> For the complete documentation index, see [llms.txt](https://gchandra.gitbook.io/big-data-and-tools-with-nosql/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://gchandra.gitbook.io/big-data-and-tools-with-nosql/data-format/storage-formats.md).
# Storage Formats
| Account number | Last name | First name | Purchase (in dollars) |
| -------------- | --------- | ---------- | --------------------- |
| 1001 | Green | Rachel | 20.12 |
| 1002 | Geller | Ross | 12.25 |
| 1003 | Bing | Chandler | 45.25 |
### Row Oriented Storage
In a row-oriented DBMS, the data would be stored as
1001,Green,Rachel,20.12;1002,Geller,Ross,12.25;1003,Bing,Chandler,45.25
Best suited for OLTP - Transaction data.
### Columnar Oriented Storage
1001,1002,1003;Green,Geller,Bing;Rachel,Ross,Chandler;20.12,12.25,45.25
Best suited for OLAP - Analytical data.
1. **Compression**: Since the data in a column tends to be of the same type (e.g., all integers, all strings), and often similar values, it can be compressed much more effectively than row-based data.
2. **Query Performance**: Queries that only access a subset of columns can read just the data they need, reducing disk I/O and significantly speeding up query execution.
3. **Analytic Processing**: Columnar storage is well-suited for analytical queries and data warehousing, which often involve complex calculations over large amounts of data. Since these queries often only affect a subset of the columns in a table, columnar storage can lead to significant performance improvements.
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