Min - Ssis-732-en-javhd-today-0804202302-26-30

[00:00:00] Package started. [00:00:01] Kafka source read 1,200 messages (total 5.1 MB compressed). [00:00:02] Payload decompressed to 23.4 MB. [00:00:04] Web Service Task sent payload to http://localhost:8080/parseTelemetry. [00:00:06] Java parser processed data in streaming mode, memory usage peaked at 1.6 GB. [00:00:08] CSV output written to /tmp/parsed_telemetry.csv (3.2 MB). [00:00:10] Flat File Destination completed. [00:00:12] Package completed successfully in 12.1 seconds. The room erupted again—this time with applause. Dr. Liu turned to the camera, his eyes twinkling. “Ladies and gentlemen, we have just demonstrated the : a fully functional, production‑grade SSIS package that integrates Java code, streams data from Kafka, compresses and decompresses on the fly, and can be extended to edge devices. All of this in less time than it takes to brew a cup of coffee.” Maya felt a warm surge of accomplishment. She imagined herself presenting a similar demo to her own team next week. Epilogue: The After‑Hours Conversation When the session ended at 08:30 AM , Maya lingered in the virtual lobby, still buzzing with ideas. Dr. Liu opened a private chat with her. Dr. Liu: “Maya, I noticed you asked a question about the error handling for malformed LIDAR data. I’ve got a GitHub repo with a sample Retry Policy and **Dead

Lila, a petite woman with a confident posture, typed: “Apologies for the late entry. I’m fascinated by this hybrid approach. At Orion we’ve been exploring edge‑to‑cloud pipelines that run Java analytics on the device and push results directly to Azure. Could SSIS‑732 handle a scenario where the Java component runs on an Azure IoT Edge module instead of a Docker container on the server?” A hush fell over the virtual room. Dr. Liu smiled, clearly pleased. Dr. Liu: “Great question, Lila. The beauty of the JAVAVD Bridge is that it abstracts the execution environment. Whether the Java code runs in a Docker container on‑premises, on an Azure IoT Edge device, or even in a Kubernetes pod , the SSIS package merely sends an HTTP request. The only thing that changes is the endpoint URL and authentication.” He shared a quick diagram: an IoT Edge device running a Java microservice , exposing an HTTPS endpoint secured with Azure AD . The Web Service Task in SSIS could use OAuth2 to obtain a token and call the edge service. This architecture would dramatically reduce latency, because raw sensor data would be processed at the edge before being aggregated in the cloud. SSIS-732-EN-JAVHD-TODAY-0804202302-26-30 Min

Error: OutOfMemoryError: Java heap space The audience gasped. The stalled, and the execution stopped at 14.8 seconds . Dr. Liu’s smile faded into a grimace. [00:00:00] Package started

Finally, a wrote the CSV to /tmp/parsed_telemetry.csv . Dr. Liu ran the package. In the Execution Results window, the package executed in 12.3 seconds —far faster than Maya expected for a process involving a Docker container, a Kafka source, and a Java library. [00:00:10] Flat File Destination completed

Next, he added a (the bridge to Java). He pointed it at a locally running Docker container: