No Bad Questions About ML
Definition of Data serialization
What is data serialization?
Data serialization is the process that transforms structured or semi-structured data into a format like JSON, YAML or XML that's compact, portable, and readable by different systems and languages.
Serialization empowers you to save the state of an object and reconstruct it elsewhere. This standardized approach streamlines data exchange, allowing you to transfer objects effortlessly:
- Across networks: For messaging applications.
- Between applications: Via web services (REST APIs).
- Through firewalls: In text-based data serialization formats (JSON) and binary data serialization formats (Protobuf).
- Across different systems: Spanning domains and data stores.
- For data comparison: To identify changes over time.
Imagine packing your belongings for a trip – serialization is like turning everything into a neat, compact package.
How data serialization works
Computer data is organized into structures like arrays, tables, trees, and classes. When these structures need to be saved or sent elsewhere, like over a network, they undergo serialization. Serialization encodes data into a byte stream or characters. This is straightforward for basic data, like numbers or strings.
But for more complex structures, like nested objects, serialization becomes trickier. Objects are collapsed into bytes, and info like order is added to rebuild them later.
When objects have references to other variables, those references are tracked and serialized too, ensuring each object is only serialized once. However, all nested objects must also be serializable.
Finally, the serialized data is saved as a byte sequence using a standard format. Common formats include ISO-8859-1 for English characters and numerals, and UTF-8 for multilingual and scientific data, with each character taking 1-4 bytes.
Which programming languages offer serialization features
Multiple popular object-oriented programming languages offer native support or libraries for serialization, including Java, .NET, C++, Node.js, Python, and Go. These languages provide robust and user-friendly serialization features, allowing developers to serialize and deserialize data seamlessly in their applications.
Use cases for data serialization
Let's explore some of its most critical use cases:
- Microservice architecture — Enables smooth communication between independent services, fostering a decoupled architecture.
- Data integration — Facilitates data transformation and conversion during ETL processes, ensuring consistency across diverse sources.
- Message queues — Enables data exchange in real-time systems through message queues and event streams, preventing bottlenecks.
- Big Data processing — Specialized formats like Avro and Parquet optimize storage and processing of large datasets in distributed computing frameworks.
Key Takeaways
- Data serialization transforms data structures into a compact, portable format (e.g., JSON, XML) for easy storage, transmission, and exchange between systems and languages.
- It streamlines data exchange across networks, applications, firewalls, and systems and facilitates data comparison for identifying changes over time.
- How it works. 1. Encodes data into a byte stream or characters. 2. Handles complex structures (nested objects, references), includes Flattening - Collapses nested data while preserving order and Reference tracking - Ensures objects are serialized only once. 3. Uses standard formats like UTF-8 for efficient storage and handling of diverse data types.
- Popular object-oriented languages (Java, .NET, C++, Python, etc.) offer built-in features or libraries for serialization.
- Use cases include microservice architecture, data integration, message queues, and Big Data processing.
