A data warehouse is a central repository of information that can be analyzed to make more informed decisions. Data flows into a data warehouse from transactional systems, relational databases, and other sources, typically on a regular cadence. Business analysts, data engineers, data scientists, and decision makers access the data through business intelligence (BI) tools.

What are the benefits of using a data warehouse?
Informed decision making
Consolidated data from many sources
Historical data analysis
Data quality, consistency, and accuracy
Separation of analytics processing from transactional databases, which improves performance of both systems

A data lake is a centralized repository that allows you to store all your structured and unstructured data at any scale. You can store your data as-is, without having to first structure the data, and run different types of analytics—from dashboards and visualizations to big data processing, real-time analytics, and machine learning to guide better decisions.

Why do you need a data lake?
Organizations that successfully generate business value from their data, will outperform their peers. An Aberdeen survey saw organizations who implemented a Data Lake outperforming similar companies by 9% in organic revenue growth. These leaders were able to do new types of analytics like machine learning over new sources like log files, data from click-streams, social media, and internet connected devices stored in the data lake. This helped them to identify, and act upon opportunities for business growth faster by attracting and retaining customers, boosting productivity, proactively maintaining devices, and making informed decisions.

Problem: Unable to get executive level business insights to monitor Magento/Marketo's Combined business performance
Solution: Centralize Enterprise Data Objects from across the customer Journey to organize as different layers serving the needs for different personas

Collection of centrally managed, governed, and monitored reusable business data objects built by co-development teams to support N number of insights and experiences. This centralized layer spans all enterprise business domains and comprises multiple layers to serve different business personas and diverse business requirements.
A collection of raw data objects from various source systems supporting the E2E enterprise business in a standardized, secured and organized manner with consistent SLAs, defined stewards, and operational monitors developed by enablement teams that serve as the foundational layer. These data objects will serve as the building blocks for Layer 2, Layer 3, and Layer 4.