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SQL and Python Analytics for Industrial Data Platforms

30 Chapters

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Industrial data platform landscape

data sources, historians, SCADA, MES, ERP, and the analytics stack.

Industrialplatform

SQL foundations for industrial time-series

SELECT, filtering, aggregation, and window functions on sensor data.

Python analytics environment setup

connecting to industrial databases, ODBC/JDBC, SQLAlchemy, and pandas.

analyticsenvironment

Data modeling for industrial assets

equipment hierarchies, tag naming conventions, and relational schema design.

modelingindustrial

Ingesting historian data with SQL and Python

PI System, Wonderware, and open-source time-series databases.

Ingestinghistorian

Time-series alignment and resampling

handling irregular timestamps, gaps, and clock drift in plant data.

Time-seriesalignment

SQL window functions for rolling metrics

moving averages, rate of change, and cumulative production counters.

Python pandas for industrial ETL

cleaning, transforming, and enriching sensor and event data.

Joining operational and business data

linking MES work orders with ERP inventory and SQL analytics.

operationalbusiness

Data quality assessment

outlier detection, missing value imputation, and sensor validation with SQL and Python.

assessmentdetection

OEE calculation pipeline

SQL aggregation and Python visualization for overall equipment effectiveness.

Anomaly detection in process data

statistical methods, isolation forests, and SQL-based thresholding.

detectionstatistical

Predictive maintenance feature engineering

vibration, temperature, and pressure signals via SQL and Python.

Predictivemaintenance

SQL for batch and lot traceability

genealogy queries, recursive CTEs, and material flow analysis.

SQLtraceability

Python time-series forecasting

ARIMA, Prophet, and gradient boosting for production and demand.

time-seriesforecasting

Energy consumption analytics

SQL rollups and Python regression for plant utilities and sustainability.

consumptionanalytics

Integrating Python with BI tools

exporting SQL results to Power BI, Tableau, and Grafana dashboards.

Integratingexporting

Real-time analytics patterns

streaming SQL, Kafka, and Python microservices for edge and cloud.

Real-timeanalytics

Query performance tuning for industrial big data

indexing, partitioning, and columnar stores.

performanceindustrial

Python parallel and distributed processing

Dask, Spark, and SQL engines for large historian datasets.

paralleldistributed

Building a data pipeline orchestration

Airflow, Prefect, and SQL-based scheduling for industrial workflows.

Buildingpipeline

root cause analysis of production downtime using SQL drill-down and Python correlation.

analysisproduction

quality prediction in manufacturing with SQL feature extraction and Python ML models.

predictionmanufacturing

supply chain analytics integrating SQL inventory data with Python demand forecasting.

analyticsintegrating

Deploying analytics models

packaging Python models, SQL stored procedures, and API endpoints.

Deployinganalytics

Data governance and security in industrial platforms

access control, auditing, and SQL injection prevention.

governancesecurity

Cloud industrial data platforms

Azure IoT, AWS IoT Analytics, and Snowflake with SQL and Python.

industrialplatforms

Edge analytics with Python and SQLite

local processing, store-and-forward, and cloud synchronization.

analyticsSQLite

Advanced SQL for industrial analytics

geospatial queries, JSON parsing, and user-defined functions.

Capstone project

end-to-end SQL and Python analytics solution for a real industrial data platform scenario.

Capstoneend-to-end