Database Reference
In-Depth Information
Big Data Platform Imperatives
Technology Capability
Discover, explore, and
navigate Big Data sources
Federated Discovery, Search, and
Navigation
1
Extreme performance-run
analytics closer to data
Massively Parallel Processing
Analytic appliances
2
Manage and analyze
unstructured data
Hadoop File System/MapReduce
Text Analytics
3
4
Analyze data in motion
Stream Computing
Rich library of analytical
functions and tools
In-Database Analytics Libraries
Big Data Visualization
5
Integrate and govern
all data sources
Integration, Data Quality, Security,
Lifecycle Management, MDM, etc
6
Figure 3-1
The Big Data platform manifesto: imperatives and underlying technologies
And don't forgot, the security profile of the underlying data systems needs to be
strictly adhered-to and preserved. These capabilities benefit analysts and data
scientists by helping them to quickly incorporate or discover new data sources in
their analytic applications.
2. Extreme Performance:
Run Analytics Closer to the Data
Traditional architectures decoupled analytical environments from data envi-
ronments. Analytical software would run on its own infrastructure and retrieve
data from back-end data warehouses or other systems to perform complex
analytics. The rationale behind this was that data environments were opti-
mized for faster access to data, but not necessarily for advanced mathematical
computations. Hence, analytics were treated as a distinct workload that had to
be managed in a separate infrastructure. This architecture was expensive to
manage and operate, created data redundancy, and performed poorly with
increasing data volumes.
The analytic architecture of the future needs to run both data processing
and complex analytics on the same platform. It needs to deliver petabyte-
scale performance throughput by seamlessly executing analytic models inside
