This document discusses building a data lake for a restaurant to optimize performance and enable complex queries. It outlines converting the restaurant's data into a big data and columnar format, accessing the data lake, extracting data to S3 for storage, reporting on the data lake, and using big data techniques for complex queries. The SEE team's requirements and a cross-platform analysis strategy are mentioned for inspiration in building the data lake.

1. DATA 411
RESTAURANT

2.  Data Lake
Inspiration
 SEE team
 Adhoc Requirements
 Cross platform Analysis

3. Data Lake
IQ
Iweb
PHChub
AFIX
DATA 411
RESTAURANT

4. Menu
 Convert Data into BigData/Columnar Format
 Access Data in Data lake
 Data Extraction to S3
 Reporting on Data Lake
 Big Data For complex Queries

5. Data lake
Process Flow

6. Summary
Performance optimization using Big data
Problem Statement/Inspiration
Strategy for building a Data lake

7. Thank
You