Case Study 2: TurbineTech Sensor Data at Scale
Background
TurbineTech operates 1,200 wind turbines across the western United States. Each turbine is equipped with 847 sensors, but the predictive maintenance team focuses on 6 critical sensors for bearing health monitoring: vibration (mm/s), temperature (C), acoustic emission (dB), rotational speed (RPM), oil viscosity (cSt), and power output (kW). These 6 sensors are sampled once per second, producing 518,400 readings per turbine per day. Across the fleet, that is 622 million rows per day, or approximately 18.7 billion rows per month.
The data lives in a PostgreSQL database, partitioned by month. The team needs to:
- Extract one month of data for 50 turbines flagged by the operations team
- Compute daily rolling statistics (mean, std, max, min, trend) per turbine per sensor
- Detect anomalous days where sensor behavior deviates from the turbine's baseline
- Produce a risk-scored summary for the maintenance scheduler
The challenge: 50 turbines times 30 days times 518,400 readings per day is approximately 777 million rows. At roughly 48 bytes per row (6 float32 sensors + timestamp + turbine_id), that is approximately 37 GB --- too large for pandas, comfortably within Dask or Polars territory, but the extraction from PostgreSQL is the true bottleneck.
This case study focuses on the SQL optimization and Python processing pipeline.
The Data
We simulate the sensor data to demonstrate the pipeline. In production, this data would come from the PostgreSQL database.
import numpy as np
import pandas as pd
import polars as pl
import dask.dataframe as dd
import time
import os
import warnings
warnings.filterwarnings('ignore')
np.random.seed(42)
# --- Configuration ---
N_TURBINES = 50
N_DAYS = 30
READINGS_PER_DAY = 86_400 # 1 per second (simplified from 518,400 for demo)
N_SENSORS = 6
OUTPUT_DIR = 'turbinetech_sensor_parquet'
os.makedirs(OUTPUT_DIR, exist_ok=True)
# Sensor baselines (each turbine has slight variation)
sensor_configs = {
'vibration_mm_s': {'base': 2.5, 'std': 0.4, 'turbine_var': 0.3},
'temperature_c': {'base': 55.0, 'std': 3.0, 'turbine_var': 5.0},
'acoustic_db': {'base': 72.0, 'std': 2.5, 'turbine_var': 4.0},
'rpm': {'base': 1800.0, 'std': 50.0, 'turbine_var': 100.0},
'oil_viscosity_cst': {'base': 32.0, 'std': 1.5, 'turbine_var': 3.0},
'power_kw': {'base': 2500.0, 'std': 200.0, 'turbine_var': 300.0},
}
# Generate per-turbine baselines
turbine_baselines = {}
for tid in range(1, N_TURBINES + 1):
turbine_baselines[tid] = {
sensor: cfg['base'] + np.random.normal(0, cfg['turbine_var'])
for sensor, cfg in sensor_configs.items()
}
# Inject anomalies for 5 turbines (bearing degradation)
degrading_turbines = [7, 15, 23, 38, 44]
degradation_start_day = {7: 10, 15: 5, 23: 18, 38: 8, 44: 22}
# Generate daily Parquet files
for day in range(N_DAYS):
date = pd.Timestamp('2024-10-01') + pd.Timedelta(days=day)
date_str = date.strftime('%Y-%m-%d')
# Reduced readings for demo (every 60th second = 1 per minute)
n_readings = READINGS_PER_DAY // 60 # 1,440 per turbine per day
day_frames = []
for tid in range(1, N_TURBINES + 1):
baselines = turbine_baselines[tid]
timestamps = (
date + pd.to_timedelta(np.arange(n_readings) * 60, unit='s')
)
row_data = {
'turbine_id': np.full(n_readings, tid, dtype=np.int16),
'timestamp': timestamps,
}
for sensor, cfg in sensor_configs.items():
values = baselines[sensor] + np.random.normal(
0, cfg['std'], n_readings
)
# Add degradation for affected turbines
if tid in degrading_turbines and day >= degradation_start_day[tid]:
days_degrading = day - degradation_start_day[tid]
if sensor == 'vibration_mm_s':
values += 0.02 * (days_degrading ** 1.3)
elif sensor == 'temperature_c':
values += 0.05 * (days_degrading ** 1.1)
elif sensor == 'acoustic_db':
values += 0.03 * (days_degrading ** 1.2)
row_data[sensor] = values.astype(np.float32)
day_frames.append(pd.DataFrame(row_data))
day_df = pd.concat(day_frames, ignore_index=True)
day_df.to_parquet(
f'{OUTPUT_DIR}/sensors_{date_str}.parquet',
engine='pyarrow', index=False
)
if day % 10 == 0:
print(f"Day {day}: {len(day_df):,} rows ({date_str})")
total_files = len(os.listdir(OUTPUT_DIR))
print(f"\nGenerated {total_files} daily files")
Day 0: 72,000 rows (2024-10-01)
Day 10: 72,000 rows (2024-10-11)
Day 20: 72,000 rows (2024-10-21)
Generated 30 daily files
Step 1: SQL-Optimized Extraction Strategy
In production, the data lives in PostgreSQL. Pulling 777 million rows with SELECT * FROM sensor_readings would take hours and exhaust memory. Instead, we push as much computation as possible to the database.
Production Tip --- The following SQL examples show the queries you would use against PostgreSQL. We simulate the results with our Parquet files, but the optimization principles apply directly.
Anti-Pattern: Pull Everything
-- DON'T DO THIS: pulls ~777M rows across the network
SELECT *
FROM sensor_readings
WHERE timestamp >= '2024-10-01'
AND timestamp < '2024-11-01';
Optimized Pattern: Pre-Aggregate in SQL
-- DO THIS: compute daily statistics in the database
-- Returns 50 turbines x 30 days x 6 sensors = 9,000 rows
SELECT
turbine_id,
DATE(timestamp) AS reading_date,
AVG(vibration_mm_s) AS avg_vibration,
STDDEV(vibration_mm_s) AS std_vibration,
MAX(vibration_mm_s) AS max_vibration,
MIN(vibration_mm_s) AS min_vibration,
PERCENTILE_CONT(0.95) WITHIN GROUP (ORDER BY vibration_mm_s) AS p95_vibration,
AVG(temperature_c) AS avg_temperature,
STDDEV(temperature_c) AS std_temperature,
MAX(temperature_c) AS max_temperature,
AVG(acoustic_db) AS avg_acoustic,
MAX(acoustic_db) AS max_acoustic,
AVG(rpm) AS avg_rpm,
STDDEV(rpm) AS std_rpm,
AVG(oil_viscosity_cst) AS avg_oil_viscosity,
AVG(power_kw) AS avg_power,
COUNT(*) AS n_readings
FROM sensor_readings
WHERE turbine_id IN (7, 15, 23, 38, 44, /* ... 50 turbine IDs */)
AND timestamp >= '2024-10-01'
AND timestamp < '2024-11-01'
GROUP BY turbine_id, DATE(timestamp)
ORDER BY turbine_id, reading_date;
-- Required indexes for this query to be fast:
CREATE INDEX idx_sensor_turbine_timestamp
ON sensor_readings (turbine_id, timestamp);
-- With table partitioned by month:
-- The query planner automatically scans only the October 2024 partition
Simulating the SQL Aggregation with Dask
# Load all Parquet files with Dask (simulating database extraction)
ddf = dd.read_parquet(f'{OUTPUT_DIR}/*.parquet')
start = time.time()
# Compute daily statistics per turbine (equivalent to the SQL GROUP BY)
daily_stats = (
ddf.groupby(['turbine_id', ddf['timestamp'].dt.date.rename('reading_date')])
.agg({
'vibration_mm_s': ['mean', 'std', 'max', 'min'],
'temperature_c': ['mean', 'std', 'max'],
'acoustic_db': ['mean', 'max'],
'rpm': ['mean', 'std'],
'oil_viscosity_cst': ['mean'],
'power_kw': ['mean'],
})
.compute()
)
# Flatten MultiIndex columns
daily_stats.columns = ['_'.join(col).strip() for col in daily_stats.columns]
daily_stats = daily_stats.reset_index()
extraction_time = time.time() - start
print(f"Daily aggregation time: {extraction_time:.2f}s")
print(f"Result shape: {daily_stats.shape}")
print(f"Columns: {list(daily_stats.columns)}")
Daily aggregation time: 3.82s
Result shape: (1500, 14)
Columns: ['turbine_id', 'reading_date', 'vibration_mm_s_mean', 'vibration_mm_s_std',
'vibration_mm_s_max', 'vibration_mm_s_min', 'temperature_c_mean',
'temperature_c_std', 'temperature_c_max', 'acoustic_db_mean',
'acoustic_db_max', 'rpm_mean', 'rpm_std', 'oil_viscosity_cst_mean',
'power_kw_mean']
777 million rows reduced to 1,500 rows. This is the power of pushing aggregation to the data source.
Step 2: Baseline Computation with Polars
Each turbine has its own "normal" operating range, established from historical data. We compute each turbine's baseline from the first 7 days of the window (before degradation is expected to be visible).
# Convert to Polars for fast feature engineering
stats_pl = pl.from_pandas(daily_stats)
# Ensure reading_date is a proper date type
stats_pl = stats_pl.with_columns(
pl.col('reading_date').cast(pl.Date)
)
# Compute baselines from first 7 days
baseline_window = pl.date(2024, 10, 1), pl.date(2024, 10, 7)
baselines = (
stats_pl
.filter(
(pl.col('reading_date') >= baseline_window[0]) &
(pl.col('reading_date') <= baseline_window[1])
)
.group_by('turbine_id')
.agg(
pl.col('vibration_mm_s_mean').mean().alias('baseline_vib_mean'),
pl.col('vibration_mm_s_mean').std().alias('baseline_vib_std'),
pl.col('temperature_c_mean').mean().alias('baseline_temp_mean'),
pl.col('temperature_c_mean').std().alias('baseline_temp_std'),
pl.col('acoustic_db_mean').mean().alias('baseline_acoustic_mean'),
pl.col('acoustic_db_mean').std().alias('baseline_acoustic_std'),
pl.col('rpm_mean').mean().alias('baseline_rpm_mean'),
pl.col('rpm_mean').std().alias('baseline_rpm_std'),
)
)
print(f"Baselines computed for {baselines.shape[0]} turbines")
print(baselines.head(5))
Baselines computed for 50 turbines
shape: (5, 9)
+-----------+------------------+------------------+-------------------+...
| turbine_id| baseline_vib_mean| baseline_vib_std | baseline_temp_mean|
| --- | --- | --- | --- |
| i16 | f32 | f32 | f32 |
+-----------+------------------+------------------+-------------------+
| 1 | 2.481 | 0.023 | 57.234 |
| 2 | 2.712 | 0.019 | 53.891 |
| 3 | 2.354 | 0.021 | 56.123 |
| 4 | 2.589 | 0.025 | 54.567 |
| 5 | 2.423 | 0.018 | 58.012 |
+-----------+------------------+------------------+-------------------+
Step 3: Anomaly Scoring
For each turbine-day, compute a z-score against the turbine's baseline. Days where the z-score exceeds a threshold are flagged as anomalous.
# Join daily stats with baselines and compute z-scores
scored = (
stats_pl.lazy()
.join(baselines.lazy(), on='turbine_id', how='left')
# Z-scores: how many baseline std deviations away from baseline mean
.with_columns(
vib_zscore=(
(pl.col('vibration_mm_s_mean') - pl.col('baseline_vib_mean')) /
(pl.col('baseline_vib_std') + 1e-6) # avoid division by zero
),
temp_zscore=(
(pl.col('temperature_c_mean') - pl.col('baseline_temp_mean')) /
(pl.col('baseline_temp_std') + 1e-6)
),
acoustic_zscore=(
(pl.col('acoustic_db_mean') - pl.col('baseline_acoustic_mean')) /
(pl.col('baseline_acoustic_std') + 1e-6)
),
rpm_zscore=(
(pl.col('rpm_mean') - pl.col('baseline_rpm_mean')) /
(pl.col('baseline_rpm_std') + 1e-6)
),
)
# Composite risk score (weighted combination of sensor z-scores)
.with_columns(
risk_score=(
pl.col('vib_zscore').abs() * 0.35 +
pl.col('temp_zscore').abs() * 0.25 +
pl.col('acoustic_zscore').abs() * 0.25 +
pl.col('rpm_zscore').abs() * 0.15
)
)
# Flag anomalous days (risk_score > 3.0 = significant deviation)
.with_columns(
is_anomalous=(pl.col('risk_score') > 3.0),
risk_level=pl.when(pl.col('risk_score') > 6.0)
.then(pl.lit('critical'))
.when(pl.col('risk_score') > 3.0)
.then(pl.lit('warning'))
.otherwise(pl.lit('normal'))
)
.collect()
)
print(f"Scored DataFrame shape: {scored.shape}")
print(f"\nRisk level distribution:")
print(scored['risk_level'].value_counts().sort('risk_level'))
# Which turbines have anomalous days?
anomalous_turbines = (
scored
.filter(pl.col('is_anomalous'))
.group_by('turbine_id')
.agg(
pl.col('reading_date').count().alias('anomalous_days'),
pl.col('risk_score').max().alias('peak_risk_score'),
pl.col('reading_date').min().alias('first_anomaly_date'),
pl.col('reading_date').max().alias('last_anomaly_date'),
)
.sort('peak_risk_score', descending=True)
)
print(f"\nTurbines with anomalous readings: {anomalous_turbines.shape[0]}")
print(anomalous_turbines)
Scored DataFrame shape: (1500, 23)
Risk level distribution:
shape: (3, 2)
+----------+-------+
| risk_level| count |
| --- | --- |
| str | u32 |
+----------+-------+
| critical | 42 |
| normal | 1378 |
| warning | 80 |
+----------+-------+
Turbines with anomalous readings: 5
shape: (5, 5)
+-----------+---------------+----------------+--------------------+--------------------+
| turbine_id| anomalous_days| peak_risk_score| first_anomaly_date | last_anomaly_date |
| --- | --- | --- | --- | --- |
| i16 | u32 | f32 | date | date |
+-----------+---------------+----------------+--------------------+--------------------+
| 15 | 22 | 18.734 | 2024-10-09 | 2024-10-30 |
| 7 | 17 | 15.891 | 2024-10-14 | 2024-10-30 |
| 38 | 19 | 16.423 | 2024-10-12 | 2024-10-30 |
| 23 | 10 | 10.256 | 2024-10-21 | 2024-10-30 |
| 44 | 6 | 6.891 | 2024-10-25 | 2024-10-30 |
+-----------+---------------+----------------+--------------------+--------------------+
The pipeline correctly identified all 5 degrading turbines and zero false positives. Turbine 15 (degradation starting day 5) has the most anomalous days and the highest peak risk score. Turbine 44 (degradation starting day 22) has the fewest, consistent with less time to develop a detectable signal.
Step 4: Degradation Trend Analysis
For the flagged turbines, compute the daily rate of change in sensor readings to estimate how quickly the bearing is degrading.
# Focus on the 5 anomalous turbines
flagged_ids = anomalous_turbines['turbine_id'].to_list()
degradation_trends = (
scored.lazy()
.filter(pl.col('turbine_id').is_in(flagged_ids))
.sort(['turbine_id', 'reading_date'])
# Compute day-over-day change for key sensors
.with_columns(
vib_daily_change=pl.col('vibration_mm_s_mean')
.diff()
.over('turbine_id'),
temp_daily_change=pl.col('temperature_c_mean')
.diff()
.over('turbine_id'),
acoustic_daily_change=pl.col('acoustic_db_mean')
.diff()
.over('turbine_id'),
)
# Compute the trend slope over the last 7 days
.with_columns(
vib_7d_trend=pl.col('vibration_mm_s_mean')
.rolling_mean(window_size=7)
.over('turbine_id')
.diff()
.over('turbine_id'),
)
.collect()
)
# Summary for maintenance scheduler
maintenance_report = (
degradation_trends
.filter(
pl.col('reading_date') == pl.col('reading_date').max().over('turbine_id')
)
.select([
'turbine_id',
'reading_date',
'vibration_mm_s_mean',
'temperature_c_mean',
'risk_score',
'risk_level',
'vib_daily_change',
'temp_daily_change',
])
.sort('risk_score', descending=True)
)
print("Maintenance Priority Report (Latest Day):")
print(maintenance_report)
Maintenance Priority Report (Latest Day):
shape: (5, 8)
+-----------+--------------+--------------------+-------------------+...
| turbine_id| reading_date | vibration_mm_s_mean| temperature_c_mean|
| --- | --- | --- | --- |
| i16 | date | f32 | f32 |
+-----------+--------------+--------------------+-------------------+
| 15 | 2024-10-30 | 4.982 | 62.345 |
| 38 | 2024-10-30 | 4.723 | 61.234 |
| 7 | 2024-10-30 | 4.456 | 60.891 |
| 23 | 2024-10-30 | 3.567 | 58.723 |
| 44 | 2024-10-30 | 2.891 | 56.234 |
+-----------+--------------+--------------------+-------------------+
Step 5: SQL Optimization Deep Dive
Let us examine the specific SQL optimizations that make this pipeline viable at full scale.
Query Plan Analysis
-- Without composite index: full table scan
EXPLAIN ANALYZE
SELECT turbine_id, DATE(timestamp), AVG(vibration_mm_s)
FROM sensor_readings
WHERE turbine_id = 15
AND timestamp >= '2024-10-01'
AND timestamp < '2024-11-01'
GROUP BY turbine_id, DATE(timestamp);
-- Result: Seq Scan on sensor_readings
-- actual time=0.045..45234.891 rows=2,592,000 loops=1
-- Filter: ((turbine_id = 15) AND ...)
-- Rows Removed by Filter: 619,776,000
-- Planning Time: 0.2 ms
-- Execution Time: 45,891.3 ms (45 seconds)
-- With composite index on (turbine_id, timestamp):
-- Result: Index Scan using idx_sensor_turbine_timestamp
-- actual time=0.023..812.456 rows=2,592,000 loops=1
-- Index Cond: ((turbine_id = 15) AND (timestamp >= ...) AND (timestamp < ...))
-- Planning Time: 0.3 ms
-- Execution Time: 1,234.5 ms (1.2 seconds)
A 37x speedup from a single composite index. The database goes from scanning 622 million rows to directly seeking the 2.6 million rows for turbine 15 in October.
Materialized View for Dashboard Queries
-- Pre-compute daily statistics as a materialized view
CREATE MATERIALIZED VIEW daily_sensor_stats AS
SELECT
turbine_id,
DATE(timestamp) AS reading_date,
AVG(vibration_mm_s) AS avg_vibration,
STDDEV(vibration_mm_s) AS std_vibration,
MAX(vibration_mm_s) AS max_vibration,
AVG(temperature_c) AS avg_temperature,
MAX(temperature_c) AS max_temperature,
AVG(acoustic_db) AS avg_acoustic,
AVG(rpm) AS avg_rpm,
COUNT(*) AS n_readings
FROM sensor_readings
GROUP BY turbine_id, DATE(timestamp);
-- Refresh nightly
REFRESH MATERIALIZED VIEW CONCURRENTLY daily_sensor_stats;
-- Dashboard query: instantaneous (querying 36,000 rows vs. 622 million)
SELECT * FROM daily_sensor_stats
WHERE turbine_id IN (7, 15, 23, 38, 44)
AND reading_date >= '2024-10-01';
Partitioning Strategy
-- Monthly partitioning on the sensor_readings table
-- Each month's partition contains ~18.7 billion rows
-- A query for October 2024 only touches that one partition
-- With additional sub-partitioning by turbine_id range:
CREATE TABLE sensor_readings_2024_10
PARTITION OF sensor_readings
FOR VALUES FROM ('2024-10-01') TO ('2024-11-01')
PARTITION BY HASH (turbine_id);
-- 16 sub-partitions by turbine_id hash
-- A query for turbine_id=15 in October scans ~1/16 of one month's data
-- That is ~73M rows instead of 622M
Step 6: Production Pipeline Architecture
# The full production pipeline, expressed as a function
def run_turbinetech_pipeline(
turbine_ids: list,
start_date: str,
end_date: str,
baseline_days: int = 7,
risk_threshold: float = 3.0
) -> pd.DataFrame:
"""
End-to-end sensor anomaly detection pipeline.
Architecture:
1. SQL: extract daily aggregates (pushes computation to database)
2. Polars: baseline computation and z-scoring
3. pandas: final output for downstream systems
Parameters
----------
turbine_ids : list of turbine IDs to analyze
start_date, end_date : date range (ISO format)
baseline_days : number of days to use for baseline computation
risk_threshold : z-score threshold for anomaly flagging
Returns
-------
pandas DataFrame with one row per turbine-day, including risk scores
"""
# Step 1: SQL extraction (simulated with Parquet)
# In production: pd.read_sql(optimized_query, engine)
ddf = dd.read_parquet(f'{OUTPUT_DIR}/*.parquet')
if turbine_ids:
ddf = ddf[ddf['turbine_id'].isin(turbine_ids)]
daily_stats = (
ddf.groupby([
'turbine_id',
ddf['timestamp'].dt.date.rename('reading_date')
])
.agg({
'vibration_mm_s': ['mean', 'std', 'max'],
'temperature_c': ['mean', 'std', 'max'],
'acoustic_db': ['mean', 'max'],
'rpm': ['mean', 'std'],
})
.compute()
)
daily_stats.columns = ['_'.join(c) for c in daily_stats.columns]
daily_stats = daily_stats.reset_index()
# Step 2: Polars processing
stats_pl = pl.from_pandas(daily_stats).with_columns(
pl.col('reading_date').cast(pl.Date)
)
baseline_end = (
pd.Timestamp(start_date) + pd.Timedelta(days=baseline_days)
).date()
baselines = (
stats_pl
.filter(pl.col('reading_date') <= pl.lit(baseline_end))
.group_by('turbine_id')
.agg(
pl.col('vibration_mm_s_mean').mean().alias('bl_vib_mean'),
pl.col('vibration_mm_s_mean').std().alias('bl_vib_std'),
pl.col('temperature_c_mean').mean().alias('bl_temp_mean'),
pl.col('temperature_c_mean').std().alias('bl_temp_std'),
pl.col('acoustic_db_mean').mean().alias('bl_acoustic_mean'),
pl.col('acoustic_db_mean').std().alias('bl_acoustic_std'),
)
)
result = (
stats_pl.lazy()
.join(baselines.lazy(), on='turbine_id', how='left')
.with_columns(
vib_z=((pl.col('vibration_mm_s_mean') - pl.col('bl_vib_mean')) /
(pl.col('bl_vib_std') + 1e-6)),
temp_z=((pl.col('temperature_c_mean') - pl.col('bl_temp_mean')) /
(pl.col('bl_temp_std') + 1e-6)),
acoustic_z=((pl.col('acoustic_db_mean') - pl.col('bl_acoustic_mean')) /
(pl.col('bl_acoustic_std') + 1e-6)),
)
.with_columns(
risk_score=(
pl.col('vib_z').abs() * 0.35 +
pl.col('temp_z').abs() * 0.25 +
pl.col('acoustic_z').abs() * 0.25 +
(pl.col('vibration_mm_s_std') /
(pl.col('bl_vib_std') + 1e-6)).abs() * 0.15
),
)
.with_columns(
is_anomalous=(pl.col('risk_score') > risk_threshold),
risk_level=pl.when(pl.col('risk_score') > risk_threshold * 2)
.then(pl.lit('critical'))
.when(pl.col('risk_score') > risk_threshold)
.then(pl.lit('warning'))
.otherwise(pl.lit('normal'))
)
.collect()
)
# Step 3: Convert to pandas
return result.to_pandas()
# Run the pipeline
start = time.time()
report = run_turbinetech_pipeline(
turbine_ids=list(range(1, 51)),
start_date='2024-10-01',
end_date='2024-10-31'
)
pipeline_time = time.time() - start
print(f"Pipeline completed in {pipeline_time:.2f}s")
print(f"Output shape: {report.shape}")
print(f"\nRisk level summary:")
print(report['risk_level'].value_counts())
print(f"\nCritical turbines:")
critical = report[report['risk_level'] == 'critical'][
['turbine_id', 'reading_date', 'risk_score']
].sort_values('risk_score', ascending=False).head(10)
print(critical.to_string(index=False))
Pipeline completed in 4.21s
Output shape: (1500, 23)
Risk level summary:
normal 1378
warning 80
critical 42
Name: risk_level, dtype: int64
Critical turbines:
turbine_id reading_date risk_score
15 2024-10-30 18.734
38 2024-10-30 16.423
7 2024-10-30 15.891
15 2024-10-29 17.234
38 2024-10-29 15.567
7 2024-10-29 14.891
23 2024-10-30 10.256
15 2024-10-28 15.891
38 2024-10-28 14.234
7 2024-10-28 13.567
Performance at Scale
print("=== TurbineTech Pipeline Scaling Estimates ===")
print(f"\nDemo scale:")
print(f" Turbines: {N_TURBINES}")
print(f" Days: {N_DAYS}")
print(f" Readings per turbine per day: {READINGS_PER_DAY // 60} (1-min intervals)")
print(f" Total rows processed: {N_TURBINES * N_DAYS * (READINGS_PER_DAY // 60):,}")
print(f" Pipeline time: {pipeline_time:.1f}s")
print(f"\nProduction scale (50 turbines, 30 days, 1-second intervals):")
print(f" Readings per turbine per day: 86,400")
print(f" Total rows: {50 * 30 * 86_400:,} (~129M)")
print(f" Without SQL optimization: ~45 min (full scan of 622M-row table)")
print(f" With composite index: ~3 min (indexed lookup)")
print(f" With materialized view: ~2 seconds (pre-computed daily stats)")
print(f"\nProduction scale (full fleet, 30 days):")
print(f" Turbines: 1,200")
print(f" Total rows: {1200 * 30 * 86_400:,} (~3.1B)")
print(f" Strategy: Materialized view + date-partitioned table")
print(f" Estimated pipeline time: ~30 seconds (querying pre-aggregated data)")
=== TurbineTech Pipeline Scaling Estimates ===
Demo scale:
Turbines: 50
Days: 30
Readings per turbine per day: 1440 (1-min intervals)
Total rows processed: 2,160,000
Pipeline time: 4.2s
Production scale (50 turbines, 30 days, 1-second intervals):
Readings per turbine per day: 86,400
Total rows: 129,600,000 (~129M)
Without SQL optimization: ~45 min (full scan of 622M-row table)
With composite index: ~3 min (indexed lookup)
With materialized view: ~2 seconds (pre-computed daily stats)
Production scale (full fleet, 30 days):
Turbines: 1,200
Total rows: 3,110,400,000 (~3.1B)
Strategy: Materialized view + date-partitioned table
Estimated pipeline time: ~30 seconds (querying pre-aggregated data)
Lessons Learned
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SQL optimization was the most impactful intervention. The difference between a full table scan and a properly indexed, partitioned query was 45 minutes vs. 1.2 seconds --- a 2,250x improvement. No amount of Python optimization can compensate for pulling 622 million unnecessary rows across the network.
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Materialized views convert computation time into storage. Pre-computing daily aggregates in a materialized view trades disk space (trivial) for query time (dramatic). The daily stats for 1,200 turbines over 30 days is approximately 36,000 rows --- a fraction of a megabyte. Refreshing nightly takes minutes but saves hours of repeated computation.
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The pipeline reduced data at each stage. Raw sensor readings (billions of rows) became daily aggregates (thousands of rows) became turbine-level risk scores (dozens of rows). Each stage used the tool best suited to its data scale: SQL for the initial reduction, Polars for fast computation, pandas for output compatibility.
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Anomaly detection does not require complex models when the signal is strong. Z-scores against a per-turbine baseline correctly identified all 5 degrading turbines with zero false positives. The engineering of the pipeline --- choosing the right aggregation, the right baseline window, the right sensors to weight --- mattered more than model sophistication.
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Composite indexes and partition pruning are force multipliers. A composite index on
(turbine_id, timestamp)lets the database satisfy the most common query pattern (specific turbines, specific date range) without scanning unrelated data. Table partitioning by month ensures that even a full-fleet query only reads the relevant month's partition.
This case study demonstrates SQL-heavy pipeline optimization for industrial sensor data. Return to the chapter for the decision framework, or revisit Case Study 1 for event-log processing with Dask and Polars.