Advantages of Modern Data Warehouse over Traditional Data Warehouse
Traditional data warehouses are not only relational or dimensional types but also hierarchical and object oriented. Network databases have their own advantages, disadvantages, and purposes from use case to use case. The traditional data warehouse analytics is structured, centralized, and controlled. Analytics is curated, summarized, contained, and predicted, and data movement is batch oriented.
There are modern use case requirements that require a data warehouse to handle the four Vs of data: high data velocity, ingestion of a variety of data, provide veracity, and handle variability of data. Expectations from modern data warehouses are changing and increasing over time. Data movement is done in real time, and there is more automation, performance, and integration with modern applications.
The following characteristics of modern data warehouses distinguish them from the traditional ones:
Smart: A modern DW uses AI/ML to learn, alert, adjust, make recommendations, and administer and use the environment efficiently and effectively. The data architecture for modern data warehouses is not just automated, but also uses (ML) machine learning and (AI) artificial Intelligence to build the tables, views, schemas, objects, and flexible data and architecture models that enable data to flow. It uses AI/ML to identify data types and common keys in tables, identify relations, map tables, join tables as part of data integration, and provide extensions to identify and fix data-quality errors. It provides recommendations related to data and analytics as part of business intelligence, and more. Recent trends and advances have been to automate the processing of unstructured data, like audio, video, images, and so on. Traditional DWs were not designed, optimized, and purposed for these types of data, only for the metadata of these files.
Security: A modern data warehouse is a security fortress; it is able to provide access to authorized users while securing it from hackers and intruders and complying with all the state, national, and international privacy regulations across industries, including the Health Insurance and Portability and Accountability Act (HIPAA) and General Data Protection Regulation (GDPR). Security is provided at multiple levels, such as physical, server, application, network, folder/schema, table, and row. Security is even provided at the data level by using encryption, masking data at rest or in motion, masking personally identifiable information, providing fraud protection services, tracking usage of metadata in catalog, tracking lineage, and tracking changes using an audit trail.
Flexibility: A modern data warehouse needs to be flexible enough to support a variety of complex business requirements from all departments; e.g., from a technology point of view, flexible storage requirements; flexible processing requirements like loading/ extract transformation; loading operation with data refresh rates and scheduling rates (e.g., batch, near real-time, real-time); concurrent query operations; flexible deployment in on-premises, private, public, and hybrid clouds; and integration within and among multiple clouds. Another technical requirement regards flexibility with data processing engines from a variety of other data warehouses, both relational and NoSQL. A modern data warehouse has the ability to be the architecture and handle multiple business use cases’ requirements. Currently, all the features of the specified flexibility are not present in a single data warehouse. Trends clearly indicate we are moving in the direction of having one solution for all. The expectation for the modern data warehouse is high performance, flexibility, and ability to handle diverse data applications, including real-time monitoring, traditional SQL analytics, and AI/ML.
Automation: The ability to handle multiple dimensions of automation, like self-observation, learning engine, self-repair, data quality, integration, and advantages is self-explanatory. Modern DW motivation is the reduction of the amount of work required to build, operate, and maintain a DW. The modern data warehouse has automated architecture in which data flows continuously, so designers must automate everything from ingestion to reports or dashboards in a visualization layer using schedulers, machine learning, and metadata injection. This process first profiles the data and then tags it while data is ingested from external sources, and then maps it to existing tables and attributes designed by data architects. This process is able to compare and detect changes in source and target schemas, objects, and applications. Once anomalies are detected, alerts are triggered to notify the stakeholders, and the issue is reported in operational dashboards. These automated data operations of monitoring, predicting failures, and avoiding them adds operational value by reducing the cycle time of accessing ready-to-use data.
