ASF Corporation

[asfcorporation]

Hello Databricks Community,

I'm representing ASF CORPORATION, a piling, foundation engineering and construction company established in 2017 and based in Dhaka, Bangladesh.

Our company specializes in hydraulic rotary piling, rotary drilling, auger boring, sheet piling, cast-in-situ piling, foundation construction, steel bracing and excavation support for commercial, industrial and infrastructure projects.

As construction projects involve significant amounts of technical and operational data, we are interested in exploring how Databricks could support better data management, reporting and project performance analysis.

For example, piling projects generate records related to drilling depth, pile diameter, soil conditions, concrete consumption, equipment utilization, daily progress and quality control.

I would appreciate suggestions from the community on how to use Databricks to organize these records, create useful project dashboards and identify opportunities to improve construction operations.

Are there any recommended Databricks tools, workflows or practical examples suitable for engineering and construction project data?

I look forward to learning from your experiences and insights.

Thank you.

ASF CORPORATION
Dhaka, Bangladesh

[saurabh18cs]

Hi @asfcorporation see if you can gain some inspiration from here :

1) https://www.databricks.com/solutions/accelerators?industry=Manufacturing

2)https://www.databricks.com/resources/demos/tutorials/lakehouse-platform/iot-and-predictive-maintenan...

3) https://www.databricks.com/resources/architectures?industries=1651

Br

Saurabh

[Mateus-Moreira]

Hi @asfcorporation, and welcome to the Databricks Community!

Beyond the usual Lakeflow + Unity Catalog + AI/BI Dashboards stack, I’d suggest not inventing your own schema: geotechnical data already has open standards. AGS4 (UK) and DIGGS (US, by ASCE Geo-Institute) cover boreholes, SPT, lab tests and pile records.

• python-ags4 (gitlab.com/ags-data-format-wg/ags-python-library) reads and validates AGS4 files into pandas DataFrames, so you can land each AGS group as a Delta table in your bronze layer.
• The DIGGS schema (github.com/DIGGSml) is a good reference data model even if you don’t use the XML.
• groundhog (github.com/snakesonabrain/groundhog) and openpile let you run pile capacity and soil correlation calculations in notebooks, so you can compare designed vs. as-built (predicted capacity vs. actual drilled depth and concrete overconsumption per pile). That’s usually where the operational insight is.

From there, a medallion layout (raw daily logs → cleaned pile/borehole tables → KPIs per rig/site) plus a Genie space lets site managers ask questions like “which rig had the highest concrete overbreak last month?” in plain English.