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.

Big Data Architecture’s advantages

1. Using parallel processing to increase performance

Big data architectures use parallel computing, in which multiple multiprocessor servers carry out calculations simultaneously, to process enormous data volumes quickly. Large problems are divided into smaller components that can be tackled at the same time.

2. Flexibility in scaling

Big Data architectures allow the environment to be scaled horizontally to accommodate the magnitude of each application. Big Data solutions are typically operated in the cloud, where you only pay for the computing and storage resources that you really utilise.

3. Individual discretion

For use in Big Data architectures, the industry offers a variety of platforms and solutions, including Azure managed services, MongoDB Atlas, and Apache technologies. Combining solutions will yield the the most appropriate choice for your diverse workloads, installed systems, and IT skill sets.

4. Compatibility with associated systems

By utilising Big Data architectural components for IoT processing, BI, and analytics processes, you may construct integrated platforms across many types of workloads.

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:

Sources Layer

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.

Consumption Layer

The consumption layer can receive results from the analytics tools. Plus, it will present the result to the pertinent output layer.

Analysis Layer

This layer is also essential for extracting business intelligence from the storage layer.

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:

Preliminary Step

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.

Data Sources

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:

1. Analyze

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.

Conclusion

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.

FAQ’S

What three categories of big data exist?

Structured data, unstructured data, and semi-structured data are the three categories into which big data is separated.

What are big data’s four characteristics?

Volume, velocity, diversity, and veracity are the four traits that most commonly characterise big data today.

What is a big data example?

Big data is derived from a variety of sources, including customer databases, transaction processing systems, documents, emails, medical records, clickstream logs on the internet, mobile apps, and social networks.