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The goal is to design and build a data warehouse / business intelligence (BI) architecture that provides a flexible, multi-faceted analytical ecosystem for each unique organization.
A traditional BI architecture has analytical processing first pass through a data warehouse.
In the new, modern BI architecture, data reaches users through a multiplicity of organization data structures, each tailored to the type of content it contains and the type of user who wants to consume it.
The data revolution (big and small data sets) provides significant improvements. New tools like Hadoop allow organizations to cost-effectively consume and analyze large volumes of semi-structured data. In addition, it complements traditional top-down data delivery methods with more flexible, bottom-up approaches that promote predictive or exploration analytics and rapid application development.
In the above diagram, the objects in blue represent traditional data architecture. Objects in pink represent the new modern BI architecture, which includes Hadoop, NoSQL databases, high-performance analytical engines (e.g. analytical appliances, MPP databases, in-memory databases), and interactive, in-memory visualization tools.
Most source data now flows through Hadoop, which primarily acts as a staging area and online archive. This is especially true for semi-structured data, such as log files and machine-generated data, but also for some structured data that cannot be cost-effectively stored and processed in SQL engines (e.g. call center records).
From Hadoop, data is fed into a data warehousing hub, which often distributes data to downstream systems, such as data marts, operational data stores, and analytical sandboxes of various types, where users can query the data using familiar SQL-based reporting and analysis tools.
Today, data scientists analyze raw data inside Hadoop by writing MapReduce programs in Java and other languages. In the future, users will be able to query and process Hadoop data using familiar SQL-based data integration and query tools.
The modern BI architecture can analyze large volumes and new sources of data and is a significantly better platform for data alignment, consistency and flexible predictive analytics.
Thus, the new BI architecture provides a modern analytical ecosystem featuring both top-down and bottom-up data flows that meet all requirements for reporting and analysis.