Create a comprehensive system design that leverages Greenplum as the central data platform for semiconductor wafer processing, integrating all the manufacturing execution and control systems you mentioned.
Data Architecture and Schema Design
Technical Specifications - Semiconductor Manufacturing Data Platform
System Performance Requirements
Data Volume Specifications
| Data Source | Volume/Day | Peak Rate | Retention | Storage Tier |
|---|---|---|---|---|
| Equipment Sensors (FDC) | 50 TB | 100K msgs/sec | 90 days hot, 2 years warm | NVMe SSD |
| Process Parameters (APC/SPC) | 5 TB | 10K msgs/sec | 1 year hot, 5 years warm | SSD/HDD Hybrid |
| Production Events (MES) | 1 TB | 1K msgs/sec | 3 years hot, 10 years cold | SSD/HDD |
| Test Results (CP/WAT) | 10 TB | 5K msgs/sec | 2 years hot, 10 years warm | SSD |
| Defect Data | 20 TB | 2K msgs/sec | 1 year hot, 5 years warm | SSD |
| Total Daily Volume | 86 TB | 118K msgs/sec |
Performance Targets
| Metric | Target | Measurement |
|---|---|---|
| Query Response Time | <5 seconds | 95th percentile for dashboard queries |
| Data Ingestion Latency | <30 seconds | End-to-end from equipment to Greenplum |
| System Availability | 99.9% | 8.76 hours downtime/year maximum |
| Concurrent Users | 500+ | Simultaneous dashboard users |
| Analytics Processing | <1 hour | Complex yield analysis queries |
| Backup/Recovery RTO | <4 hours | Recovery Time Objective |
| Backup/Recovery RPO | <15 minutes | Recovery Point Objective |
Greenplum Cluster Configuration
Hardware Specifications
Master Nodes (2x - Active/Standby)
Hardware:
Model: Dell PowerEdge R760
CPU: 2x Intel Xeon Platinum 8358 (32 cores, 2.6GHz)
Memory: 512GB DDR4-3200
Storage: 4x 1.92TB NVMe SSD (RAID 10)
Network: 2x 25GbE + 2x 100GbE
Configuration:
OS: Red Hat Enterprise Linux 8.6
Greenplum: Version 7.0
PostgreSQL: Version 14.x base
Memory Settings:
shared_buffers: 128GB
work_mem: 1GB
maintenance_work_mem: 8GB
max_connections: 1000
Segment Nodes - Hot Tier (8x nodes)
Hardware:
Model: Dell PowerEdge R750
CPU: 2x Intel Xeon Gold 6338 (32 cores, 2.0GHz)
Memory: 1TB DDR4-3200
Storage: 8x 3.84TB NVMe SSD (RAID 10)
Network: 2x 25GbE + 2x 100GbE
Configuration:
Segments per Node: 4 primary + 4 mirror
Total Segments: 32 primary + 32 mirror
Memory per Segment: 120GB
Storage per Segment: 12TB usable
Segment Nodes - Warm Tier (8x nodes)
Hardware:
Model: Dell PowerEdge R750
CPU: 2x Intel Xeon Silver 4314 (16 cores, 2.4GHz)
Memory: 512GB DDR4-3200
Storage: 4x 7.68TB SSD + 8x 10TB HDD (RAID 6)
Network: 2x 25GbE
Configuration:
Segments per Node: 2 primary + 2 mirror
Total Segments: 16 primary + 16 mirror
Memory per Segment: 240GB
Storage per Segment: 60TB usable
Segment Nodes - Cold Tier (8x nodes)
Hardware:
Model: Dell PowerEdge R740
CPU: 2x Intel Xeon Silver 4210R (10 cores, 2.4GHz)
Memory: 256GB DDR4-2933
Storage: 12x 18TB HDD (RAID 6)
Network: 2x 10GbE
Configuration:
Segments per Node: 1 primary + 1 mirror
Total Segments: 8 primary + 8 mirror
Memory per Segment: 240GB
Storage per Segment: 150TB usable
Greenplum Optimization Parameters
Master Node Configuration
# postgresql.conf - Master Node
max_connections = 1000
superuser_reserved_connections = 10
shared_buffers = 128GB
work_mem = 1GB
maintenance_work_mem = 8GB
dynamic_shared_memory_type = posix
# Optimizer settings
enable_nestloop = off
enable_mergejoin = on
enable_hashjoin = on
enable_seqscan = on
enable_indexscan = on
enable_bitmapscan = on
# Greenplum specific
gp_autostats_mode = on_change
gp_autostats_on_change_threshold = 2147483647
gp_enable_global_deadlock_detector = on
gp_log_gang = terse
gp_max_packet_size = 65536
# Connection and authentication
listen_addresses = '*'
port = 5432
max_prepared_transactions = 500
Segment Node Configuration
# postgresql.conf - Segment Nodes
max_connections = 750
shared_buffers = 120GB
work_mem = 512MB
maintenance_work_mem = 4GB
temp_buffers = 32MB
max_prepared_transactions = 500
# I/O settings
checkpoint_segments = 128
checkpoint_completion_target = 0.9
wal_buffers = 64MB
wal_writer_delay = 10ms
# Query processing
effective_cache_size = 600GB
random_page_cost = 1.0
seq_page_cost = 1.0
cpu_tuple_cost = 0.01
cpu_index_tuple_cost = 0.005
cpu_operator_cost = 0.0025
# Greenplum segment specific
gp_role = segment
gp_contentid = -1 # Set per segment
gp_dbid = 2 # Set per segment
Data Streaming Architecture
Apache Kafka Configuration
Broker Configuration
# server.properties
broker.id=1
listeners=PLAINTEXT://0.0.0.0:9092,SSL://0.0.0.0:9093
advertised.listeners=PLAINTEXT://kafka1:9092,SSL://kafka1:9093
# Network and I/O
num.network.threads=16
num.io.threads=32
socket.send.buffer.bytes=102400
socket.receive.buffer.bytes=102400
socket.request.max.bytes=104857600
num.replica.fetchers=4
# Log settings
num.partitions=12
default.replication.factor=3
min.insync.replicas=2
log.retention.hours=168
log.retention.bytes=1073741824000
log.segment.bytes=1073741824
log.cleanup.policy=delete
# Compression and performance
compression.type=lz4
message.max.bytes=10485760
replica.fetch.max.bytes=10485760
group.initial.rebalance.delay.ms=3000
# JVM settings
-Xmx32g -Xms32g
-XX:+UseG1GC
-XX:MaxGCPauseMillis=20
-XX:InitiatingHeapOccupancyPercent=35
Topic Specifications
# Equipment sensor data - high volume, short retention
kafka-topics.sh --create --topic equipment-sensors \
--partitions 24 --replication-factor 3 \
--config compression.type=lz4 \
--config retention.ms=259200000 \
--config segment.ms=3600000 \
--config cleanup.policy=delete
# Production events - medium volume, longer retention
kafka-topics.sh --create --topic production-events \
--partitions 12 --replication-factor 3 \
--config compression.type=snappy \
--config retention.ms=2592000000 \
--config cleanup.policy=delete
# Test results - large messages, medium volume
kafka-topics.sh --create --topic test-results \
--partitions 8 --replication-factor 3 \
--config compression.type=gzip \
--config max.message.bytes=10485760 \
--config retention.ms=604800000
# Alert events - low volume, immediate processing
kafka-topics.sh --create --topic manufacturing-alerts \
--partitions 4 --replication-factor 3 \
--config compression.type=snappy \
--config retention.ms=86400000 \
--config cleanup.policy=delete
Apache Storm Configuration
Storm Cluster Specifications
# storm.yaml
storm.zookeeper.servers:
- "zk1.manufacturing.local"
- "zk2.manufacturing.local"
- "zk3.manufacturing.local"
storm.zookeeper.port: 2181
# Nimbus configuration
nimbus.seeds: ["storm-nimbus1", "storm-nimbus2"]
nimbus.host: "storm-nimbus1"
storm.local.dir: "/opt/storm/data"
# Worker configuration
supervisor.slots.ports:
- 6700
- 6701
- 6702
- 6703
worker.childopts: "-Xmx4g -XX:+UseConcMarkSweepGC"
supervisor.childopts: "-Xmx2g"
# Performance tuning
topology.workers: 8
topology.ackers: 4
topology.max.spout.pending: 10000
topology.message.timeout.secs: 300
Real-time Processing Topology
public class ManufacturingStreamTopology {
public static class SensorDataBolt extends BaseBasicBolt {
private OutputCollector collector;
private Map<String, MovingAverage> equipmentAverages;
@Override
public void prepare(Map stormConf, TopologyContext context,
OutputCollector collector) {
this.collector = collector;
this.equipmentAverages = new HashMap<>();
}
@Override
public void execute(Tuple input, BasicOutputCollector collector) {
try {
String equipmentId = input.getStringByField("equipment_id");
Double sensorValue = input.getDoubleByField("sensor_value");
String parameterName = input.getStringByField("parameter");
// Calculate moving average
String key = equipmentId + "_" + parameterName;
MovingAverage avg = equipmentAverages.computeIfAbsent(
key, k -> new MovingAverage(100)); // 100-point moving average
avg.addValue(sensorValue);
// Check for anomalies (3-sigma rule)
if (Math.abs(sensorValue - avg.getAverage()) > 3 * avg.getStdDev()) {
Values anomalyTuple = new Values(equipmentId, parameterName,
sensorValue, avg.getAverage(),
System.currentTimeMillis());
collector.emit("anomaly-stream", anomalyTuple);
}
// Emit aggregated data every minute
if (System.currentTimeMillis() % 60000 < 1000) {
Values aggTuple = new Values(equipmentId, parameterName,
avg.getAverage(), avg.getStdDev(),
System.currentTimeMillis());
collector.emit("aggregated-stream", aggTuple);
}
} catch (Exception e) {
LOG.error("Error processing sensor data", e);
collector.reportError(e);
}
}
@Override
public void declareOutputFields(OutputFieldsDeclarer declarer) {
declarer.declareStream("anomaly-stream",
new Fields("equipment_id", "parameter", "value",
"expected", "timestamp"));
declarer.declareStream("aggregated-stream",
new Fields("equipment_id", "parameter", "avg_value",
"std_dev", "timestamp"));
}
}
}
Data Integration Specifications
Apache NiFi Configuration
System Requirements
Hardware:
CPU: 16 cores minimum
Memory: 64GB minimum
Storage: 10TB for content repository
Network: 10GbE minimum
NiFi Configuration:
Java Heap: 32GB
FlowFile Repository: SSD storage
Content Repository: High-throughput storage
Provenance Repository: Separate disk array
# nifi.properties key settings
nifi.flowfile.repository.implementation=org.apache.nifi.controller.repository.WriteAheadFlowFileRepository
nifi.flowfile.repository.wal.implementation=org.apache.nifi.wali.SequentialAccessWriteAheadLog
nifi.flowfile.repository.directory=./flowfile_repository
nifi.flowfile.repository.checkpoint.interval=20 secs
nifi.content.repository.implementation=org.apache.nifi.controller.repository.FileSystemRepository
nifi.content.repository.directory.default=./content_repository
nifi.content.claim.max.appendable.size=50 MB
nifi.content.repository.archive.max.retention.period=7 days
nifi.content.repository.archive.max.usage.percentage=80%
nifi.provenance.repository.implementation=org.apache.nifi.provenance.WriteAheadProvenanceRepository
nifi.provenance.repository.directory.default=./provenance_repository
nifi.provenance.repository.max.storage.time=30 days
nifi.provenance.repository.max.storage.size=10 GB
Data Flow Templates
MES System Integration Flow
<template>
<name>MES_Integration_Flow</name>
<description>Extract and transform MES production data</description>
<!-- Extract from MES database -->
<processor>
<id>extract-mes-data</id>
<class>org.apache.nifi.processors.standard.ExecuteSQL</class>
<properties>
<property>
<name>Database Connection Pooling Service</name>
<value>mes-db-connection-pool</value>
</property>
<property>
<name>SQL select query</name>
<value>
SELECT
lot_id, product_id, step_name, equipment_id,
start_time, end_time, status, operator_id,
recipe_parameters, quality_flags
FROM production_tracking
WHERE last_modified >= CURRENT_TIMESTAMP - INTERVAL '5 MINUTES'
</value>
</property>
</properties>
<scheduling>
<period>5 min</period>
<concurrent-tasks>1</concurrent-tasks>
</scheduling>
</processor>
<!-- Transform data format -->
<processor>
<id>transform-mes-data</id>
<class>org.apache.nifi.processors.script.ExecuteScript</class>
<properties>
<property>
<name>Script Engine</name>
<value>python</value>
</property>
<property>
<name>Script Body</name>
<value><![CDATA[
import json
import uuid
from datetime import datetime
flowFile = session.get()
if flowFile is not None:
try:
# Read input data
content = flowFile.getAttributes()['sql.result.records']
records = json.loads(content)
# Transform to standard format
transformed_records = []
for record in records:
transformed = {
'event_id': str(uuid.uuid4()),
'event_type': 'lot_tracking',
'timestamp': datetime.now().isoformat(),
'lot_id': record['lot_id'],
'product_id': record['product_id'],
'process_step': record['step_name'],
'equipment_id': record['equipment_id'],
'start_time': record['start_time'],
'end_time': record['end_time'],
'status': record['status'],
'metadata': {
'operator_id': record['operator_id'],
'recipe_params': json.loads(record['recipe_parameters']),
'quality_flags': record['quality_flags']
}
}
transformed_records.append(transformed)
# Write transformed data
output_content = json.dumps(transformed_records, indent=2)
flowFile = session.write(flowFile, output_content)
session.transfer(flowFile, REL_SUCCESS)
except Exception as e:
session.transfer(flowFile, REL_FAILURE)
session.log.error('Transform error: {}'.format(str(e)))
]]></value>
</property>
</properties>
</processor>
<!-- Load to Greenplum -->
<processor>
<id>load-to-greenplum</id>
<class>org.apache.nifi.processors.standard.PutDatabaseRecord</class>
<properties>
<property>
<name>Record Reader</name>
<value>json-tree-reader</value>
</property>
<property>
<name>Database Connection Pooling Service</name>
<value>greenplum-connection-pool</value>
</property>
<property>
<name>Statement Type</name>
<value>INSERT</value>
</property>
<property>
<name>Table Name</name>
<value>production.lot_events</value>
</property>
<property>
<name>Batch Size</name>
<value>1000</value>
</property>
</properties>
</processor>
</template>
Machine Learning Platform Specifications
Apache Spark Configuration
Cluster Specifications
# Spark Master Nodes (2x for HA)
Master_Nodes:
Hardware: Dell PowerEdge R750
CPU: 2x Intel Xeon Gold 6338 (32 cores)
Memory: 256GB DDR4-3200
Storage: 4x 1.92TB NVMe SSD
Network: 2x 25GbE
# Spark Worker Nodes (6x)
Worker_Nodes:
Hardware: Dell PowerEdge R750
CPU: 2x Intel Xeon Gold 6338 (32 cores)
Memory: 1TB DDR4-3200
Storage: 8x 3.84TB NVMe SSD + 4x Tesla V100 GPU
Network: 2x 25GbE + InfiniBand
# spark-defaults.conf
spark.master=spark://spark-master1:7077,spark-master2:7077
spark.executor.cores=8
spark.executor.memory=120g
spark.executor.memoryFraction=0.8
spark.sql.adaptive.enabled=true
spark.sql.adaptive.coalescePartitions.enabled=true
spark.serializer=org.apache.spark.serializer.KryoSerializer
spark.sql.warehouse.dir=hdfs://namenode:9000/spark-warehouse
ML Pipeline Implementation
from pyspark.sql import SparkSession
from pyspark.ml.feature import VectorAssembler, StandardScaler
from pyspark.ml.classification import RandomForestClassifier
from pyspark.ml.regression import LinearRegression
from pyspark.ml.pipeline import Pipeline, PipelineModel
from pyspark.ml.evaluation import BinaryClassificationEvaluator
from pyspark.ml.tuning import CrossValidator, ParamGridBuilder
class YieldPredictionPipeline:
def __init__(self, spark_session):
self.spark = spark_session
self.model_path = "hdfs://namenode:9000/models/yield_prediction"
def create_feature_pipeline(self):
"""Create feature engineering pipeline"""
# Process parameter features
process_features = [
'temperature_avg', 'temperature_std', 'temperature_range',
'pressure_avg', 'pressure_std', 'pressure_range',
'flow_rate_avg', 'flow_rate_std', 'flow_rate_range',
'power_avg', 'power_std', 'power_range'
]
# Equipment health features
equipment_features = [
'utilization_rate', 'cycle_count', 'maintenance_hours',
'failure_count_30d', 'mtbf', 'mttr'
]
# Historical yield features
historical_features = [
'prev_lot_yield', 'product_avg_yield_7d', 'product_avg_yield_30d',
'equipment_avg_yield_7d', 'step_avg_yield_7d'
]
# Quality features
quality_features = [
'defect_density', 'critical_dimension_variation',
'overlay_error', 'thickness_uniformity'
]
all_features = (process_features + equipment_features +
historical_features + quality_features)
# Feature assembly
assembler = VectorAssembler(
inputCols=all_features,
outputCol="raw_features",
handleInvalid="skip"
)
# Feature scaling
scaler = StandardScaler(
inputCol="raw_features",
outputCol="scaled_features",
withStd=True,
withMean=True
)
return Pipeline(stages=[assembler, scaler])
def create_prediction_model(self):
"""Create yield prediction model"""
# Random Forest for yield classification (Good/Bad)
rf_classifier = RandomForestClassifier(
featuresCol="scaled_features",
labelCol="yield_class",
predictionCol="yield_prediction",
probabilityCol="yield_probability",
numTrees=200,
maxDepth=15,
minInstancesPerNode=10,
seed=42
)
# Linear Regression for continuous yield prediction
lr_regressor = LinearRegression(
featuresCol="scaled_features",
labelCol="yield_percentage",
predictionCol="predicted_yield",
maxIter=100,
regParam=0.1,
elasticNetParam=0.5
)
return Pipeline(stages=[rf_classifier, lr_regressor])
def train_model(self, training_data):
"""Train the complete ML pipeline"""
# Create complete pipeline
feature_pipeline = self.create_feature_pipeline()
prediction_pipeline = self.create_prediction_model()
complete_pipeline = Pipeline(
stages=feature_pipeline.getStages() + prediction_pipeline.getStages()
)
# Hyperparameter tuning
param_grid = ParamGridBuilder() \
.addGrid(complete_pipeline.getStages()[-2].numTrees, [100, 200, 300]) \
.addGrid(complete_pipeline.getStages()[-2].maxDepth, [10, 15, 20]) \
.addGrid(complete_pipeline.getStages()[-1].regParam, [0.01, 0.1, 1.0]) \
.build()
# Cross validation
evaluator = BinaryClassificationEvaluator(
labelCol="yield_class",
rawPredictionCol="rawPrediction",
metricName="areaUnderROC"
)
cv = CrossValidator(
estimator=complete_pipeline,
estimatorParamMaps=param_grid,
evaluator=evaluator,
numFolds=5,
seed=42
)
# Train model
cv_model = cv.fit(training_data)
best_model = cv_model.bestModel
# Save model
best_model.write().overwrite().save(self.model_path)
return best_model
def batch_prediction(self, input_data):
"""Perform batch predictions on new data"""
# Load trained model
model = PipelineModel.load(self.model_path)
# Make predictions
predictions = model.transform(input_data)
# Select relevant columns
result = predictions.select(
"lot_id", "wafer_id", "product_id",
"yield_prediction", "yield_probability", "predicted_yield",
"scaled_features"
)
return result
Security Specifications
Network Security Architecture
# Network Segmentation
vlans:
manufacturing_floor:
id: 100
subnet: "10.100.0.0/16"
security_zone: "manufacturing"
firewall_rules:
- allow: "tcp/502" # Modbus
- allow: "tcp/44818" # OPC UA
- deny: "any/any" # Default deny
data_platform:
id: 200
subnet: "10.200.0.0/16"
security_zone: "data"
firewall_rules:
- allow: "tcp/5432" # Greenplum
- allow: "tcp/9092" # Kafka
- allow: "tcp/8080" # NiFi
- deny: "any/any"
analytics:
id: 300
subnet: "10.300.0.0/16"
security_zone: "analytics"
firewall_rules:
- allow: "tcp/8888" # Jupyter
- allow: "tcp/7077" # Spark
- deny: "any/any"
management:
id: 400
subnet: "10.400.0.0/16"
security_zone: "management"
firewall_rules:
- allow: "tcp/22" # SSH
- allow: "tcp/443" # HTTPS
- deny: "any/any"
Authentication & Authorization
# LDAP Integration
ldap_config:
server: "ldaps://ad.manufacturing.local:636"
base_dn: "dc=manufacturing,dc=local"
bind_dn: "cn=svc_greenplum,ou=service_accounts,dc=manufacturing,dc=local"
# Role-Based Access Control
rbac_roles:
data_engineer:
permissions:
- greenplum: ["CONNECT", "CREATE", "SELECT", "INSERT", "UPDATE", "DELETE"]
- kafka: ["READ", "WRITE", "CREATE_TOPICS"]
- nifi: ["READ", "WRITE", "MODIFY_FLOW"]
analyst:
permissions:
- greenplum: ["CONNECT", "SELECT"]
- tableau: ["READ", "CREATE_WORKBOOKS"]
- jupyter: ["READ", "EXECUTE"]
operator:
permissions:
- dashboards: ["READ"]
- alerts: ["READ", "ACKNOWLEDGE"]
administrator:
permissions:
- system: ["ALL"]
- security: ["MANAGE_USERS", "MANAGE_ROLES"]
# SSL/TLS Configuration
ssl_config:
greenplum:
ssl_mode: "require"
ssl_cert: "/etc/ssl/certs/greenplum.crt"
ssl_key: "/etc/ssl/private/greenplum.key"
ssl_ca: "/etc/ssl/certs/manufacturing-ca.crt"
kafka:
ssl_enabled: true
ssl_keystore: "/etc/kafka/ssl/kafka.server.keystore.jks"
ssl_truststore: "/etc/kafka/ssl/kafka.server.truststore.jks"
ssl_protocols: "TLSv1.2,TLSv1.3"
Monitoring & Alerting Specifications
System Monitoring Stack
# Prometheus Configuration
prometheus:
retention: "30d"
scrape_interval: "15s"
evaluation_interval: "15s"
storage: "1TB SSD"
targets:
greenplum:
- greenplum-master:9187
- greenplum-segments:9187
kafka:
- kafka-brokers:9308
storm:
- storm-nimbus:8080
- storm-supervisors:8080
system:
- all-nodes:9100
# Grafana Dashboards
grafana:
dashboards:
manufacturing_overview:
metrics:
- equipment_utilization
- production_throughput
- yield_trends
- alert_summary
data_platform_health:
metrics:
- greenplum_query_performance
- kafka_throughput
- storm_processing_latency
- storage_utilization
system_infrastructure:
metrics:
- cpu_utilization
- memory_usage
- disk_io
- network_traffic
# Alert Rules
alerting_rules:
critical:
- name: "greenplum_master_down"
condition: "up{job='greenplum-master'} == 0"
for: "30s"
- name: "high_equipment_downtime"
condition: "equipment_downtime_hours > 4"
for: "5m"
- name: "yield_drop_significant"
condition: "rate(yield_percentage[1h]) < 0.8 * rate(yield_percentage[24h:1h])"
for: "15m"
warning:
- name: "kafka_lag_high"
condition: "kafka_consumer_lag > 100000"
for: "10m"
- name: "query_performance_degraded"
condition: "greenplum_query_duration_p95 > 10"
for: "15m"
Performance Monitoring KPIs
| Component | KPI | Target | Alert Threshold |
|---|---|---|---|
| Greenplum Master | Query Response Time | <3s avg | >10s for 5min |
| Greenplum Segments | CPU Utilization | <80% avg | >90% for 10min |
| Kafka Brokers | Message Throughput | 100K msg/sec | <50K msg/sec |
| Storm Topology | Processing Latency | <5s avg | >30s for 5min |
| Equipment Integration | Data Freshness | <60s | >300s |
| Yield Analysis | Model Accuracy | >95% | <90% |
Disaster Recovery Specifications
Backup Strategy
backup_strategy:
greenplum:
full_backup:
frequency: "weekly"
retention: "3 months"
storage: "tape_library"
incremental_backup:
frequency: "daily"
retention: "30 days"
storage: "disk_array"
continuous_wal_shipping:
frequency: "real-time"
retention: "7 days"
target: "dr_site"
kafka:
topic_replication:
target: "dr_kafka_cluster"
replication_factor: 3
application_data:
configuration_backup:
frequency: "daily"
storage: "git_repository"
model_backup:
frequency: "after_training"
storage: "hdfs_dr_cluster"
# Recovery Procedures
recovery_procedures:
rto_targets:
tier1_systems: "1 hour" # Critical production systems
tier2_systems: "4 hours" # Analytics and reporting
tier3_systems: "24 hours" # Development and testing
rpo_targets:
production_data: "15 minutes"
analytical_data: "4 hours"
historical_data: "24 hours"
This comprehensive technical specification provides the detailed configuration requirements, performance targets, and implementation guidelines necessary for successfully deploying and operating the semiconductor manufacturing data platform with Greenplum at its core. The specifications ensure enterprise-grade reliability, security, and performance while enabling advanced analytics and real-time decision making across all manufacturing processes.
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