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Big Data Platforms

Taipei_Taiwan_052322A
[Taipei, Taiwan]

 

- 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.

 

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.

 

 

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