I Love Reading Technology History: Why Things Were Created, and Why They Were Replaced
I love reading technology history because it explains two powerful things: why a tool was born , and why people eventually moved on . Data formats tell this story perfectly. None of them were random. Each one answered a real pain of its era then later met new pressures: bigger scale, faster systems, better developer experience, lower cost. So instead of treating XML, JSON, and others like a…
Reading technology history provides insight into why tools are created and why they are later replaced. Data formats, in particular, illustrate this evolution. Each format emerged to address real pain points of its time, whether it was the need for simple row-column movement in CSV during the 1960s-1970s, or the requirement for binary language consistency in XDR for distributed systems in 1987.
XML appeared in 1998 as businesses demanded structure and enterprise-level governance. Despite its verbosity, XML remained relevant in regulated and legacy-heavy ecosystems due to its predictability. JSON, arriving in the early 2000s, became the web's favorite for lightweight communication, especially with JavaScript's widespread use. However, JSON's limitations at large scale led to the adoption of binary formats like Protocol Buffers (Protobuf) and MessagePack.
Protobuf, introduced around 2001, offered compact binary encoding and strong schemas, catering to the needs of microservices and high-throughput systems. MessagePack, its fast cousin, maintained JSON's data model while improving encoding efficiency. Avro emerged in 2009 to handle schema evolution in big data and event-driven architectures, becoming essential in Kafka and Hadoop ecosystems. Parquet and ORC formats, designed for analytics workloads, revolutionized data storage and query efficiency in 2013.
CBOR, introduced in 2014, provided a compact binary structure suited for constrained environments like IoT devices. FlatBuffers and Cap’n Proto, emerging in 2015, aimed for ultra-low latency communication. These data formats evolved to address specific needs, whether it was schema evolution, analytical efficiency, or low-latency communication, shaping the landscape of technology development and replacement.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
