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Best Practices of Big Data Architecture

Big data architecture refers to the logical and physical framework that must be implemented in order to ingest, process, and analyze data sets that are either too massive or too complicated to be handled by conventional database management systems. Big data architecture serves as the basis for big data analytics and is required for the following tasks: 

  • Keeping and processing vast quantities of data 
  • Analyzing unstructured data. 
  • The combination of predictive analytics and machine learning 

How Does Architecture for Big Data Work?

Operating big data architectures is an extremely involved and intricate process. Every day, data solutions rely on layers of communication, HPC (high-performance computing) architecture, software, and user interfaces to orchestrate data ingestion, processing, and computation. 

Even though various solutions will have distinct applications and configurations, their underlying architecture will typically consist of a standard set of interoperable layers. 

The following are included in these layers:

Data Layer (Sources of Big Data): This stratum contains all available data for analysis. Possibly the largest component in the system, the data layer may contain multiple information sources for use in the overall architecture. This layer includes data repositories (databases, data lakes, etc.), smart devices and external sensors (in the Internet of Things systems), data-collection platforms, enterprise data systems, and data management systems as data sources. 

Management Layer: This stratum comprises the technologies responsible for the ingestion of data from external sources. Moreover, anything in this layer handles data conversion and formatting from these data sources so that analytics tools can utilize the information. Here, preparation processes may include formatting data for storage, translating unstructured data into a structured format for a database, or incorporating metadata to facilitate organization and utilization. 

Analysis Layer: This layer manages the actual information processing computationally. In this stratum, workload-related information is modeled, evaluated, scored, or otherwise processed. This is also the stratum where data can be utilized for data analytics, machine learning, and artificial intelligence (AI) algorithms.

Consumption Layer: As implied by its name, this is the layer where data is ingested for use. The consumption layer does not refer to the ingestion of data (which occurs at the management layer), but rather the process of producing the results of analysis for use by analytics programs or software platforms for data analysts. Insights and process outcomes can be incorporated into data visualization, process management tools, and real-time analytics at this level.

Within each layer of the big data architecture, several crucial processes traverse layers and structure the solution’s functionality. Among these procedures are the following: 

Connecting to Multiple Data Sources: Big data does not rely on a standardized data source format. Data ingestion must operate across multiple, disparate systems to capture the data required to power solutions such as life science modeling and machine learning. Big data architecture can simultaneously communicate to multiple platforms, devices, or protocols. 

Managing Cloud System: Large infrastructures necessitate the coordination and orchestration of distributed systems, such as cloud server clusters and file systems, as well as system management tools and policies, to ensure that these systems operate effectively and efficiently.

Data Governance: Governance policies center on the procedures to govern data across all systems, including compliance, security, deletion, transfer, storage, management, and processing provisions. 

Quality Assurance: Managing data integrity throughout its voyage in your systems, including anything related to compliance and business operations’ efficacy. 

Big Data Architecture - Best Practices

When purchasing or implementing big data architecture, there are several recommended practices to consider across these various applications: 

Determine Data Sources: You should have a comprehensive list of where, when, and how data will be collected. There is a likelihood that these compilation efforts will concentrate on distinct devices, sources, or technologies that will require their interfaces and administration. 

Customization: Your solution should be tailored to your requirements, not those of the architect. While some architectures are designed for specific applications, many can be engineered to meet the requirements of any organization in conjunction with industry-specific design. 

Numerous kinds of batch processing and storage systems can support a variety of applications. You should inquire with your provider about the type of data management system they can implement (Hadoop, NoSQL, or conventional database management systems). 

Consider Data Volume: Armed with a list of data sources, you should be able to articulate a volume. Your architecture must be capable of accommodating this capacity. You should also be able to construct a capacity plan with your provider to support the scope, scale, and implementation. 

Plan for Disaster and Resilience: Any infrastructure that is dependable and resilient will include data recovery and protection services. Inquire about the backup mechanisms and redundancy plans (hot, cold, and hybrid). 

Nallas Data Engineering Services provides high-performance architecture to organize and perform cutting edge workloads and applications. If you want to know more on how our services can help your business visit our website or directly talk to our experts. 

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