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Data warehouses have a long history, and the basic idea here is that most enterprises build a variety of applications to automate their basic business processes, which evolve to produce multiple versions of data. In the past, data inconsistency was a headache for those who wanted to know about sales revenue or profit margins in different product lines or regions.

In essence, the original concept of data warehouse is to copy key data from various transaction systems, solve inconsistencies and generate clean data sets that are easy to analyze. This process is usually done using programs that run regularly that keep the data warehouse up-to-date as new data is ingested.

History of data Warehouse Environment

To ensure the proper operation of the data warehouse environment, many components are needed here. It requires a data extractor, and the data warehouse must be designed using its own architecture. Modern data warehouses also need other programs to determine the hierarchy of the main version of the data by parsing competitive versions of the background data through business rules.

The historic challenge here is that data warehouses are similar to buildings built on changing underlying operating systems. When major changes take place in these systems (such as restructuring or business acquisitions), the structure of the data warehouse needs to be changed to reflect underlying business changes. If the pace of business change is too fast, the data warehouse may become unreliable for a period of time, undermining business people's confidence in it.

In order to solve this problem, the data Mart came into being. However, unless data marts are synchronized with the data in the data warehouse, they may compete with them and produce multiple versions of the data. To address this instability, a variety of data warehouse designs have begun to emerge, including star pattern, snowflake pattern, and other patterns advocated by technical experts Bill Inmon and Ralph Kimball.

Then the field of master data management began to emerge, and enterprises want to collect more and more complex business background data sets, which usually have independent databases that can work with data warehouses. Competing for different versions of the product hierarchy requires business investment, which enables data governance in the data warehouse to provide processes for business control of this type of master data.

In addition to the huge amount of data, the increased complexity is also a problem. Eventually we begin to see more complex query and analysis tools that usually require their own metadata layer to represent the business view of the data warehouse.

At the same time, extracting, transforming and loading (ETL) data spawned the development of the data integration tool industry. These tools automate the process and have their own proprietary scripts to add other components to the data warehouse environment that need to be processed.

Modernization of data warehouse

For years, people have been trying to organize the components of the enterprise data warehouse environment. To modernize increasingly complex data warehouses, vendors try to produce pre-built templates and data warehouse generators, such as Idera, Magnitude, and Attunity. Despite success in some use cases, none of these have achieved market dominance.

In addition, DevOps and DataOps are committed to helping the data warehouse schema evolve, as well as other aspects of making the data warehouse environment operate in a controlled manner.

Despite the great efforts made by innovative suppliers, there are no shortcuts to the modernization of data warehouses.

Large enterprises have invested a lot of money in enterprise data warehouses and related environments, but large processes, procedures, scripts, and patterns are still major obstacles to progress. Another obstacle is to overcome the inertia of the current practices of database administrators and IT employees.

Because most of the enterprise's analysis depends on the data warehouse, it is difficult to migrate. Reorganizing the operational data warehouse environment is like a mechanic trying to upgrade an engine for a moving car. Nevertheless, data warehouse automation tools and modern DataOps markets are doing their best to help enterprises modernize their data warehouse environment.

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