A Big Data Fairy Tale

Meet Alice

Alice has a Store

Alice's store collects sales data

Alice wants to use her sales data to run a promotion

So Alice loads up her sales data in her trusty database/hadoop/spark/etc... server.

... asks her question ...

... and basks in the limitless possibilities of big data.

Why is this a fairy tale?

It's never this easy...

CSV Import

Run a SELECT on a raw CSV File

• File may not have column headers
• CSV does not provide "types"
• Lines may be missing fields
• Fields may be mistyped (typo, missing comma)
• Comment text can be inlined into the file

State of the art: External Table Defn + "Manually" edit CSV

Merge Two Datasets

UNION two data sources

• Schema matching
• Deduplication
• Format alignment (GIS coordinates, $ vs €)
• Precision alignment (State vs County)

State of the art: Manually map schema

JSON Shredding

Run a SELECT on JSON or a Doc Store

• Separating fields and record sets:
  (e.g., { A: "Bob", B: "Alice" })
• Missing fields (Records with no 'address')
• Type alignment (Records with 'address' as an array)
• Schema matching$^2$

State of the art: DataGuide, Wrangler, etc...

Data Cleaning is Hard!

State of the Art

Alice spends weeks cleaning her data before using it.

Newer State of the Art