Genesis AI Data Engineering Agents Make Synthetic Data скачать в хорошем качестве

Genesis AI Data Engineering Agents Make Synthetic Data

Need synthetic data fast? Generate a full schema + realistic patterns with an AI data‑engineering blueprint. In this demo, we walk through Genesis’s Synthetic Data Generation Blueprint—a structured, multi‑phase workflow that lets our data agent create synthetic tables + data either by (1) repopulating an existing schema or (2) generating an entirely new schema from scratch. We kick off a mission by providing lightweight requirements: a list of asset management business questions and a dashboard sketch we want the data to support (fund performance, peer rankings, AUM growth, etc.). Then we use Replay to review how the agent worked through the blueprint phases, wrote documentation (with diagrams), generated Python-based data builders, and delivered a complete synthetic raw schema designed to roll into a bronze / silver / gold medallion pipeline. You’ll learn When synthetic data is most useful (demos, testing, pipeline development) How the Synthetic Data Generation Blueprint is structured (phases, actions, exit criteria) How to start a synthetic data mission with simple kickoff notes (questions + dashboard sketch) -How Replay lets you review long-running work (2+ hours of agent execution) How the agent designs realistic data patterns (not flat, “fake-looking” data) The output: a full synthetic raw schema + a handoff doc for the next mission (S2T → bronze/silver/gold → dashboards) Who it’s for: data engineers, analytics engineers, and platform teams who need safe, realistic synthetic datasets to accelerate delivery without using sensitive production data. ⏱️ Chapters 00:00 Why synthetic data matters for data engineering 00:18 Synthetic Data Generation Blueprint overview (6 phases) 00:45 Actions, context docs & exit criteria (guardrails) 01:16 Starting a mission + kickoff notes (requirements) 01:40 Asset management use case: questions + dashboard goals 02:25 Create mission: agent begins phase-by-phase execution 02:55 Fast-forward with Replay (DVR controls) 03:18 Replay highlights: 137-minute run time 03:38 Phase deep dive: understanding context + documenting the plan 04:20 Designing realistic patterns for dashboards (not flat data) 05:10 Tool use + Python generation (data builder scripts) 06:08 Mission complete: synthetic schema + handoff documentation 06:35 Data viewer: sample synthetic tables (trades, products, positions) 07:11 What’s next: S2T mapping → bronze/silver/gold → dbt/Snowpark/Databricks 07:50 End result: dashboard built on top of the synthetic data 08:11 Wrap-up: why this blueprint is a core data engineering capability Key takeaways 1. Synthetic data becomes genuinely useful when it’s shaped by real business questions and intended outputs (dashboards, KPIs). 2. Blueprints keep long workflows reliable via phases + exit criteria, and Replay makes them auditable. 3. The deliverable isn’t just tables—it’s a reusable spec + handoff that feeds the next pipeline missions. If this helped 👍 Like 🔔 Subscribe 💬 Comment What would you generate synthetic data for first—testing, demos, or training a new pipeline? #SyntheticData #DataEngineering #AIAgents #LLMOps #MedallionArchitecture #dbt #Snowflake #Databricks #AnalyticsEngineering