In the world of data processing and analytics, schemas define the structure, relationships, and constraints of the data. Two paradigms dominate this landscape: Schema-on-read and Schema-on-write. These approaches are critical to how data is ingested, stored, and queried, and their application can significantly affect performance, flexibility, and usability in various scenarios. Over two decades in the tech corporate world, I have led transformative initiatives that ignite innovation, build scalable solutions, and drive organizations to unparalleled tech success. My expertise has become a go-to resource for businesses determined to revolutionise their technology and achieve remarkable growth. In this tech concept, we’ll explore their definitions, purposes, use cases, technologies, and best practices for determining the right approach.

What is Schema-on-read ?

Schema-on-read means the schema is applied to the data only when it is read or queried. This paradigm defers the schema definition to the moment of access, allowing raw data to be ingested and stored as-is.

• Example: A JSON file stored in a data lake is parsed and queried when accessed by a tool like Apache Spark.

Purpose

• Provides flexibility for unstructured or semi-structured data.
• Ideal for exploratory analysis, where the structure of the data might evolve over time.
• Often used in data lakes and big data environments.

What is Schema-on-write ?

Schema-on-write requires that the data conforms to a predefined schema before being written to storage. This ensures data integrity and consistency from the moment it is ingested.

• Example: A relational database like PostgreSQL requires data to match its schema before insertion.

Purpose

• Ensures data integrity and consistency by enforcing a schema upfront.
• Suitable for transactional systems, where strict structure and validation are necessary.
• Commonly used in data warehouses and relational databases.

Use Cases

Schema-on-read

1. Big Data Analytics:
   • Tools: Hadoop, Apache Spark, AWS S3.
   • Scenario: Analyzing clickstream data, which is stored raw and queried in various formats for different analyses.
2. Data Lakes:
   • Technologies: Azure Data Lake, Google Cloud Storage.
   • Scenario: Storing large volumes of unstructured IoT data for downstream analytics.
3. Exploratory Data Analysis:
   • Scenario: Data scientists working with raw JSON or CSV files to derive insights.

Schema-on-write

1. Transactional Systems:
   • Tools: MySQL, Oracle, PostgreSQL.
   • Scenario: E-commerce platforms storing structured order and payment details.
2. Data Warehouses:
   • Technologies: Snowflake, BigQuery.
   • Scenario: Structuring customer data for business intelligence reports.
3. Regulatory Compliance:
   • Scenario: Financial institutions storing audit-compliant data in predefined schemas.

Technologies Used

Schema-on-read

• Storage: Amazon S3, Azure Blob Storage, HDFS.
• Query Engines: Presto, Apache Drill, Athena.
• Processing Frameworks: Apache Spark, Dask.

Schema-on-write

• Databases: Oracle, PostgreSQL, MySQL.
• Data Warehousing Tools: Snowflake, Redshift, Teradata.
• ETL Tools: Informatica, Talend.

Right Approach and Timing

When to Use Schema-on-read

• Exploratory Analysis: When the schema might evolve or is unknown at the start.
• Unstructured Data: For semi-structured or raw data sources like logs, JSON, or IoT streams.
• Scalability: When the system needs to scale to accommodate diverse data formats.

When to Use Schema-on-write

• Transactional Integrity: For systems that demand high reliability and ACID compliance.
• Regulatory Compliance: When strict data structures are mandated by law or policy.
• Operational Efficiency: When well-defined schemas can optimize query performance.

Hybrid Approach

In modern architectures, a hybrid approach combining schema-on-read and schema-on-write is often employed. For instance, raw data can be stored in a data lake (schema-on-read) and later processed and moved into a data warehouse (schema-on-write) for reporting.

Best Practices

1. Understand Business Needs:
   • Determine the type of data and its use case.
2. Optimize for Performance:
   • Use schema-on-write for predictable workloads.
   • Leverage schema-on-read for flexibility in exploratory scenarios.
3. Use Appropriate Tools:
   • Match the technology stack to your chosen schema strategy.
4. Adopt Governance Policies:
   • Ensure proper data cataloging and metadata management in schema-on-read setups.
   • Enforce rigorous schema definitions in schema-on-write environments.

My Tech Advice: The concepts of Schema-on-Read and Schema-on-Write are foundational paradigms in data processing. Though they often take a backseat in today’s tech developments, understanding and embracing these principles is essential for building robust data technologies. While schema-on-read prioritizes flexibility and adaptability, schema-on-write emphasizes structure and consistency. Understanding these approaches and applying them appropriately can help organizations unlock the full potential of their data assets.

#AskDushyant

#TechConcept #TechAdvice #DataTech #DataLake #BigData