Big data architecture is the overall structure that represents the logical and physical systems of big data.
This article is part of a full discussion of big data in this article. Want to know more about big data in detail? Please read this article:
Advantages of big data architecture
To reap the benefits of big data, it is critical to invest in a big data infrastructure capable of handling large volumes of data. Companies can invest in Bigbox infrastructure solutions and big data platforms to reap the benefits of big data or big data like
- 1. Improving the quality of understanding and analyzing big data
- 2. Make better and faster decisions
- 3. Reduce the company’s operating costs by analyzing the company’s big data to find things that can be improved and saved.
- 4. Anticipating future needs and trends
- 5. Encouraging companies to be able to set standards within the company
- 6. Can provide proven ways to apply the best technology to solve problems
Challenges in Big Data Architecture / Big Data Architecture Challenges
In building a big data architecture, there will certainly be various challenges, including:
- Ensuring that the architecture can meet the company’s needs
- Anticipating big data needs, even if it gets bigger and more complex, the big data architecture can still manage or the big data architecture can be easily upgraded/scalable.
If it is not good when designing big data architecture / big data architecture can cause significant costs, unstable performance or insufficient need to learn more, you can read the article: Big data problems, challenges and solutions.
Big data architecture layers
This layer in Big Data Architecture Layers / Big Data Architecture consists of several layers
1. Big Data Source Layers / Big Data Layers
Big data can process batch processing or real-time processing from big data sources such as data warehouses, relational databases, non-relational databases, IoT devices, and from various other sources.
2. Management and storage layers / Big data storage and management
This layer receives data from the big data source layers and converts the data into a format that can be understood and processed by data analysis tools / data analysis tools and stored according to the data format.
3. Analysis layer
In the analysis layer, analytical tools extract data from the big data storage layer
4. Consumption layer
The consumption layer receives the analysis results from the big data analysis layer and provides the analysis to the business intelligence layer.
Big data architecture processes
1. Connect to these data sources
“Connectors” and “adapters” are platforms, software or features that can connect to any data format and can also connect to different storage systems, protocols and networks. . This feature is much needed when implementing big data to make data retrieval and loading easier. In big data platforms that do not have end-to-end solutions, this is usually done by data engineers.
2. Data governance
The task of the big data management process is to ensure that the data processing, analysis, storage and deletion of the data used comply with data privacy and security.
3. Systems management
The entire process should be continuously monitored through central management consoles
4. Maintaining the quality of service
Big data should be maintained for service quality by establishing a service quality framework, starting with defining data quality, compliance policies, and the frequency and amount of data to be processed in big data.
How to build a big data architecture
To build a big data architecture, several procedural steps are required, which are:
1. Problem analysis
Of course, the first step in building a big data architecture is to first analyze the problem or understand what problems or goals the company wants to achieve? Common things to consider are data changes, data processing speed, and problems the system/platform is facing at the time.
Common use cases that occur in companies include:
- Perform data archiving
- Offload processing
- Implementation of data lakes
- Unstructured data
- Modernization of the current data warehouse
2. Choose the seller
There are many big data solutions in the market now, such as Microsoft, AWS, Hortonworks and Bigbox, choose one that can solve your company’s big data problems.
3. Deploying the deployment strategy
can be done on-premises, cloud-based or hybrid, it is recommended to choose a server solution in Indonesia to comply with Indonesian data governance, you can look at NeuCentrix cloud solutions located in Indonesia.
4. Capacity planning
In the construction of a big data architecture, of course, one must plan the built big data capacity, determine the hardware, infrastructure size, the daily amount of data to be processed, the amount of data by looking at the historical data loading in the previous month or maintenance, the maintenance period Data / application data storage, multiple data deployment, etc
5. The size of the infrastructure
This infrastructure sizing step is based on capacity planning, determining the number of clusters and type of hardware needed, as well as the type of disk, number of disks per device, type of processing memory, amount of memory, number of CPUs and cores, and where data will be stored.
6. Disaster recovery
Planning in the construction of big data, of course, it is necessary to consider backup and disaster recovery planning, storage of critical data, backup reversal, deployment of multiple data centers, and the active-active or active-passive disaster recovery method. Choose the right one. for the company