TDP is based on proven, reliable and scalable components from the Hadoop eco-system.
End-to-end architectures include data ingestion, transformation, processing, and storage in a batch-oriented manner, enabling the flow of data from various sources to target systems for analysis and reporting purposes.
End-to-end architectures facilitate the real-time collection, processing, and analysis of streaming data, enabling immediate insights, rapid decision-making, and proactive actions based on the continuous flow of information.
Data engineers uses TDP and distributed engines such as Spark to efficiently process and analyze large volumes of diverse data, enabling the extraction of valuable insights and the development of data-driven solutions.
Data scientists utilize TDP to leverage its distributed computing capabilities and scalable storage infrastructure for handling big data, enabling them to train machine learning models with large datasets and extract features.
Data analysts leverage TDP to efficiently process and analyze large volumes of structured and unstructured data, enabling them to uncover meaningful patterns, trends, and insights that drive data-informed decision-making within organizations.
Chief data officers, data stewards and other people in charge of data governance utilize TDP to ensure the quality, integrity, and governance of data by implementing data management policies, data lineage tracking, and metadata management, thereby facilitating effective data governance and compliance within an organization.