The document discusses the characteristics and challenges of data platforms including data warehouses and data lakes. It highlights the fragmentation and silos that can occur within organizations, leading to significant integration efforts needed to create a holistic solution. Additionally, it stresses that integration work can consume a substantial portion of the time and cost associated with building a digital platform.

1. Modern Data Platform
What does your organizations data platform look like?
Most of you have a data warehouse with the following
characteristics.
Many of you may also have data lakes with data across
multiple subject areas. It contains
data of mixed value or perhaps potentially unknown value.
Great platform for
experimentation, quickly integrating new sources. Simply
load files into an object storage, no
schema, schema on read and then start analyzing it
immediately.
Not necessary, your data is scattered between these two
independent platforms.
Data Warehouse, A great platform for fast query
performance with large number of
concurrent users. And Data Lake which is great for data
scientists and have acceptable
performance.
Are these two areas stove piped in your organization?
• Platforms are owned by different teams
• Address specific and limited problem statements, in Silos
• Require major Integration efforts for holistic solution

2. Silos?
Integrating Fragmented Databases Challenges
• Development & Management
• Data Consistency & Security
• Availability & Scalability
• Cross Site Consistency
Data Warehouse
• Curated, trusted data
• High concurrency
• Schema on write
• Subject area
• High Performance
Data Lake
• Data of mixed value
• Lower concurrency
• Schema on read
• Multiple subject areas
• Acceptable
Performance
Integration work will account for
50% of the time & cost
of building a digital platform