Introduction
Data Lakehouse process data at scale, fast, with visual reporting, exploratory analysis, and with point-and-click accessibility; supporting every business user – not just data scientists or IT staff.
Accelerate an organization’s journey towards advanced analytics by enabling value-added data innovation and exploration.
Data Challenges
Modern challenges with data are major bottlenecks
Limited scalability, with the need to serve unlimited volumes of data (Big Data)
Siloed Data, not easily available for cross-function reporting
Issues with connecting new sources
Lack of technical foundations to support advanced analytics
Inefficient advanced analytics
Lack of flexibility of the existing Data Warehouse ecosystem
Oversaturated with Data – Data Swamp
Poor-quality data caused by master data adjustment challenges
Our Offer
Properly Engineered Data Lakehouse Deliver Clear Value
Scenarios of Data Lakehouse Usage
Data Lake as a Central Repository
(Incl. Data replication)
1
Data Science Lab
(Incl. Data replication)
2
Offload
(Incl. Data replication)
3
Data Lake as a Central Repository
(Incl. Data replication)
4
Data Lake as a Central Repository
(Incl. Data replication)
5
