Big Data Architecture and Characteristics

- Overview

Big data architecture is the logical and physical structure that manages how data is ingested, processed, stored, managed, and accessed. It's designed to handle large or complex data sets that are too large for traditional database systems. 

Big data architecture typically includes four layers: 

• Data collection and ingestion
• Data processing and analysis
• Data visualization and reporting
• Data governance and security

Each layer has its own set of technologies, tools, and processes. 

Big data is often defined by three Vs: greater variety, volumes, and velocity. The five main characteristics of big data are: Velocity, Volume, Value, Variety, Veracity. 

Big data solutions typically involve one or more of the following types of workload: 

• Batch processing of big data sources at rest
• Stream processing system
• High-performance computing (HPC)

- Big Data Architecture

The term "big data architecture" refers to the systems and software used to manage big data. A big data architecture must be able to handle the scale, complexity, and variety of big data. It must also be able to support the needs of different users, who may want to access and analyze data in different ways. A big data architecture must support all of these activities so that users can use big data effectively. It includes organizational structures and processes for managing data.

Big data architecture is a comprehensive solution for processing massive amounts of data. It details the blueprint for providing solutions and infrastructure for handling big data according to the company's needs. It clearly defines the components, layers and methods of communication. The reference point is the ingestion, processing, storage, management, access and analysis of data.

Some Big Data Architecture examples include - Azure Big Data Architecture, Hadoop Big Data Architecture, and Spark Architecture in Big Data.

- Big Data Platforms

A big data platform acts as an organized storage medium for large amounts of data. Big data platforms utilize a combination of data management hardware and software tools to store aggregated datasets, usually in the cloud. 

A big data platform is a type of IT solution that combines the features and capabilities of several big data application and utilities within a single solution. It is an enterprise class IT platform that enables organization in developing, deploying, operating and managing a big data infrastructure /environment. 

A big data platform includes data collection, preparation, analysis and reporting tools. The traditional definition of big data has changed over the years, and it is critical to understand it before harnessing it. Efficient scalability is one of the main requirements and considerations when choosing a platform. 

Gaining operational insights may be the goal of big data, but don't overlook data processing, mining, and cleansing operations that take up more than 80 percent of a company's time and resources. 

Building your personalized business needs can be time-consuming, but critical to software selection. Machine learning (ML) and natural language processing (NLP) are the future of big data analytics. 

A big data platform generally consists of big data storage, servers, database, big data management, business intelligence and other big data management utilities. It also supports custom development, querying and integration with other systems. 

The primary benefit behind a big data platform is to reduce the complexity of multiple vendors/ solutions into a one cohesive solution. Big data platform are also delivered through cloud where the provider provides an all inclusive big data solutions and services.

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- A Modern Data and AI Platform - Power Digital Transformation

Data drives digital transformation, and most businesses have increased revenue due to the adoption of AI. However, many people still struggle to infuse AI at scale in their organizations. Complex data environments limit agility, while data silos and inconsistent datasets hinder AI implementation. 

We live in the age of data. We have access to more data than ever before. We use it in many ways. From analyzing and understanding customer behavior to gathering insights for software QA companies, organizations of all kinds use large datasets every day. 

A true data and AI platform should eliminate data silos and allow you to process data without moving it, regardless of its type, structure, or origin. When choosing a data and AI platform, look for platforms that can query across multiple data sources without duplicating and duplicating data. This query capability helps reduce costs and simplifies your analysis, making it more up-to-date and accurate because you can access up-to-date data at the source. 

In particular, a platform that can bring together all data should include integrated solutions for databases, data warehouses, and data lakes. Its database should employ high-performance and scalable transaction processing with query optimization. Its data warehouse should be able to perform analytics across local environments. Regardless of the volume of data, its data lake should be able to help you store and query structured and unstructured data.