Big data architecture: Definition, Challenges, How to Create

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Definition of Big Data Architecture

The term “big data architecture” refers to the overarching framework that symbolizes the various logical and physical components of big data.

Benefit Big Data Architecture

Investing in big data infrastructure that can process massive amounts of data is essential if you want to reap the benefits of big data. The benefits of big data can be realized by businesses through the use of infrastructure solutions and big data platforms.

1.Accurately interpret and analyze big data

2. Make better and faster decisions

3. Reducing the company’s operational costs by analyzing the company’s big data to find out what can be improved and saved

4. Predict future needs and trends

5. Encouraging businesses to establish company-wide standards

6. Can provide a consistent method to apply the best technology to solve problems

Challenges in Big Data Architecture

In creating a big data architecture, of course, you will face various challenges, namely:

1. Ensuring the architecture can meet the needs of the company

2. Predicting big data needs even though they grow bigger and more complex, the big data architecture can still handle it or this big data architecture can be easily upgraded / scalable

If when designing a big data architecture / big data architecture this is not good, it can incur substantial costs, unstable performance or insufficient to learn more, you can read the article: Big Data Problems, Challenges and Solutions.

Big Data Architecture Layers

The layers in the big data architecture / big data architecture layers consist of several layers

1. Big Data Source layers

Big data can process either 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 & Storage layers

This layer receives data from the big data source layer and converts the data into a format that can be understood and processed by data analytic tools and stored according to the data format.

3. Analysis layer

Extraction of information from the big data storage layer is performed by analytical tools in the analysis layer.

4. Consumption layer 

The consumption layer receives analysis results from the Big Data Analysis layer and provides the analysis to the business intelligence layer


Big Data Architecture Processes 

1. Establish connection to Data Sources

“Connectors” and “adapters” are platforms, software or features that are capable of connecting to various data formats and can also connect to a variety of storage systems, protocols and networks. This feature is really needed when implementing big data to make it easier to retrieve and load data. On big data platforms that don’t have end-to-end solutions, this work is usually done by data engineers.

2. Data governance 

The Big Data governance process is responsible for ensuring that the data used complies with data privacy and security starting from processing, analyzing, storing and deleting data.

3. Systems Management

All processes must be continuously monitored through the central management console

4. Maintain Quality of service

Big data must maintain its quality of service by creating a quality of service 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 steps are needed, namely:

1. Analyze the problem

The first step in building a big data architecture is of course to analyze the problem first or look for what problems or goals does the company want to achieve? Things that usually need to be considered are data variations, speed of processing data and problems faced by the system / platform at this time

Common use cases that occur in companies include:

  • Perform data archiving
  • Offload processing
  • Implementation of data lakes
  • Processing data unstructured
  • Modernization of the current data warehouse

2. Choose Vendors

Microsoft, Amazon Web Services (AWS), Hortonworks, are just a few of the big data solutions available today; your business should select the one that best fits its needs.

3. Deployment strategy

Deployment can be done on premises, cloud based or mixed, it is advisable to choose a server solution in Indonesia to comply with Indonesian data governance, you can see Neu Centric cloud solutions which are located across Indonesia.

4. Capacity Planning

In building a big data architecture, of course, it is necessary to consider the big data capacity planning to be built, determine the hardware, infrastructure sizing, the amount of daily data to be processed, the amount of data by looking at the historical data load in the month or earlier, the data retention period / data storage schedule, multiple data deployment and so on

5. Infrastructure sizing

The infrastructure sizing stage is carried out based on capacity planning, determining the number of clusters and the type of hardware needed, also considering the type of disk, the number of disks per machine, the type of processing memory, the amount of memory, the number of CPUs and cores and where the data will be stored.

6. Making a Disaster Recovery Plan

In making big data, of course, it is necessary to do backup and disaster recovery planning, consider where very important data will be stored, backup intervals, multiple data center deployments and choose the active-active or active-passive disaster recovery method that is most appropriate for the company.