You will find value in bringing the capabilities of the data warehouse and the big data environment together. You need to create a hybrid environment where big data can work hand in hand with the data warehouse.
First it is important to recognize that the data warehouse as it is designed today will not change in the short term.
Therefore, it is more pragmatic to use the data warehouse for what it has been designed to do — provide a well-vetted version of the truth about a topic that the business wants to analyze. The warehouse might include information about a particular company’s product line, its customers, its suppliers, and the details of a year’s worth of transactions.
The information managed in the data warehouse or a departmental data mart has been carefully constructed so that metadata is accurate. With the growth of new web-based information, it is practical and often necessary to analyze this massive amount of data in context with historical data. This is where the hybrid model comes in.
Certain aspects of marrying the data warehouse with big data can be relatively easy. For example, many of the big data sources come from sources that include their own well-designed metadata. Complex e-commerce sites include well-defined data elements. Therefore, when conducting analysis between the warehouse and the big data source, the information management organization is working with two data sets with carefully designed metadata models that have to be rationalized.
Of course, in some situations, the information sources lack explicit metadata. Before an analyst can combine the historical transactional data with the less structured big data, work has to be done. Typically, initial analysis of petabytes of data will reveal interesting patterns that can help predict subtle changes in business or potential solutions to a patient’s diagnosis.
The initial analysis can be completed leveraging tools like MapReduce with the Hadoop distributed file system framework. At this point, you can begin to understand whether it is able to help evaluate the problem being addressed.
In the process of analysis, it is just as important to eliminate unnecessary data as it is to identify data relevant to the business context. When this phase is complete, the remaining data needs to be transformed so that metadata definitions are precise. In this way, when the big data is combined with traditional, historical data from the warehouse, the results will be accurate and meaningful.
The big data integration lynchpin
This process requires a well-defined data integration strategy. While data integration is a critical element of managing big data, it is equally important when creating a hybrid analysis with the data warehouse. In fact, the process of extracting data and transforming it in a hybrid environment is very similar to how this process is executed within a traditional data warehouse.
In the data warehouse, data is extracted from traditional source systems such as CRM or ERP systems. It is critical that elements from these various systems be correctly matched.
Rethink extraction, transformation, and loads for data warehouses
In the data warehouse, you often find a combination of relational database tables, flat files, and nonrelational sources. A well-constructed data warehouse will be architected so that the data is converted into a common format, allowing queries to be processed accurately and consistently. The extracted files must be transformed to match the business rules and processes of the subject area that the data warehouse is designed to analyze.
In other words, the data has to be extracted from the big data sources so that these sources can safely work together and produce meaningful results. In addition, the sources have to be transformed so that they are helpful in analyzing the relationship between the historical data and the more dynamic and real-time data that comes from big data sources.
Loading information in the big data model will be different than what you would expect in a traditional data warehouse. With data warehouses, after data has been codified, it never changes. A typical data warehouse will provide the business with a snapshot of data based on the need to analyze a particular business issue that requires monitoring, such as inventory or sales.
The distributed structure of big data will often lead organizations to first load data into a series of nodes and then perform the extraction and transformation. When creating a hybrid of the traditional data warehouse and the big data environment, the distributed nature of the big data environment can dramatically change the capability of organizations to analyze huge volumes of data in context with the business.
