Introduction
Macroeconomic data aggregation represents one of the most complex challenges in economic data processing due to the heterogeneous nature of economic indicators, their varying release schedules, and the need to combine data from multiple authoritative sources. Unlike operational business data that typically flows in consistent formats and frequencies, economic indicators arrive according to irregular schedules determined by statistical agencies, central banks, and international organizations.
The primary purpose of macroeconomic aggregation pipelines is to answer comparative and trend-based questions that require combining multiple indicators across time periods and geographic regions. These systems excel at addressing questions like “Is global debt growing faster than the previous decade?” or “How does European lending growth compare to historical patterns?” Rather than providing real-time market predictions, these pipelines focus on comprehensive analytical capabilities that support strategic decision-making and policy analysis.
The challenge lies not just in collecting data from diverse sources, but in harmonizing different methodologies, handling revision patterns, and maintaining data quality across the entire aggregation process. Economic indicators often undergo methodological changes, seasonal adjustments, and regular revisions that must be carefully managed to preserve analytical integrity.
This guide builds upon the foundational data collection techniques covered in API Integration for Economic Data Sources and incorporates the quality practices detailed in Data Quality Practices for Economic Datasets. The pipeline architectures presented here provide the foundation for the advanced analytics discussed in Machine Learning Applications Economic Data Analysis and the real-time capabilities covered in Real-Time Data Processing Economic Indicators.
Pipeline Architecture and Design Patterns
Modern macroeconomic aggregation pipelines typically follow a layered architecture that separates data collection, transformation, aggregation, and delivery concerns. This separation enables independent scaling of different pipeline components and facilitates maintenance as data sources evolve or new indicators are added to the system.
The ingestion layer handles the complexity of connecting to diverse data sources, each with their own authentication requirements, rate limits, and data formats. This layer must be resilient to source outages and capable of handling the irregular release schedules that characterize economic data. For example, employment statistics might be released monthly on the first Friday of each month, while GDP data follows a quarterly schedule with preliminary, revised, and final estimates.
The transformation layer addresses the complex task of harmonizing data from different methodological approaches. Central bank interest rates might be reported as policy rates, overnight rates, or longer-term benchmark rates, requiring sophisticated mapping and conversion logic. Similarly, inflation measures might use different base years, geographic coverage, or calculation methodologies that must be reconciled for meaningful aggregation.
The aggregation layer implements the analytical logic that combines individual indicators into composite measures or cross-indicator analyses. This layer must handle temporal alignment of indicators with different frequencies, geographic aggregation across countries or regions, and statistical operations that preserve the meaningful relationships between economic variables.
import pandas as pd
import numpy as np
from datetime import datetime, timedelta
from typing import Dict, List, Any, Optional
import logging
from dataclasses import dataclass
from abc import ABC, abstractmethod
@dataclass
class DataSource:
"""Configuration for economic data sources"""
name: str
base_url: str
auth_config: Dict[str, Any]
update_frequency: str # 'daily', 'weekly', 'monthly', 'quarterly'
lag_days: int # Expected lag between event and data availability
indicators: List[str]
@dataclass
class IndicatorMetadata:
"""Metadata for economic indicators"""
code: str
name: str
description: str
unit: str
frequency: str
source: str
country: str
seasonal_adjustment: bool
methodology_notes: str
class EconomicDataPipeline:
"""Comprehensive pipeline for aggregating macroeconomic indicators"""
def __init__(self, config: Dict[str, Any]):
self.config = config
self.data_sources = self._initialize_data_sources()
self.collectors = {}
self.transformers = {}
self.aggregators = {}
self.quality_checker = QualityAssurance()
self.metadata_manager = MetadataManager()
def _initialize_data_sources(self) -> Dict[str, DataSource]:
"""Initialize configured data sources"""
sources = {}
# FRED configuration
if 'fred' in self.config['sources']:
sources['fred'] = DataSource(
name='FRED',
base_url='https://api.stlouisfed.org/fred',
auth_config=self.config['sources']['fred']['auth'],
update_frequency='daily',
lag_days=1,
indicators=self.config['sources']['fred']['indicators']
)
# World Bank configuration
if 'worldbank' in self.config['sources']:
sources['worldbank'] = DataSource(
name='World Bank',
base_url='https://api.worldbank.org/v2',
auth_config={}, # World Bank API doesn't require authentication
update_frequency='annually',
lag_days=365, # Annual data typically has significant lag
indicators=self.config['sources']['worldbank']['indicators']
)
# IMF configuration
if 'imf' in self.config['sources']:
sources['imf'] = DataSource(
name='IMF',
base_url='http://dataservices.imf.org/REST/SDMX_JSON.svc',
auth_config={},
update_frequency='quarterly',
lag_days=90,
indicators=self.config['sources']['imf']['indicators']
)
return sources
def execute_pipeline(self, start_date: str, end_date: str) -> Dict[str, Any]:
"""Execute complete aggregation pipeline"""
pipeline_results = {
'execution_start': datetime.utcnow(),
'data_collected': {},
'transformations_applied': {},
'aggregations_created': {},
'quality_results': {},
'execution_end': None,
'status': 'running'
}
try:
# Phase 1: Data Collection
logging.info("Starting data collection phase")
collected_data = self._collect_all_data(start_date, end_date)
pipeline_results['data_collected'] = collected_data
# Phase 2: Data Transformation
logging.info("Starting data transformation phase")
transformed_data = self._transform_all_data(collected_data)
pipeline_results['transformations_applied'] = transformed_data
# Phase 3: Quality Assurance
logging.info("Starting quality assurance phase")
quality_results = self._perform_quality_checks(transformed_data)
pipeline_results['quality_results'] = quality_results
# Phase 4: Data Aggregation
logging.info("Starting data aggregation phase")
aggregated_data = self._aggregate_indicators(transformed_data)
pipeline_results['aggregations_created'] = aggregated_data
# Phase 5: Output Generation
logging.info("Starting output generation phase")
final_outputs = self._generate_outputs(aggregated_data)
pipeline_results['final_outputs'] = final_outputs
pipeline_results['status'] = 'completed'
except Exception as e:
logging.error(f"Pipeline execution failed: {e}")
pipeline_results['status'] = 'failed'
pipeline_results['error'] = str(e)
finally:
pipeline_results['execution_end'] = datetime.utcnow()
pipeline_results['execution_duration'] = (
pipeline_results['execution_end'] - pipeline_results['execution_start']
).total_seconds()
return pipeline_results
Data Collection and Source Integration
The data collection phase requires sophisticated coordination across multiple economic data sources, each with unique characteristics, authentication requirements, and update patterns. Economic data sources range from high-frequency financial APIs that update continuously during market hours to annual statistical releases that follow predetermined publication schedules.
FRED (Federal Reserve Economic Data) represents the gold standard for economic data APIs, providing comprehensive coverage of US economic indicators with reliable access patterns and extensive metadata. FRED’s API design accommodates the revision patterns common in economic data by providing access to both real-time and final revised data series. This capability becomes crucial when building analytical systems that need to distinguish between the data that was available at a specific point in time versus the final revised values.
International data sources like the World Bank and IMF present additional challenges due to their focus on cross-country comparative data with varying methodologies and coverage. These sources often provide data in standardized formats that facilitate international comparisons but may require additional processing to align with domestic data sources. The temporal coverage of international data also tends to be more limited and subject to longer lags than domestic economic statistics.
The collection system must implement intelligent scheduling that accounts for the known release patterns of different economic indicators. Employment data typically follows monthly schedules tied to survey periods, while GDP data follows quarterly patterns with multiple revision cycles. Understanding these patterns enables the pipeline to collect data efficiently while minimizing unnecessary API calls during periods when new data is not expected.
class DataCollector:
"""Handles data collection from various economic data sources"""
def __init__(self, data_source: DataSource):
self.source = data_source
self.client = self._create_api_client()
self.rate_limiter = RateLimiter(self.source.name)
self.cache = DataCache(ttl_hours=24)
def _create_api_client(self):
"""Create appropriate API client for the data source"""
if self.source.name == 'FRED':
return FREDAPIClient(self.source.auth_config)
elif self.source.name == 'World Bank':
return WorldBankAPIClient()
elif self.source.name == 'IMF':
return IMFAPIClient()
else:
raise ValueError(f"Unsupported data source: {self.source.name}")
def collect_indicator_data(self, indicator_code: str, start_date: str,
end_date: str) -> pd.DataFrame:
"""Collect data for a specific indicator"""
# Check cache first
cache_key = f"{self.source.name}_{indicator_code}_{start_date}_{end_date}"
cached_data = self.cache.get(cache_key)
if cached_data is not None:
return cached_data
# Rate limiting
self.rate_limiter.wait()
try:
# Collect data using appropriate client
raw_data = self.client.get_data(
indicator_code,
start_date=start_date,
end_date=end_date
)
# Standardize data format
standardized_data = self._standardize_data_format(raw_data, indicator_code)
# Cache the result
self.cache.set(cache_key, standardized_data)
# Log collection success
logging.info(f"Collected {len(standardized_data)} observations for {indicator_code} from {self.source.name}")
return standardized_data
except Exception as e:
logging.error(f"Failed to collect {indicator_code} from {self.source.name}: {e}")
return pd.DataFrame()
def _standardize_data_format(self, raw_data: Any, indicator_code: str) -> pd.DataFrame:
"""Standardize data format across different sources"""
# Convert to DataFrame if necessary
if not isinstance(raw_data, pd.DataFrame):
if isinstance(raw_data, dict):
df = pd.DataFrame(raw_data)
elif isinstance(raw_data, list):
df = pd.DataFrame(raw_data)
else:
raise ValueError(f"Unsupported data format: {type(raw_data)}")
else:
df = raw_data.copy()
# Standardize column names
column_mapping = {
'date': 'date',
'value': 'value',
'obs_value': 'value',
'observation_date': 'date',
'year': 'date'
}
# Apply column mapping
for old_name, new_name in column_mapping.items():
if old_name in df.columns and new_name not in df.columns:
df = df.rename(columns={old_name: new_name})
# Ensure required columns exist
if 'date' not in df.columns or 'value' not in df.columns:
raise ValueError(f"Required columns missing in data for {indicator_code}")
# Standardize date format
df['date'] = pd.to_datetime(df['date'])
# Standardize value format
df['value'] = pd.to_numeric(df['value'], errors='coerce')
# Add metadata columns
df['source'] = self.source.name
df['indicator_code'] = indicator_code
df['collection_timestamp'] = datetime.utcnow()
# Sort by date
df = df.sort_values('date').reset_index(drop=True)
return df
Data Transformation and Harmonization
The transformation phase addresses the complex challenge of harmonizing economic data from sources that use different methodologies, units, frequencies, and geographic coverage. This harmonization goes beyond simple format standardization to include substantive adjustments that make cross-source comparisons meaningful and analytically valid.
Frequency harmonization represents one of the most technically challenging aspects of economic data transformation. GDP data released quarterly must be aligned with employment data released monthly and financial market data available daily. Simple interpolation or aggregation can introduce statistical artifacts that compromise analytical validity, requiring sophisticated temporal alignment techniques that preserve the underlying economic relationships.
Unit standardization becomes particularly complex when dealing with international data that might be reported in different currencies, different base years for real variables, or different population bases for per-capita measures. The transformation system must maintain comprehensive metadata about these conversions to ensure that analytical results can be properly interpreted and audited.
Seasonal adjustment harmonization requires careful attention because different sources might apply different seasonal adjustment procedures or provide both seasonally adjusted and non-adjusted data. The transformation system must make consistent choices about which data series to use while maintaining the ability to access alternative versions when required for specific analytical purposes.
class DataTransformer:
"""Handles transformation and harmonization of economic data"""
def __init__(self):
self.conversion_rates = ExchangeRateManager()
self.seasonal_adjuster = SeasonalAdjustmentEngine()
self.frequency_harmonizer = FrequencyHarmonizer()
self.unit_converter = UnitConverter()
def transform_dataset(self, df: pd.DataFrame, target_config: Dict[str, Any]) -> pd.DataFrame:
"""Apply comprehensive transformation to economic dataset"""
transformed_df = df.copy()
# 1. Handle missing values
transformed_df = self._handle_missing_values(transformed_df)
# 2. Frequency conversion if needed
if 'target_frequency' in target_config:
transformed_df = self.frequency_harmonizer.convert_frequency(
transformed_df,
target_frequency=target_config['target_frequency']
)
# 3. Unit conversion
if 'target_units' in target_config:
transformed_df = self.unit_converter.convert_units(
transformed_df,
target_units=target_config['target_units']
)
# 4. Currency conversion for international data
if 'target_currency' in target_config:
transformed_df = self._convert_currency(
transformed_df,
target_currency=target_config['target_currency']
)
# 5. Seasonal adjustment
if target_config.get('apply_seasonal_adjustment', False):
transformed_df = self.seasonal_adjuster.adjust_series(transformed_df)
# 6. Growth rate calculation if requested
if target_config.get('calculate_growth_rates', False):
transformed_df = self._calculate_growth_rates(transformed_df)
# 7. Add transformation metadata
transformed_df['transformation_timestamp'] = datetime.utcnow()
transformed_df['transformation_config'] = str(target_config)
return transformed_df
def _handle_missing_values(self, df: pd.DataFrame) -> pd.DataFrame:
"""Handle missing values using economic data best practices"""
result_df = df.copy()
# Forward fill for stock variables (levels)
stock_indicators = ['gdp', 'population', 'debt', 'reserves']
for indicator in stock_indicators:
if any(indicator in col.lower() for col in result_df.columns):
matching_cols = [col for col in result_df.columns if indicator in col.lower()]
for col in matching_cols:
result_df[col] = result_df[col].fillna(method='ffill', limit=3)
# Interpolate for flow variables (rates, changes)
flow_indicators = ['inflation', 'unemployment', 'interest', 'growth']
for indicator in flow_indicators:
if any(indicator in col.lower() for col in result_df.columns):
matching_cols = [col for col in result_df.columns if indicator in col.lower()]
for col in matching_cols:
result_df[col] = result_df[col].interpolate(method='linear', limit=2)
return result_df
def _convert_currency(self, df: pd.DataFrame, target_currency: str) -> pd.DataFrame:
"""Convert monetary values to target currency"""
result_df = df.copy()
# Identify monetary columns
monetary_indicators = ['gdp', 'debt', 'reserves', 'trade', 'investment']
monetary_columns = []
for indicator in monetary_indicators:
matching_cols = [col for col in result_df.columns if indicator in col.lower()]
monetary_columns.extend(matching_cols)
# Apply currency conversion
for col in monetary_columns:
if col in result_df.columns:
# Get source currency from metadata (simplified)
source_currency = self._infer_source_currency(result_df, col)
if source_currency != target_currency:
# Apply conversion rates
for idx, row in result_df.iterrows():
conversion_rate = self.conversion_rates.get_rate(
source_currency,
target_currency,
row['date']
)
if conversion_rate is not None:
result_df.at[idx, col] = row[col] * conversion_rate
return result_df
def _calculate_growth_rates(self, df: pd.DataFrame) -> pd.DataFrame:
"""Calculate various growth rate measures"""
result_df = df.copy()
# Identify level variables that should have growth rates calculated
level_indicators = ['gdp', 'population', 'employment', 'investment']
for indicator in level_indicators:
matching_cols = [col for col in result_df.columns
if indicator in col.lower() and 'growth' not in col.lower()]
for col in matching_cols:
if col in result_df.columns:
# Year-over-year growth rate
result_df[f'{col}_yoy_growth'] = (
result_df[col].pct_change(periods=4) * 100 # Assuming quarterly data
)
# Quarter-over-quarter growth rate (annualized)
result_df[f'{col}_qoq_growth_annualized'] = (
(result_df[col].pct_change() + 1) ** 4 - 1
) * 100
return result_df
def _infer_source_currency(self, df: pd.DataFrame, column: str) -> str:
"""Infer source currency from data context"""
# Simplified currency inference based on source
source = df['source'].iloc[0] if 'source' in df.columns else 'unknown'
currency_mapping = {
'FRED': 'USD',
'ECB': 'EUR',
'BOJ': 'JPY',
'World Bank': 'USD', # World Bank typically reports in USD
'IMF': 'USD'
}
return currency_mapping.get(source, 'USD')
Aggregation and Cross-Indicator Analysis
The aggregation phase implements the analytical logic that transforms individual economic indicators into composite measures and cross-indicator insights. This phase requires deep understanding of economic relationships and careful attention to statistical validity when combining indicators with different methodological foundations.
Cross-indicator analysis becomes particularly valuable when examining economic relationships that span multiple data sources and frequencies. For example, analyzing the relationship between monetary policy (interest rates), inflation expectations, and real economic activity requires combining high-frequency financial data with lower-frequency economic statistics while preserving the temporal relationships that drive economic dynamics.
Geographic aggregation presents unique challenges when combining country-level data into regional or global measures. Simple averaging might not be appropriate for economic indicators where countries have vastly different economic sizes, requiring weighted aggregation schemes that reflect economic significance. The aggregation system must also handle missing data situations where some countries report certain indicators while others do not.
Temporal aggregation across different frequencies requires sophisticated techniques that preserve the economic meaning of the indicators. Converting monthly employment data to quarterly frequency for comparison with GDP data should maintain the underlying labor market dynamics while providing meaningful alignment with economic growth measures.
class DataAggregator:
"""Handles aggregation and cross-indicator analysis"""
def __init__(self):
self.weighting_schemes = WeightingSchemeManager()
self.economic_relationships = EconomicRelationshipEngine()
self.statistical_engine = StatisticalAnalysisEngine()
def create_composite_indicators(self, datasets: Dict[str, pd.DataFrame],
composite_config: Dict[str, Any]) -> pd.DataFrame:
"""Create composite indicators from multiple data sources"""
composite_results = []
for composite_name, config in composite_config.items():
try:
composite_data = self._build_composite_indicator(
datasets,
composite_name,
config
)
composite_results.append(composite_data)
except Exception as e:
logging.error(f"Failed to create composite indicator {composite_name}: {e}")
if composite_results:
return pd.concat(composite_results, ignore_index=True)
else:
return pd.DataFrame()
def _build_composite_indicator(self, datasets: Dict[str, pd.DataFrame],
composite_name: str, config: Dict[str, Any]) -> pd.DataFrame:
"""Build a single composite indicator"""
# Extract component indicators
components = []
for component_config in config['components']:
dataset_name = component_config['dataset']
indicator_name = component_config['indicator']
if dataset_name in datasets:
component_data = datasets[dataset_name][
datasets[dataset_name]['indicator_code'] == indicator_name
].copy()
if not component_data.empty:
component_data['component_name'] = component_config.get('name', indicator_name)
component_data['component_weight'] = component_config.get('weight', 1.0)
components.append(component_data)
if not components:
raise ValueError(f"No valid components found for composite {composite_name}")
# Align components temporally
aligned_components = self._align_temporal_components(components)
# Calculate composite based on method
aggregation_method = config.get('method', 'weighted_average')
if aggregation_method == 'weighted_average':
composite_df = self._calculate_weighted_average(aligned_components)
elif aggregation_method == 'principal_component':
composite_df = self._calculate_principal_component(aligned_components)
elif aggregation_method == 'geometric_mean':
composite_df = self._calculate_geometric_mean(aligned_components)
else:
raise ValueError(f"Unsupported aggregation method: {aggregation_method}")
# Add composite metadata
composite_df['composite_name'] = composite_name
composite_df['aggregation_method'] = aggregation_method
composite_df['component_count'] = len(components)
return composite_df
def _align_temporal_components(self, components: List[pd.DataFrame]) -> pd.DataFrame:
"""Align components temporally for aggregation"""
# Find common date range
min_date = max(comp['date'].min() for comp in components)
max_date = min(comp['date'].max() for comp in components)
# Create common date index (monthly frequency)
common_dates = pd.date_range(start=min_date, end=max_date, freq='M')
aligned_data = []
for component in components:
# Resample to monthly frequency
comp_monthly = component.set_index('date')['value'].resample('M').last()
comp_monthly = comp_monthly.reindex(common_dates)
# Forward fill missing values (up to 3 periods)
comp_monthly = comp_monthly.fillna(method='ffill', limit=3)
aligned_data.append({
'component_name': component['component_name'].iloc[0],
'component_weight': component['component_weight'].iloc[0],
'values': comp_monthly
})
# Combine into single DataFrame
result_df = pd.DataFrame(index=common_dates)
for comp_data in aligned_data:
result_df[comp_data['component_name']] = comp_data['values']
result_df[f"{comp_data['component_name']}_weight"] = comp_data['component_weight']
result_df = result_df.reset_index()
result_df = result_df.rename(columns={'index': 'date'})
return result_df
def _calculate_weighted_average(self, aligned_df: pd.DataFrame) -> pd.DataFrame:
"""Calculate weighted average composite"""
# Identify value and weight columns
value_cols = [col for col in aligned_df.columns
if not col.endswith('_weight') and col != 'date']
composite_values = []
for idx, row in aligned_df.iterrows():
weighted_sum = 0
total_weight = 0
for col in value_cols:
weight_col = f"{col}_weight"
if not pd.isna(row[col]) and weight_col in aligned_df.columns:
weight = row[weight_col]
weighted_sum += row[col] * weight
total_weight += weight
if total_weight > 0:
composite_value = weighted_sum / total_weight
else:
composite_value = np.nan
composite_values.append(composite_value)
result_df = pd.DataFrame({
'date': aligned_df['date'],
'value': composite_values
})
return result_df
def perform_cross_indicator_analysis(self, datasets: Dict[str, pd.DataFrame],
analysis_config: Dict[str, Any]) -> Dict[str, Any]:
"""Perform cross-indicator analysis"""
analysis_results = {}
# Correlation analysis
if 'correlation_analysis' in analysis_config:
correlation_results = self._perform_correlation_analysis(
datasets,
analysis_config['correlation_analysis']
)
analysis_results['correlations'] = correlation_results
# Lead-lag analysis
if 'lead_lag_analysis' in analysis_config:
lead_lag_results = self._perform_lead_lag_analysis(
datasets,
analysis_config['lead_lag_analysis']
)
analysis_results['lead_lag'] = lead_lag_results
# Regime analysis
if 'regime_analysis' in analysis_config:
regime_results = self._perform_regime_analysis(
datasets,
analysis_config['regime_analysis']
)
analysis_results['regimes'] = regime_results
return analysis_results
def _perform_correlation_analysis(self, datasets: Dict[str, pd.DataFrame],
config: Dict[str, Any]) -> Dict[str, float]:
"""Perform correlation analysis between indicators"""
# Extract specified indicators
indicators_data = {}
for indicator_config in config['indicators']:
dataset_name = indicator_config['dataset']
indicator_code = indicator_config['indicator']
if dataset_name in datasets:
indicator_df = datasets[dataset_name][
datasets[dataset_name]['indicator_code'] == indicator_code
]
if not indicator_df.empty:
indicators_data[indicator_code] = indicator_df.set_index('date')['value']
# Calculate cross-correlations
correlation_matrix = pd.DataFrame(indicators_data).corr()
return correlation_matrix.to_dict()
Pipeline Orchestration and Monitoring
Production deployment of macroeconomic aggregation pipelines requires sophisticated orchestration that can handle the complex dependencies between different data sources, transformation steps, and analytical outputs. The orchestration system must account for the varying update schedules of economic data sources and implement intelligent retry mechanisms when source data is temporarily unavailable.
The monitoring framework must track both technical performance metrics and domain-specific quality indicators that reflect the unique characteristics of economic data processing. Technical metrics include API response times, data processing throughput, and system resource utilization, while domain-specific metrics track data freshness, cross-source consistency, and analytical result stability.
Alert systems become particularly important in economic data pipelines because data quality issues or processing failures can have significant business impact when they affect critical analytical outputs. The alerting framework should implement intelligent escalation that distinguishes between minor issues that can be resolved automatically and major problems that require immediate human intervention.
Integration with the broader economic data architecture requires careful attention to data lineage tracking and version management. The systems described in Data Lake Architecture Economic Analytics provide the storage foundation, while Cloud Deployment Scaling Economic Data Systems covers the infrastructure requirements for scaling these pipeline patterns to enterprise levels.
class PipelineOrchestrator:
"""Orchestrates the complete economic data aggregation pipeline"""
def __init__(self, config: Dict[str, Any]):
self.config = config
self.scheduler = TaskScheduler()
self.monitor = PipelineMonitor()
self.alerting = AlertingSystem()
self.state_manager = PipelineStateManager()
def schedule_pipeline_execution(self):
"""Schedule pipeline execution based on data source patterns"""
# Schedule immediate execution for daily indicators
self.scheduler.schedule_task(
name='daily_indicators',
func=self._execute_daily_pipeline,
schedule='0 9 * * *', # 9 AM daily
timezone='UTC'
)
# Schedule weekly aggregation
self.scheduler.schedule_task(
name='weekly_aggregation',
func=self._execute_weekly_aggregation,
schedule='0 10 * * 1', # 10 AM every Monday
timezone='UTC'
)
# Schedule monthly comprehensive analysis
self.scheduler.schedule_task(
name='monthly_analysis',
func=self._execute_monthly_analysis,
schedule='0 11 1 * *', # 11 AM on first day of month
timezone='UTC'
)
# Schedule ad-hoc execution triggers
self.scheduler.schedule_task(
name='data_availability_check',
func=self._check_data_availability,
schedule='*/30 * * * *', # Every 30 minutes
timezone='UTC'
)
def _execute_daily_pipeline(self):
"""Execute daily data collection and basic aggregation"""
execution_id = self.state_manager.create_execution('daily_pipeline')
try:
# Collect high-frequency indicators
daily_indicators = self.config['daily_indicators']
pipeline = EconomicDataPipeline(self.config)
results = pipeline.execute_pipeline(
start_date=(datetime.utcnow() - timedelta(days=7)).strftime('%Y-%m-%d'),
end_date=datetime.utcnow().strftime('%Y-%m-%d')
)
# Update monitoring metrics
self.monitor.record_execution_metrics(execution_id, results)
# Check for quality issues
if results['status'] != 'completed':
self.alerting.send_alert(
severity='medium',
message=f"Daily pipeline execution issues: {results.get('error', 'Unknown error')}",
execution_id=execution_id
)
self.state_manager.complete_execution(execution_id, results)
except Exception as e:
self.state_manager.fail_execution(execution_id, str(e))
self.alerting.send_alert(
severity='high',
message=f"Daily pipeline execution failed: {str(e)}",
execution_id=execution_id
)
This comprehensive approach to macroeconomic data aggregation provides organizations with the capability to build robust analytical platforms that can reliably process complex economic data from multiple sources. The pipeline architecture scales from simple indicator collection to sophisticated cross-indicator analysis while maintaining the data quality and auditability requirements essential for economic analysis applications.
The patterns and implementations shown here integrate seamlessly with the broader economic data ecosystem, particularly the quality frameworks and real-time processing capabilities that extend the batch aggregation paradigm to comprehensive economic data platforms. These foundations enable the advanced analytical capabilities covered in the machine learning and visualization guides, providing the data infrastructure necessary for modern economic analysis and decision-making.
Related Guides
For comprehensive economic data pipeline implementation, explore these complementary resources:
- API Integration for Economic Data Sources - Foundation for data collection components of aggregation pipelines
- Data Quality Practices for Economic Datasets - Essential quality controls for aggregated economic data
- Real-Time Data Processing Economic Indicators - Extend aggregation pipelines with streaming capabilities
- Data Lake Architecture Economic Analytics - Storage architecture for aggregated economic datasets
- ETL Tool Comparison - Choose the right tools for economic data aggregation workflows
- Machine Learning Applications Economic Data Analysis - Apply advanced analytics to aggregated datasets
- Cloud Deployment Scaling Economic Data Systems - Scale aggregation pipelines in production environments