; it means understanding what changed in the data ingestion layer and where that change matters. This blog walks you through the modern data ingestion and streaming stack built to support real-time agents. Read on.
Why batch data pipelines don't work for AI agents
Batch data works when a person reviews the output and can mentally account for the delay. For instance, a 2 a.m. load feeding a 9 a.m. report is fine, because the person reading it understands that the data is a few hours stale and adjusts their judgment accordingly. Agents don’t do that adjustment. AI agents don’t have that judgment built in, and that gap is where most agent failures start.
The fix isn’t to stream every table in the enterprise. It’s to know which workflows are latency-sensitive enough that staleness becomes a wrong action, and build for those deliberately.
What the modern real-time data ingestion stack looks like
Modern data platforms such as Informatica, Snowflake, and Databricks have each rebuilt their ingestion layers around the same principle. Real-time data should live in the same governed environment as everything else.
Here’s a deep dive:
Platform deep dive
Informatica
Informatica’s real-time story has two layers. One established, one new. SuperPipe, built on Snowflake’s Snowpipe Streaming, has been loading data into Snowflake up to 3.5x faster than prior methods, and it remains the workhorse for CDC-style replication at scale. What’s new is Informatica Agentic Integration, which extends that real-time flow to unstructured data as well as structured, on the premise that an agent needs the full context of the business.
Platform deep dive
Snowflake
Snowflake’s answer is Openflow, a managed ingestion service built on Apache NiFi that connects Kafka, Kinesis, SharePoint, and dozens of SaaS sources directly into Snowflake, paired with a newer Snowpipe Streaming architecture built for high-throughput, low-latency loads. On top of that, Dynamic Tables let teams declare a freshness target in SQL and let Snowflake manage the incremental refresh. This way, the same pipeline can move from hourly to near-real-time with a parameter change instead of a rebuild.
Platform deep dive
Databricks
Databricks takes a similarly comprehensive approach, collapsing the line between operational and analytical entirely. Lakeflow unifies ingestion, transformation, and orchestration under Unity Catalog, with Zerobus Ingest handling high-volume event streams and Real-Time Mode in Spark Declarative Pipelines hitting millisecond-scale processing without a second engine like Flink running alongside it. Lakebase adds an operational Postgres layer synced to the lakehouse in near real time, and Lakehouse//RT serves millisecond-latency queries directly on governed Delta and Iceberg tables.
3.5x
Faster data loading into Snowflake with Informatica SuperPipe, compared to prior methods — the workhorse for CDC-style replication at scale.
Why operational & analytical data are converging
Agents need governed, analytical-grade data at operational speed, and that’s collapsing a separation enterprises have relied on for decades.
For most of the last two decades, enterprises ran two separate worlds. An operational database that handled transactions, and an analytical warehouse that handled reporting, connected by an overnight ETL job. That split made sense when the two workloads had genuinely different access patterns. It stops making sense when an agent needs to read a governed analytical record and act on it in the same second, which is an operational access pattern applied to analytical data.
That’s precisely what Lakebase, Lakehouse//RT, and Snowflake’s Dynamic Tables are responding to. Not a request for faster batch jobs, but a collapse of the assumption that operational and analytical data need to live in different systems with different rules. The practical implication is architectural. Teams that spent the last few years building a clean separation between “the OLTP system” and “the warehouse” now have to decide, workload by workload, whether that separation still earns its complexity.
How to choose the right data freshness pattern for each workload
Not every pipeline needs to be real-time. The right freshness pattern depends on whether a stale read would change the decision being made.
Streaming ingestion at high volume costs more to run than batch. The workloads worth that cost are the ones where an agent is making a decision a stale read would get wrong. The workloads that don’t need it are still better served by batch or a scheduled Dynamic Table refresh.
The architecture question isn’t “streaming or batch.” It’s which workloads have an agent, or a person, making a time-sensitive decision on the other end, and building freshness only where that’s true.
What real-time data architecture delivers
Real-time architecture is already replacing batch-driven approaches in production. But speed on its own isn’t the win, and it’s worth being precise about why.
A pipeline that streams bad data into an agent just produces bad decisions faster than a batch pipeline would have. Batch processing has always had a built-in safety margin. The delay between when data lands and when it’s used gives a data quality check, a validation job, or a human reviewer time to catch an error. Real-time ingestion removes that margin. If schema validation, deduplication, and lineage tracking don’t run inline, at the same speed as the data itself, streaming doesn’t reduce risk; it just compresses the window available to catch it. The organizations getting real value from real-time architecture are the ones treating governance as part of the pipeline’s speed requirement.
Done right, that’s what real-time architecture delivers – agents acting on data that’s both current and trustworthy, not just fast.
Leveraging the LumenData Advantage
Informatica, Snowflake, and Databricks each bring strength of their own. Informatica’s core strength is moving and governing data as it changes, as SuperPipe shows, while Snowflake and Databricks have each built native ingestion and processing power directly into their own platforms, as Openflow and Lakeflow show. The advantage lies in architecting all three together so a change in a source system reaches a governed, agent-ready record.
LumenData has the credentials and the cross-platform reach to do that. We have a Platinum Enterprise Partner status with Informatica, Premier Services Partner status with Snowflake, and Approved Delivery Partner status with Databricks. That depth means we design the right freshness pattern for each workload instead of defaulting to whichever platform a team happens to know best, and carry the governance all the way through to where agents act on the data, not just to where it lands.
Govern first, then decide what needs to be fast.
Ready to architect a streaming and ingestion strategy? Connect with LumenData to build a real-time data foundation your agents can trust.
About LumenData
LumenData is a leading provider of Enterprise Data Management, Cloud and Analytics solutions and helps businesses handle data silos, discover their potential, and prepare for end-to-end digital transformation. Founded in 2008, the company is headquartered in Santa Clara, California, with locations in India.
With 150+ Technical and Functional Consultants, LumenData forms strong client partnerships to drive high-quality outcomes. Their work across multiple industries and with prestigious clients like Versant Health, Boston Consulting Group, FDA, Department of Labor, Kroger, Nissan, Autodesk, Bayer, Bausch & Lomb, Citibank, Credit Suisse, Cummins, Gilead, HP, Nintendo, PC Connection, Starbucks, University of Colorado, Weight Watchers, KAO, HealthEdge, Amylyx, Brinks, Clara Analytics, and Royal Caribbean Group, speaks to their capabilities.
For media inquiries, please contact: marketing@lumendata.com.
Ready to architect a streaming and ingestion strategy?
Authors
Reference links
- Informatica Announces SuperPipe for Snowflake with Up to 3.5x Faster Data Integration and Replication
- Informatica Unveils Headless Data Management, Agentic MDM at Informatica World 2026 — BigDATAwire
- Snowflake Openflow: Unified Data Integration at Scale
- Lakeflow: A New Era of Agentic Data Engineering — Databricks
- Introducing Lakehouse//RT — Databricks
- LumenData: AI and Data Consulting
