Database Story for Curious Builders

From punch cards to lakehouses, meet the people who taught databases to safeguard our records

In 1890 census clerks feared they would miss yet another publication deadline. Herman Hollerith rolled in his punch card machines and promised, “Let holes carry the numbers for you.” Decades later bank tellers and Apollo coordinators pleaded for faster lookups, so they tried spinning disks and hierarchical maps.

During the 1970s E.F. Codd and Peter Chen organized data into tables and diagrams, while Oracle proved those ideas worked on the shop floor. In the 1990s MySQL and PostgreSQL broadened open-source options, and the 2000s brought Dynamo and MongoDB for web-scale experiments.

Tap any year to discover the problem each era faced, the fix they shipped, and how it echoes in today’s systems. New to terms like SQL, NoSQL, lakehouse, or vector search? Each modal keeps the focus on people and decisions so you never get lost.

Selecting a year opens a nearby dialog so you can keep your place while reading the details.

1890s

Census clerks and punch cards

Clerks and engineers traded paper tables for punch cards and electromechanical tabulators so machines could shoulder repetitive counts.

1950s

Magnetic disks promise instant updates

Bank and insurance teams were tired of swapping tapes, and spinning disks offered a “fix it the moment you notice it” workflow.

1960s

Rockets and banks map hierarchies

Apollo parts and airline bookings forced teams to stack records in layers or link them with network models.

1970s

Relational thinking and diagram language

Researchers and designers agreed to “just state the conditions,” standardizing SQL and ER diagrams along the way.

1980s

Standardization and parallel engines spread

Large enterprises made relational databases their default tool, accelerating SQL standards and massive parallel hardware.

1990s

Open source and data warehouses

Web startups picked lightweight, free databases while executives built dedicated warehouses for decision making.

2000s

Web scale and NoSQL experiments

Internet companies sharded logs across thousands of machines and tested document or key-value stores for flexibility.

2010s

Global consistency and streaming pipelines

Worldwide services needed the same data everywhere and streamed events to prepare real-time analytics.

2020s

Lakehouses and vector search arrive

Architects bridged lakes and warehouses as AI teams adopted vector databases for semantic search.

Further reading

Dive into the primary sources on relational theory, distributed design, lakehouse strategy, and vector search.