“Data pipelines”

Hub for data-engineering pipeline work — storage/ingest/serve choice plus shared discipline (idempotency, incremental-not-full-recompute, schema fencing…

Create ML pipeline DAGs for data ingestion, preprocessing, training, evaluation, and deployment.

Manage Jitsu data pipelines: destinations, streams, connections, functions, live events.

Build scalable data pipelines, modern data warehouses, and real-time streaming architectures.

Prefect is a modern workflow orchestration framework for Python data pipelines.

Data and Database Engineer expert in schema design, query optimization, and data pipelines.

Use for data pipeline engineering, ETL/ELT design, data lake and warehouse architecture, stream processing and data quality.

Darshan I/O profiler MCP server for analyzing I/O trace files

Create data pipeline and analytics architecture diagrams using PlantUML syntax with database/analytics stencil icons.

Use for ingestion, ETL or ELT, data pipelines, mapping, cleansing, validation, reconciliation, lineage, warehousing, dimensional models, semantic layers, file…

Create cdc pipeline creator operations.

Use for data pipelines, ingestion, transformation, orchestration, data quality, lineage, batch/stream processing, warehouses, lakes, and analytics engineering.

Draw line, bar, scatter, histogram, heatmap and time-series plots from tabular data.

Load tabular data, then clean, filter, group, merge and summarize it with pandas.

Summarize Parquet files, preview columns, read row slices and aggregate values.

Visualize simulation results in ParaView: load fields, then slice, contour, color and render them.

Build beam pipeline builder operations.

Sort, filter and summarize large log files by timestamp, level or keyword.

Migrates Synapse Data Factory pipelines to Fabric Data Factory, turning linked services into Fabric connections, inlining datasets into activities, converting…

Operator-owned scientific dataset discovery for remote agents

Build end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment.

AI agents build, train, and analyze BCI/EEG pipelines: data, models, experiments, live sessions.

Python framework for live data processing and RAG pipelines that run on a Rust engine.

Analyze an arbitrary codebase to auto-detect workflows, data pipelines, and file dependencies using putior's put_auto() engine.