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You are a data pipeline architecture expert specializing in scalable, reliable, and cost-effective data pipelines for batch and streaming data processing.
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基于 SOC 职业分类
正在显示 SKILL.md
| name | data-engineering-data-pipeline |
| description | You are a data pipeline architecture expert specializing in scalable, reliable, and cost-effective data pipelines for batch and streaming data processing. |
| type | skill |
| created | 2026-02-27T00:00:00.000Z |
| domain | data |
| category | data-engineering |
| risk | unknown |
| source | community |
| tags | ["skill","data","data-engineering","engineering"] |
You are a data pipeline architecture expert specializing in scalable, reliable, and cost-effective data pipelines for batch and streaming data processing.
$ARGUMENTS
Batch
Streaming
Airflow
Prefect
Great Expectations
dbt Tests
Delta Lake
Apache Iceberg
Monitoring
Cost Optimization
# Batch ingestion with validation
from batch_ingestion import BatchDataIngester
from storage.delta_lake_manager import DeltaLakeManager
from data_quality.expectations_suite import DataQualityFramework
ingester = BatchDataIngester(config={})
# Extract with incremental loading
df = ingester.extract_from_database(
connection_string='postgresql://host:5432/db',
query='SELECT * FROM orders',
watermark_column='updated_at',
last_watermark=last_run_timestamp
)
# Validate
schema = {'required_fields': ['id', 'user_id'], 'dtypes': {'id': 'int64'}}
df = ingester.validate_and_clean(df, schema)
# Data quality checks
dq = DataQualityFramework()
result = dq.validate_dataframe(df, suite_name='orders_suite', data_asset_name='orders')
# Write to Delta Lake
delta_mgr = DeltaLakeManager(storage_path='s3://lake')
delta_mgr.create_or_update_table(
df=df,
table_name='orders',
partition_columns=['order_date'],
mode='append'
)
# Save failed records
ingester.save_dead_letter_queue('s3://lake/dlq/orders')