Your Data Lives Everywhere, and That is the Problem

2026-02-26 · Source: Data Engineering on Medium · Field: Technology & Digital — Data Science & Analytics, Cloud Computing & IT Infrastructure, Software Development & Engineering · Depth: Intermediate, medium

Summary

Data never stays in one place, spreading across databases, cloud storage, and streaming systems, making data integration a primary task for data engineers. Enterprise data typically resides in cloud storage like AWS S3, Azure Data Lake, or Google Cloud Storage for raw files; databases such as PostgreSQL or MySQL for transactional data; and streaming systems like Kafka or Kinesis for real-time events. Modern data platforms often use cloud storage as a foundation due to its scalability and cost-effectiveness, requiring careful handling of authentication and file formats. Key file formats include CSV for simplicity, JSON for nested data, Parquet for columnar analytics, Avro for self-describing streams, and Delta for transactional capabilities. The article emphasizes converting data to Parquet or Delta early, explicitly defining schemas, and using partitioning for performance, while also highlighting common production challenges like changing source systems, expiring credentials, and varying data quality.

Key takeaway

For Data Engineers building data pipelines, recognize that data scattering is a reality, not a design flaw. Prioritize converting raw data into Delta tables with explicit schemas as early as possible to establish a reliable foundation. Implement robust secret management and partition data by date to ensure performance and security, anticipating and building for common production failures like schema changes or credential expiration.

Key insights

Data integration is the core task in data engineering due to data scattering across diverse systems.

Principles

• Data scatters naturally across systems.
• Convert to Parquet or Delta early.
• Define schemas explicitly for production.

Method

Read data from source, immediately write to Delta tables (Bronze layer) with explicit schemas, and partition by date for efficient querying. Use secret management for credentials.

In practice

• Use read replicas for database analytics.
• Parallelize database reads for speed.
• Partition data by date for large datasets.

Topics

• Data Integration
• Cloud Data Storage
• Data Lake Formats
• Data Pipeline Best Practices
• Schema Management

Best for: Data Engineer, MLOps Engineer, Software Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Data Engineering on Medium.