An Overview of Big Data Architecture
As the growth of technology is increasing, most industries are upgrading their service. Big data architecture is very important for these industries as they can save records by using this system.
The data revolution has changed our digital world. The growth has started in the early 2000s as data scientists are trying to generate more data in a smaller space. This is why they designed DBMS (database management system). It has become cost and time-efficient.
In this article, we are covering what is big data architecture, layers, best practices, and how to build one. Hence, you will get a complete overview by reading this article. Let’s get started.
What is Big Data Architecture?
In simple words, big data architecture works as the foundation of big data analytics. Data scientists use this overarching system to manage a large number of data. It can provide an environment where people can store vital business information. The architecture includes four big data layers.
[Read more: Big Data in Healthcare – Everything You Need to Know]
Big Data Architecture Layers
As we mentioned above, big data architecture has four logical layers. Let’s find out how these layers work in the following:
This environment manages both real-time and batch processing of big data sources. It can process relational database management systems, IoT devices, and data warehouses.
Management & Storage Layer
This layer receives data from various sources. Moreover, it converts data to make it compatible with analytics tools. The management and storage layer also store the data as per its format.
The consumption layer can receive results from the analytics tools. Plus, it will present the result to the pertinent output layer.
This layer is also essential for extracting business intelligence from the storage layer.
[ Read more: How Can Small Businesses Leverage Big Data?]
Big Data Architecture Best Practices
Before you design database architecture, you need to understand the value of this system. Moreover, you also need to understand how to use the data for your business. This is why you need to implement the following big data architecture principles:
Your company’s big data project should understand the value and vision of your business. On the other hand, it needs to understand architecture principles, framework, and work requirements. In some cases, big data reference architecture should have a good understanding of the business landscapes.
Big Data API
Checking data service API is also essential to practice before choosing a database solution. Make sure you are checking if the database solution has standard query language. Also, understand how to connect the database, the scalability, and security mechanisms.
User Interface Service
An ideal big data architecture should be customizable. This means the database should be accessible for the cloud and people can use the dashboards. So, before you are choosing a database, make sure you are checking the user interface service.
Before a database is designed, you need to consider the data sources. It’s essential because the database can normalize the data to a common format. Moreover, you need to consider this practice as it can take care of both structured and unstructured data.
How to Build a Big Data Architecture
To design big data reference architecture, you need to follow some crucial steps. Let’s find out how to design an architecture:
The foremost thing you need to do is analyze the problem. A business can have various big data problems such as data velocity, variety, and challenges. Moreover, your business can face other problems with the current system including data warehouse modernization, data lake implementation, data archival, and unstructured data processing.
2. Select a Vendor
The next step of building a big data architecture is very crucial. If you are a beginner, it would be good if you are choosing Hadoop. This is one of the best big data reference architecture tools. So, you can easily manage your company’s big data. Hadoop manages data of some popular companies such as Hortonworks, Microsoft, Amazon Web Services, and Mapr.
3. Capacity Planning
Capacity planning is another crucial step when you are designing architecture. Before designing, make sure you are considering data volume and daily ingestion volume. Moreover, you need to consider multi-data center deployment and data retention period as well. Plus, make sure you are also considering the time period.
4. Disaster Recovery Planning
Last but not least, disaster recovery planning is essential for every database. Your company needs a backup plan for critical data storage. On the other hand, you need to consider some other things such as multi-datacenter deployment, backup interval, and more.
Finally, you know about how to build a big data architecture, layers, and best practices. You see, this architecture could be very helpful, especially for business information. If you want this system for your company, make sure you are contacting top data scientists. For more information, you can start your research.