𝗦𝘁𝗿:::𝗟𝗮𝗯 𝗦𝘁𝘂𝗱𝗶𝗼 - Real-time Stream Processing on a Telecoms use case I just ran a full 𝗧𝗲𝗹𝗲𝗰𝗼𝗺 𝗡𝗲𝘁𝘄𝗼𝗿𝗸 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 pipeline live inside Str:::Lab Studio - and here's what it looked like. 𝗪𝗵𝗮𝘁'𝘀 𝗿𝘂𝗻𝗻𝗶𝗻𝗴 𝗶𝗻 𝘁𝗵𝗶𝘀 𝗱𝗲𝗺𝗼: A synthetic RAN (Radio Access Network) pipeline - 200 cell towers streaming telemetry at 𝟮𝟬𝟬 𝗲𝘃𝗲𝗻𝘁𝘀/𝘀𝗲𝗰 across 6 regions. 9 parallel Flink jobs. 9 Kafka topics. All observable in real time. 𝗧𝗵𝗲 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲 𝗰𝗼𝘃𝗲𝗿𝘀: • Tower enrichment - decoding raw integers into regions, tower types, signal quality tiers and SLA classes • 1-minute tumbling window KPI aggregation per region • Rolling Signal Quality Index using HOP windows (30s slide / 5min range) • Packet loss + high-latency alert filtering • Handover anomaly detection • SESSION window outage risk scoring (closes after 60s of tower silence) • 5-minute capacity planning metrics • SLA breach event scoring • Live continuous SELECT into the Results tab for NOC monitoring 𝗪𝗵𝗮𝘁 𝘆𝗼𝘂'𝗿𝗲 𝘀𝗲𝗲𝗶𝗻𝗴 𝗶𝗻 𝘁𝗵𝗲 𝗚𝗜𝗙: • JVM Heap per job - memory consumption tracked live across all 9 running jobs, showing how each pipeline branch carves out its own heap footprint • Live metric graphs - throughput, backpressure indicators and event rates per pipeline, updating in real time as the datagen floods 200 rec/s through the cluster • Checkpointing metrics - checkpoint duration, size and alignment times for each job with EXACTLY_ONCE mode enabled and state persisted to

/tmp/flink-checkpoints

• Job node live events - per-operator stats on the execution graph: records in/out, watermark lag, and busy/idle ratios at the subtask level All of this running locally. No cloud. No infra setup beyond Docker. 𝗪𝗮𝗻𝘁 𝘁𝗼 𝘁𝗿𝘆 𝗶𝘁 𝘆𝗼𝘂𝗿𝘀𝗲𝗹𝗳? The full pipeline SQL is in the repo under example directory. Spin up the stack, paste the queries, and watch your cluster come alive. • 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗱𝗼𝗰𝘀: https://str-lab.io/docs • 𝗗𝗼𝗰𝗸𝗲𝗿 𝗶𝗺𝗮𝗴𝗲: codedstreams/strlabstudio - Docker Image • 𝗚𝗶𝘁𝗛𝘂𝗯 𝗿𝗲𝗽𝗼: coded-streams/strlabstudio 𝘐𝘧 𝘵𝘩𝘪𝘴 𝘭𝘰𝘰𝘬𝘴 𝘶𝘴𝘦𝘧𝘶𝘭 𝘵𝘰 𝘺𝘰𝘶: 1. Give the repo a star on GitHub - it genuinely helps the project grow 2. Leave a review on docker hub so others can find it. 3. Drop a comment below - questions, feedback, or what pipeline you'd want to run next More demos coming .😎

Looks pretty good! Is there a video version of the GIF?

yes there is a video. I am planning on doing a video demo on connecting the studio to a cluster environment, create a demo pipeline and also use the pipeline builder to build a pipeline without using a SQL