1. Pipeline Architecture Overview
A web scraping pipeline for economic data requires careful orchestration of multiple components to handle data collection, processing, and storage. The system needs to adapt to different data sources while maintaining consistent output formats. This architecture separates concerns into distinct modules for configuration, scraping, processing, and storage, allowing each component to evolve independently.
class ScrapingPipeline:
def __init__(self, config_path):
self.config = self._load_config(config_path)
self.scraper = WebScraper()
self.processor = DataProcessor()
self.storage = DataStorage()
self.logger = logging.getLogger(__name__)
def execute(self, source_id):
with self.monitor.track_execution(source_id):
data = self.scraper.scrape_url(
self.config['sources'][source_id]
)
return self.processor.process_data(data)
2. Configuration System
The configuration system uses YAML to define scraping rules, making it easy to add or modify data sources without changing code. Each source configuration specifies URL patterns, HTML selectors, and validation rules. This approach separates the technical implementation from the source-specific details, enabling non-technical users to manage data source configurations.
sources:
gdp_data:
url_pattern: "https://example.com/economics/{indicator}"
selectors:
value: "#gdp-table tbody tr td:first-child"
date: "#gdp-table tbody tr td:last-child"
frequency: "quarterly"
requires_javascript: false
validation:
value:
type: "float"
range: [-30, 30]
3. Data Collection Engine
The collection engine handles both static and dynamic web content through a unified interface. For JavaScript-rendered pages, it automatically switches to using Selenium WebDriver, while static pages use requests for better performance. The engine implements rate limiting and respects robots.txt directives to ensure ethical data collection.
class WebScraper:
def __init__(self):
self.session = self._init_session()
self.driver = None
self.rate_limiter = RateLimiter(requests_per_minute=30)
def scrape_url(self, config):
self.rate_limiter.wait()
if config['requires_javascript']:
return self._scrape_dynamic(config)
return self._scrape_static(config)
4. Data Processing & Validation
The processing pipeline transforms raw scraped data into standardized formats while validating data integrity. It handles common issues like inconsistent date formats, number representations, and missing values. Each transformation step is logged for debugging and audit purposes.
class DataProcessor:
def process_data(self, raw_data):
validated_data = self._validate_schema(raw_data)
clean_data = self._normalize_formats(validated_data)
return self._transform_types(clean_data)
def _normalize_formats(self, data):
for field, value in data.items():
if self._is_date_field(field):
data[field] = self._standardize_date(value)
elif self._is_numeric_field(field):
data[field] = self._convert_to_float(value)
return data
5. Storage & Versioning
The storage system implements a versioned approach to data management, tracking all changes and updates to economic indicators. It uses a combination of partitioned storage for efficient querying and metadata tracking for data lineage. This design enables historical analysis and data quality monitoring.
class DataStorage:
def save(self, data, source_id):
version = self._generate_version()
path = self._get_partitioned_path(source_id, version)
with self.transaction() as txn:
txn.write_data(path, data)
txn.write_metadata(path, {
'version': version,
'timestamp': datetime.utcnow(),
'source': source_id,
'record_count': len(data)
})
6. Error Handling & Recovery
The error handling system provides automatic retry mechanisms with exponential backoff for transient failures. It distinguishes between different types of errors (network, parsing, validation) and implements appropriate recovery strategies for each. This ensures resilient operation even under unstable network conditions or website changes.
class ErrorHandler:
def execute_with_retry(self, operation, max_retries=3):
for attempt in range(max_retries):
try:
return operation()
except NetworkError as e:
self._handle_network_error(e, attempt)
except ValidationError as e:
self._handle_validation_error(e, attempt)
except ParsingError as e:
self._handle_parsing_error(e, attempt)
7. Monitoring & Alerting
The monitoring system tracks both technical metrics and data quality indicators in real-time. It generates alerts for anomalies in data patterns, scraping failures, and performance degradation. Custom thresholds can be set for different economic indicators based on their expected behavior.
class PipelineMonitor:
def track_execution(self, source_id):
metrics = {
'start_time': time.time(),
'records_processed': 0,
'validation_errors': 0
}
def update_metrics(event_type, data):
metrics['records_processed'] += len(data)
self._check_thresholds(metrics)
self._record_metrics(source_id, metrics)
return MetricsContext(update_metrics)
8. Usage Examples & Best Practices
This section demonstrates practical implementation patterns and common usage scenarios. The examples show how to configure new data sources, handle different types of economic data, and implement custom validation rules. These patterns have been proven in production environments handling diverse economic indicators.
# Complete pipeline example
def main():
pipeline = ScrapingPipeline('config.yaml')
monitor = PipelineMonitor()
with monitor.session() as session:
data = pipeline.execute('gdp_quarterly')
session.record_execution(data)
if monitor.should_alert():
alert_team("Anomalies detected in GDP data")
Remember to regularly review and update selector patterns as websites change, and always respect rate limits and terms of service of the websites being scraped.