# Big data

> Data so large, fast or messy that one ordinary computer cannot handle it.

- Level: Basic
- Topics: Data
- URL: https://tidbit.legios.com.ar/big-data/
- In Spanish: [Big data](https://tidbit.legios.com.ar/es/big-data/)

**Big data** describes collections of data too big or too fast for the usual tools on a single computer. The classic definition uses three Vs: **volume** (huge amounts, far beyond what fits on one machine), **velocity** (arriving constantly, like every click on a website or every reading from thousands of sensors) and **variety** (many shapes at once: tables, text, images, logs).

The term became popular in the late 2000s, when companies like Google and Yahoo needed to process the whole web. Their answer was to split the data into pieces and process them in parallel across many cheap machines, an idea that led to open source tools like Hadoop and later Spark. Today the same idea powers cloud data warehouses, data lakes and streaming systems, so a company can query years of records without owning a single server.

Examples are everywhere: a streaming service recommending shows from what millions of people watched, a bank flagging an unusual card payment in real time, a city timing traffic lights from road sensors. The term itself has faded a little, because handling large data has become normal. What has not changed is the lesson behind it: more data only helps if it is good data and someone asks it a useful question.

## An analogy

Counting the coins in your pocket is easy. Counting every coin that passes through a country's shops in a day needs thousands of counters working at once, each on its own pile, and someone adding up their totals.

## Keep in mind

- The three Vs: volume, velocity and variety.
- Handled by splitting work across many machines in parallel.
- Hadoop and Spark started it; cloud warehouses and lakes made it common.
- Big is not the same as useful: quality and good questions still matter.

## Go deeper

- [What is big data? (AWS)](https://aws.amazon.com/what-is/big-data/)
- [Big data on Wikipedia](https://en.wikipedia.org/wiki/Big_data)

## Related terms

- [Google](https://tidbit.legios.com.ar/google/): The search company that gives most products away and makes its money on ads.
- [Open source](https://tidbit.legios.com.ar/open-source/): Software whose code anyone can read, use, change and share.
- [Apache Spark](https://tidbit.legios.com.ar/apache-spark/): An open source engine that splits work on huge datasets across many machines.
- [Cloud computing](https://tidbit.legios.com.ar/cloud-computing/): Renting computers over the internet instead of buying your own.
- [Data warehouse](https://tidbit.legios.com.ar/data-warehouse/): A database built for analysis: years of history, from many sources, in one place.
- [Data lake](https://tidbit.legios.com.ar/data-lake/): Cheap storage where all your data is kept as raw files, to be sorted out later.
- [Server](https://tidbit.legios.com.ar/server/): A computer whose job is to answer requests from other computers, all day, every day.