Automating Streaming Pipelines with Databricks DLT Using GenAI Agents

Mar 1

6 min read

Having real-time data is no longer just a competitive edge. It's a necessity.

In today's fast-paced market, the ability to make swift, informed decisions can be a game-changer. With the growing demand for streaming data, businesses need solutions that are not only easy to implement but also scalable and manageable. Databricks DLT (Delta Live Tables) is just what the doctor ordered. But how can you get your streaming data pipelines live without relying on an expensive army of consultants working for years?

From decentralized source data to a centralized Data Platform

When it comes to streaming data, Amazon Kinesis, Kafka or Azure Event Hub are typically used. In a multi-cloud environment this can become a headache — building event-driven, real-time architectures often turns expensive, complex and difficult to maintain. Each streaming solution must be built separately, and multiple teams handling development and maintenance is more the rule than the exception. If only there were a single centralized solution to handle it all effortlessly...

And this is where Databricks comes in. Databricks runs on all major cloud platforms (AWS, Azure and Google Cloud), providing an easy way to standardize across cloud environments. You can build a standardized and identical data flow in Databricks and then replicate it across different cloud environments with minimal modifications. This makes development more efficient and reduces redundant work. Simply put, a streaming architecture looks like this:

Next-gen automation using GenAI agents

As shown in the diagram, Databricks provides everything needed within its ecosystem. However, let's not delve into the technical details of streaming architecture in this article. Instead, our focus is on business value — how to efficiently turn ideas into concrete, cost-effective streaming data pipelines. As the saying goes, 'a picture is worth a thousand words,' so let’s take it a step further and watch our GenAI DLT agent in action!

See GenAI DLT agent in action

Pretty neat, right? Of course, our DLT agent isn’t a magic bullet for everything — especially for the most complex data pipelines. But it continuously learns and improves. Thanks to standardization, each use case only needs to be configured once. I've given the agent full autonomy to run processes independently, retry in case of failure and leverage all necessary tools. Monitoring is done using MLflow Tracing. Here’s a high-level overview of the GenAI DLT agent architecture:

Solution architecture how to automate Databricks DLT pipeline creation using GenAI Agents — GenAI DLT Agent Solution Architecture

The tool names are quite self-explanatory, so I won’t go into detail about their functionalities. And yes, I have updated the tool names based on what is visible in the video above. Just like in the GenAI agent world, development is progressing at a rapid pace, and even demo videos become outdated quickly. Now, let’s walk through each component step by step to understand what’s happening.

DLT agent executor

Although a multi-agent approach is not being used in this case, going through this phase is still valuable. The DLT agent leverages an LLM model via Mosaic AI Model Serving endpoint, ensuring seamless governance over the deployed model and allowing for effortless model swaps. This means that as LLMs continue to evolve, the best-performing model can always be kept in use. Read more on Mosaic AI governance from the blog I have written earlier: Mosaic AI Gateway. In this context, short-term memory means the agent continuously retains every step of the process. This is not an interactive chatbot but a task-oriented agent, which eliminates the need for long-term memory. Since reliability is a top priority for this agent, I have minimized the role of the thought process in planning phase. The problem being addressed is straightforward and unnecessary hallucinations are avoided by keeping the agent focused on execution rather than excessive decision-making.

Setting up the integration

Azure Event Hub is being used as the "source system" for real-time data, streaming it into Databricks via the Apache Kafka protocol. To establish a secure connection, authentication is done using a dedicated Service Principal, ensuring maximum security. The Service Principal is pre-authorized for the designated Namespace in Azure, so that part has been set up in advance. Of course, this step could also be automated — either as part of a standardized Terraform CI/CD pipeline or triggered automatically by the agent with parameterized execution. But in this example it's kept it out of scope. As a first step, the agent uses the tool fetch_event_hub_config to configure the connection to the correct Event Hub.

Retrieving streaming data

The next step is retrieving raw data from Event Hub. For this, The agent uses the tool stream_event_hub_data, which fetches raw data based on the configurations set in the previous step. To keep token usage under control, the example data retrieval is limited to five rows.

Decoding data

Now that the data is available, it needs to be decoded from binary format into a pyspark dataframe. For this, the agent can use the transform_event_hub_stream tool, which automates the decoding process seamlessly. I won’t go into the details here, but let me tell you, it works surprisingly smoothly.

Validating Data

Now that the data has been decoded from binary format into a business-ready dataframe format which can be used in silver delta table, it needs to be validated. For this, the agent can use the validate_transformed_stream tool, which allows it to execute the decoding logic and verify its functionality.

Creating the DLT pipeline

Now it's time to create the DLT pipeline. This requires using two tools. First, fetch_dlt_pipeline_config provides the agent with the necessary configurations for building the DLT pipeline (including catalog name, schema name, and table name). In some cases, this tool is used right at the beginning of the process (the required information is validated in the beginning, so the order activating this tool doesn't matter so much). Once the configurations are in place, the agent can then create the DLT pipeline using create_dlt_pipeline, which leverages outputs from the earlier steps. This tool automatically builds a DLT pipeline out-of-the-box, utilizing prebuilt frameworks — all the way up to the gold table. I don’t want the agent to handle coding entirely, as that could lead to inconsistent outputs. Instead, frameworks helps the agent maintain standardization and ensure the best possible outcome. While there’s plenty of room for further enhancements, even at this stage, the process is robust and reliable.

Validation

Once everything is in place, validation should not be overlooked. To address this, the agent implementation includes a dedicated LLM-as-a-Judge component, which performs a final validation of the agent’s execution. With the automatically generated Agent Flowchart, the entire process is visually mapped out, making it easy to track each stage of execution. Beyond simple validation, the "Judge" elevates performance assessment by analyzing the agent’s overall effectiveness. It evaluates how well the agent completes its assigned task, considering both tool utilization and the reasoning process behind its decisions.

Monitoring

Continuous validation and quality control are essential to ensuring reliability and should always be a priority. That’s where MLflow comes into play. Every run is automatically logged under an experiment using MLflow Tracing, enabling seamless and automated quality monitoring. Databricks also provides out-of-the-box capabilities for model serving endpoint monitoring, making monitoring solutions even more efficient and enjoyable. To streamline oversight even further, I’ve built a comprehensive AI/BI Dashboard giving me a clear, near real-time overview of all key metrics at a glance.

Automating Streaming pipelines with Databricks DLT (Delta Live Tables) Using GenAI Agents

Building streaming pipelines has traditionally been time-consuming and complex, requiring deep expertise — and that doesn’t scale cheaply. But the solution lies in leveraging GenAI Agents. By embedding best practices and expertise into agent-driven logic, you can streamline and automate the process like never before. This example demonstrated how effortlessly it can be done, taking automation in data engineering to a whole new level. Of course, agents need to be fine-tuned to fit each company’s specific needs and enhanced with new functionalities over time. One of the key functionalities is data quality management. An exciting future integration opportunity in this area is Databricks Labs' DQX.

While this was just a high-level glimpse into the possibilities, one thing is already clear: Automating streaming pipelines with Databricks DLT (Delta Live Tables) using GenAI agents is a highly efficient approach. To ensure reliability, this was designed as a sophisticated function-calling agent rather than relying heavily on ReAct or Chain-of-Thought reasoning. This approach makes it easier to integrate the DLT agent into a larger agent ecosystem, allowing for seamless collaboration within complex workflows. This is something I’m actively working on and it will take things to the next level — but more on that later!

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