6 Tips for Creating Effective Models for Big Data Database Management

Big Data Database Management

Big data is wider and less predictable than the traditional data sets, so it requires very special consideration while building database models. Irrespective of the business size data is an important aspect. In the current times, no one can do without it. So, it is vital to have proper knowledge about how to use it effectively. This article discusses some essentials to keep in mind while you are planning for big data stores.

Data modeling principles and practices are also very complex, which involves methodologies of organizing and processing corporate data to fit it to the need of business applications and processes. Handling big data stores also require the design to maintain logical relationships so the date can be easily interrelated with each other for easy retrieval. Logical big data designs are further translated into easy physical models, consisting of the storage devices, files, and databases that house the data.

Historically, all types of businesses have used RDBMS technologies like SQL to develop various data models suited for linking dataset keys flexibly to the data types to support informational data management needs for business applications and processes. Unfortunately, big data, which comprises a large volume of data to manage, does not run effectively on relational databases. It runs ideally on non-relational DBs as NoSQL databases. However, this is taken wrongly by many as the big data stores do not need a model at all. The fact is that big data management requires a better data model. Let us review it further.

Tips for Modeling Big Data Stores:

1. Do not push the traditional data models for big data

The traditional data models of fixed data records are very stable and predictable in terms of growth. These key features make it very easy to model traditional DB stores. However, big data follows an exponential growth model, which is very rapid and unpredictable. Also, the nature of data is widely diversified with a myriad of sources and forms. When the sites contemplate big data modeling, the efforts for the same should center around constructing elastic and open data interfaces as you may never know when the new data source or data forms may emerge. However, this does not come as a priority in the traditional data models with fixed records.

2. Do not just design schemas; try for a complete system

In traditional database systems, a standard relational database schema is used to cover almost all relationships and the links between inter-related data as required by the business for information support. However, this is not the case with big data. It may not have a set database structure or may use the new-gen databases like NoSQL or NewSQL, which do not require any database schema.

For this reason, as suggested by RemoteDBA, big data has to be built on specific systems and not on databases. System components which big data models contain are primarily business information sets, corporate governance, and security. Physical storage systems are used for data storing, integration of all types of data. The system also should have the capability to handle different types of data.

3. Check out for the big data modeling tools

There are many data modeling tools available for commercial data models that can support Hadoop and big data reporting software like Tableau. While considering big data models and methodologies, IT decision-makers need to consider building various data models for big data stores as the major requirement. You can find plenty of tools around, but not all of them are the same. Consider the pros and cons of each to understand the benefits in light of your requirements in hand to further use them.

4. Focus on core business data

Big data tend to pour in huge volumes each day to the enterprise big data stores. As we can see, the majority of this data is largely extraneous. So, it makes no sense to create models that include all the data. The best approach here is to identify big data essential for your enterprise applications and operations and try to model the same. This will give you an upper hand in need-based data modeling, which will boost further growth prospects.

5. Always deliver quality data

A superior data model and relationships may come into good effect for big data if the organizations fully focus on developing solid definitions for the data. This needs to be done with thorough metadata, which describes which decries enough inf as to where the data comes from, its purpose, etc. The more you know about the data in hand, the easier it is for you to properly place in the data model to effectively support your business.

6. Check for the inroads of data

One majorly used vector in terms of big data nowadays is the geographical location. Based on the business you are handling and the industry you represent, there are many common keys to the big data users want to access. The more you identify this common entry point into various aspects of your data, the better you can design a reliable data model that can support the company’s key information access paths.

All these aspects of big data database modeling play a crucial role in meeting your key business database management objectives successfully. When it comes to big data, there are various well-set database systems available out there as both NoSQL and NewSQL. The traditional relational databases model is not ideal for big data management, which usually pumps in at huge volumes and in an unstructured model, which is not digestible to those users.

While planning for big data, you need to be very clear about your purposes, too, as the use and function of big data applications vary largely from industry to industry and from user to user. A thorough baseline analysis will give you a proper insight into what type of data models to be planned and what kind of technological tools and infrastructure must be established and maintained.

Author’s Bio:

Pete Campbell is a social media manager at RemoteDBA.com who has worked as a database administrator in the IT industry and has written numerous articles and blog posts on topics related to DBA services for small businesses. He loves to travel, write and play baseball.

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