---

name: ds-data-ingestion compatibility: opencode completeness: 95 content-types:

• code
• guidance
• do-dont
• examples description: '"Provides Designs and implements ETL pipelines, streaming data ingestion batch processing, and data pipeline orchestration for reliable data flow"' license: MIT maturity: stable metadata: domain: coding output-format: code related-skills: ds-data-collection, ds-data-quality, ds-data-versioning role: implementation scope: implementation triggers: ETL pipeline, data ingestion, streaming data, batch processing, pipeline how do i ingest data archetypes:
  • tactical
  • generation anti_triggers:
  • brainstorming
  • vague ideation
  • code golf
  • over-engineering response_profile: verbosity: low directive_strength: high abstraction_level: operational version: "1.0.0"

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Data Ingestion

Comprehensive guide to data ingestion in machine learning and data science workflows.

When to Use This Skill

• Solving real-world data collection & ingestion problems
• Building machine learning pipelines with data ingestion
• Implementing best practices for data ingestion
• Optimizing model performance using data ingestion techniques
• Learning industry-standard approaches to data ingestion

When NOT to Use This Skill

• When using pre-built libraries without understanding underlying concepts
• For toy problems that don't require data ingestion rigor
• When domain expertise in specific problem requires different approach
• If your problem doesn't require the complexity this skill provides

Purpose and Key Concepts

Data Ingestion is a critical component of the machine learning workflow. This skill covers:

1. Theoretical foundations — Mathematical principles and statistical concepts
2. Practical implementation — Working code examples and patterns
3. Common pitfalls — Mistakes to avoid and how to recover from them
4. Best practices — Industry-standard approaches and optimization techniques

Core Workflow

1. Understand the problem — Clearly define what you're solving for
2. Select approach — Choose the right technique for your data and constraints
3. Implement solution — Write clean, tested code following best practices
4. Validate results — Verify your implementation with tests and validation
5. Optimize performance — Improve efficiency and accuracy incrementally

Implementation Patterns

Pattern 1: Basic Data Ingestion

import pandas as pd
import numpy as np
from typing import Dict, Any, Optional

def basic_data_ingestion(file_path: Optional[str] = None, sample_data: Optional[pd.DataFrame] = None) -> Dict[str, Any]:
    """
    Basic data ingestion: reads data, handles missing values, validates schema.
    """
    if sample_data is not None:
        df = sample_data.copy()
    elif file_path:
        df = pd.read_csv(file_path)
    else:
        raise ValueError("Provide either file_path or sample_data")
    
    # Handle missing values
    numeric_cols = df.select_dtypes(include=[np.number]).columns
    df[numeric_cols] = df[numeric_cols].fillna(df[numeric_cols].mean())
    categorical_cols = df.select_dtypes(include=['object', 'category']).columns
    df[categorical_cols] = df[categorical_cols].fillna(df[categorical_cols].mode().iloc[0])
    
    # Validate schema
    required_cols = ['feature_1', 'feature_2', 'target']
    missing = [col for col in required_cols if col not in df.columns]
    if missing:
        raise ValueError(f"Missing required columns: {missing}")
        
    return {
        'data': df
        'shape': df.shape
        'missing_values': int(df.isnull().sum().sum())
        'status': 'success'
    }

Pattern 2: Production-Ready Data Ingestion

import logging
import pandas as pd
import numpy as np
from typing import Any, Dict, List, Optional
from datetime import datetime

logger = logging.getLogger(__name__)

class ProductionDataIngestion:
    """Production-grade data ingestion following SOLID principles."""
    
    def __init__(self, required_columns: List[str], log_level: str = "INFO"):
        self.required_columns = required_columns
        self.logger = logging.getLogger(__name__)
        self.logger.setLevel(getattr(logging, log_level))
        
    def _validate_schema(self, df: pd.DataFrame) -> bool:
        missing = [col for col in self.required_columns if col not in df.columns]
        if missing:
            raise ValueError(f"Schema validation failed. Missing: {missing}")
        return True
        
    def _clean_data(self, df: pd.DataFrame) -> pd.DataFrame:
        df = df.drop_duplicates()
        numeric_cols = df.select_dtypes(include=[np.number]).columns
        df[numeric_cols] = df[numeric_cols].fillna(df[numeric_cols].median())
        return df
        
    def execute(self, data: pd.DataFrame) -> Dict[str, Any]:
        """Execute production data ingestion pipeline."""
        try:
            self._validate_schema(data)
            cleaned_df = self._clean_data(data)
            result = {
                'ingested_data': cleaned_df
                'row_count': len(cleaned_df)
                'column_count': len(cleaned_df.columns)
                'timestamp': datetime.now().isoformat()
                'status': 'completed'
            }
            self.logger.info(f"Successfully ingested {len(cleaned_df)} rows.")
            return result
        except Exception as e:
            self.logger.error(f"Ingestion failed: {str(e)}")
            return {'status': 'failed', 'error': str(e)}

Pattern 3: BAD vs GOOD Implementation

# BAD: Hardcoded values, no error handling, bypasses validation
def bad_ingestion(df):
    df['col1'] = df['col1'].fillna(0)
    df['col2'] = df['col2'].fillna(0)
    return df

# GOOD: Configurable, validated, follows DRY principle
def good_ingestion(df: pd.DataFrame, fill_strategy: str = "median") -> pd.DataFrame:
    if df.empty:
        raise ValueError("DataFrame cannot be empty")
    numeric_cols = df.select_dtypes(include=[np.number]).columns
    if fill_strategy == "median":
        df[numeric_cols] = df[numeric_cols].fillna(df[numeric_cols].median())
    elif fill_strategy == "mean":
        df[numeric_cols] = df[numeric_cols].fillna(df[numeric_cols].mean())
    else:
        raise ValueError("Invalid fill_strategy")
    return df

Best Practices

• ✅ Always validate your implementation on test data
• ✅ Document your assumptions and methodology
• ✅ Use version control for reproducibility
• ✅ Monitor performance metrics in production
• ✅ Periodically review and update your approach
• ✅ Test with edge cases and outliers
• ✅ Log all significant operations for debugging
• ✅ Adhere to SOLID principles and DRY guidelines for maintainable, scalable code

Common Pitfalls

| Pitfall | Problem | Solution | |

---

---

Constraints

MUST DO

• Validate all data preprocessing steps are fit-only on training data, never on validation or test sets
• Implement reproducible pipelines with fixed random seeds and deterministic operations where possible
• Report model performance with confidence intervals via bootstrapping or cross-validation across multiple runs
• Log all experiments with parameters, metrics, and artifacts using MLflow or equivalent tracking system

MUST NOT DO

• Do not evaluate a model on the same data used for training — always hold out a proper test set
• Avoid overfitting to the validation set by limiting hyperparameter search iterations
• Never use features that can only be computed at inference time (look-ahead bias)
• Do not report single-run accuracy without statistical significance testing or error bars

Live References

Authoritative documentation links for this skill's domain. The model follows markdown links at load time to resolve external references and inline content.

• Apache Spark Streaming Programming Guide
• Apache Kafka Connect — Data Ingestion
• AWS Kinesis — Real-Time Data Streaming
• Google Cloud Pub/Sub Documentation
• dbt Docs — Data Transformation & Ingestion