The enormous volumes of data created and maintained by industries outgrew its infrastructure capabilities and is increasingly dependent on the extensive use of big data architectures, due to the heterogeneous nature of the data generated by modern high‐throughput instrumentation technologies stored on premisses.
Heterogeneous data are any data with high variability of data types and formats. They are possibly ambiguous and low quality due to missing values, high data redundancy, and untruthfulness. It is difficult to integrate heterogeneous data to meet the business information demands.
The data integration and data interoperability are complex challenges for the organizations deploying big data architectures due to the heterogeneous nature of the data, which tend to implement advancements in their workflow and influence the organization’s performance.
Big data relates large-volume and complex datasets with multiple independent sources, in itself is worthless and requires data analysis to retrieve or acquire intelligence from the data and help in decision-making. Big Data processes or pipeline of extracting insights can be divided in two sub-processes: data management and data analytics. Data management and data analytics can be troublesome because it often involves the heterogeneous collection and storage of mixed data based on different patterns or rules.
Big Data Management provides support for big content and high stream throughput in all the components in the automated pipeline. It allows data to be stored across multiple regions, it scales infinitely to petabytes and beyond, and it offers customizable metadata to aid with retrieving files.
Data Integration – Achieve fast, flexible, and repeatable data integration and ingestion at scale.
Data Preparation – Intelligently find and prepare trusted data for your analytics and AI/ML projects.
Data Quality – Delivers fit-for-purpose big data, with a scalable, role based data quality environment.
Data Masking – Protects unauthorized access to and disclosure of sensitive, private, and confidential information.
Data Catalog – Discover and inventory data assets across your organization.
Cloud Mass Ingestion – Efficiently ingest streaming data and move it to other targets for real-time analytics.
Data Streaming – Power real-time analytics with code-less IoT and stream processing.