The document discusses the key differences between data lakes and data warehouses. It provides examples of how a retail company can use a data lake to store various structured and unstructured data sources together. This allows data scientists to more easily combine different data types into models to forecast product demand and for marketing experts to analyze sentiment and determine sales focus. The document also lists some SAP tools for administering and working with data in a data lake, including the SAP HANA Database Explorer, SAP HANA Cockpit, and SAP HANA Cloud Central.

1. By
Lakshmi Sankaran
Research Scholar @ Christ
SAP Architect @ Pythian

4. Characteristics Data Lake Data Warehouse
Data Any data type from any source Relational or structured
Schema Schema-on-read (time of analysis) Schema-on-write (predefined)
Storage costs Lower cost – petabyte scale Higher cost – terabyte scale
Data quality Curated or non-curated data Curated data
Users
Data scientists, data developers (using Python, for example), and business analysts
(using SQL for curated data)
Business analysts using SQL
Analytics Machine learning, predictive analytics, data discovery/profiling
Batch reporting, BI,
visualizations

7. Here are two examples of a data lake use case in retail.
Long term sales data is stored in a data lake alongside unstructured data like
Web site clickstreams, weather, news, and micro/macroeconomic
data. Having this data stored together and accessible makes it easier for a data
scientist to combine these different sources of information into a model that will
forecast demand for a specific product or line of products. This information is
then used as inputs to the retail ERP system to drive increased or decreased
production plans.
In parallel, a marketing expert may access this same data lake and look at a
sentiment analysis of the Web site and social media engagement with news,
macroeconomic, and sales history data to determine which products to focus
on and how best to maximize sales, profit and/or adoption.
Analytics for Retail
1. Web shop
2. Store Management
3. POS Inventory
4. Demand Planning
https://insights.sap.com/what-is-a-data-lake/
https://saphanajourney.com/data-analytics-industry-retail/?highlight-term=

11. SAP Teched Data Lake Session for 40 minutes
Demo 18->35 minutes
Administration: SAP HANA Cloud User Interfaces
1. SAP HANA Database Explorer,
2. SAP HANA Cockpit, and
3. SAP HANA Cloud Central
18:21
19::03
23:08