Age of Big Data, how to construct the data warehouse for enterprises ?

Updated: Feb 14

In the era of information, it mainly focused on the data. That the competition enterprises largely depended on the competition between datum. As the central system of the big data cluster, the data warehouse provides and establishes the decision support and executes the information system tools, which play an important role in the enterprise’s development.

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How to achieve the unified management of mass data?

The progress in the computer and network contribute to increasing the appearance of the various powerfully functional hardware, apps, and platforms, which can collect, manage and distribute mass data. In the business application, the communication relating to products and services often refers to complex business solving. These don’t be limited to the business, but also concerning the government, the health and fitness, insurance, manufacturing, finance, sales and distribution and education, and so on. Due to the large quantity and various sources of data, when enterprises construct the management information system, they might inevitably face problems. That they have no idea how to manage these mass data and how to extract some useful information. At that time, it is obvious that constructing enterprise data warehouses is especially significant.

The data warehouse has the biggest advantage. That it can gather together the business data, being from different information islands in enterprise networks, store them in a single integrated database, and provide various means for the statistic and analysis of the data. For example, the application scope of data warehouses in commercial banks includes deposit analysis, loan analysis, customer market analysis, relevant financial industry analysis and decision-making (securities, foreign exchange trading), risk prediction, benefits analysis, etc. The data warehouse will provide standardized and integrated historical data for some downstream information systems such as customer relationship systems, management committee systems, and supervisory reporting systems, etc. From the above cases, it can be seen that the data warehouse as an enterprise-level data application, having been with the upstream transaction system and the downstream information system, is the center of enterprise big data. So how to build an enterprise data warehouse?

The Method of Implementing the Data warehouse

At present, there are two main ways in the actual application of data warehouse technology, such as building a data warehouse on a relational database (ROLAP) or a data warehouse on a multidimensional database (MOLAP).

The MOLAP solution uses a multidimensional manner to organize and store data; while the ROLAP solution holds a two-dimensional relational table as the core to express the multidimensional concept. Dividing the multidimensional structure into two types of tables, including dimension tables and fact tables, can make the relational structure better adapt to the representation and storage of multidimensional data. In terms of the expression of the multidimensional data model, the multidimensional matrix is clearer than the relational table and occupies less storage, while searching the ROLAP system of data through the connection between the relational tables, the system performance becomes the biggest problem. The MOLAP solution is more concise than the ROLAP solution. It can be automatically carried out and manage its index and data aggregation, but it has slightly poorer flexibility; while it is more complicated to implement the ROLAP solution, but it has relatively great flexibility. That means users can dynamically define statistics and calculation methods, and also protect the investment in existing relational databases.

Since the two schemes both have their own advantages and disadvantages, in practical applications, MOLAP and ROLAP are often used in their combination. That is a hybrid model. It will use relational databases to store historical data, detailed data, or non-numerical data, which will take advantage of the mature techniques of the relational database to reduce costs; while in the multidimensional data, it will store current data and common statistical data to improve the operational performance.

Since massive data have been accumulated in the currently running OLTP system, how to extract useful information needed for decision-making has become the most urgent need for users. Although the newly-built data warehouse can provide a complete solution in terms of functions and performance, it requires a lot of manpower and material resources. Moreover, constructing the data warehouse and accumulating the analytical data require a certain amount of time, which cannot meet users’ urgent needs for the information analysis in time. Therefore, in the early stage of preparation, some suitable tools can be adapted to establish a logical data warehouse system under the original OLTP system.

Which core technologies do OLAP data warehouses need?

It mainly includes the following ones, such as distributed execution framework, VPP user-mode TCP protocol, support >1000 servers, 10,000-level CPU core parallel computing. While and the multi-threaded parallel algorithm to achieve parallel execution within core operators; support for many cores (>64 cores), NUMA Architecture optimization; SIMD + vectorization engine, one instruction executes a batch of data operations; supports X86, ARM instructions; LLVM compilation and execution, pre-compiling hot functions into machine code, reducing the number of SQL execution instructions, and improving performance.

In terms of data processing: high-accuracy data processing, total score reconciliation, and other demanding application scenarios; high timeliness requirements, large data volume, complex SQL, cross-table join batch processing tasks; fixed time period fixed mode statistical analysis Report tasks; interactive query tasks with low latency and quick response under large data volume, complex query conditions.

If you master the above core technologies, you can help enterprises construct a center of big data, data warehouse. That will improve their ability to insight into operational trends, the accuracy of predictions and plans, and help companies achieve greater business value.

In the Huawei Big Data Certification Course (HCIP Big Data), students will learn the development content from the FusionInsight GaussDB200, an enterprise-level large-scale parallel processing distributed data warehouse platform, including database design and development of the database, and application development and  SQL standards, as well as other operations and maintenance content, such as database clusters management, security management, concurrency control, load balancing management, database tuning, and database performance monitoring. All of these will motivate us to build data warehouses for enterprises.

Do you want to gain the ability to build data warehouses? The HCIP Big Data certificate can assist you in easily master the skills and be competent for related positions, which refer to data warehouse development, big data analysis, database operation, and maintenance, and DBA.

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