What is the differences between RDBMS Data Warehouse and big data Hadoop?

Relational Database Management Systems (RDBMS) Data Warehouses and Big Data Hadoop are both technologies used for handling and processing large volumes of data, but they differ in several key aspects. Here are the main differences between them: Data Structure: RDBMS Data Warehouse: RDBMS typically stores structured data in tables with predefined schemas. The relationships between tables are well-defined through keys (primary and foreign keys). Big Data Hadoop: Hadoop can handle structured, semi-structured, and unstructured data. It is not bound by a predefined schema, allowing for the storage and processing of diverse data types. Data Processing Model: RDBMS Data Warehouse: RDBMS uses SQL (Structured Query Language) for data querying and processing. The processing model is typically batch-oriented, and transactions are ACID compliant (Atomicity, Consistency, Isolation, Durability). Big Data Hadoop: Hadoop uses a distributed processing model, and its primary processing framework is MapReduce. However, other frameworks like Apache Spark are also commonly used. Hadoop is designed for processing data in parallel across a distributed cluster of nodes. Scalability: RDBMS Data Warehouse: Traditional RDBMS systems may face challenges in scaling horizontally (across multiple machines) to handle large volumes of data. Vertical scaling (adding more resources to a single machine) is a common approach. Big Data Hadoop: Hadoop is designed for horizontal scalability. It can easily scale by adding more nodes to the cluster to handle increasing data volumes and processing requirements. Storage Cost: RDBMS Data Warehouse: The cost of storing and processing large volumes of data in traditional RDBMS systems can be high, especially as data grows. Big Data Hadoop: Hadoop can be more cost-effective for large-scale data storage and processing, as it utilizes commodity hardware and is designed for distributed storage and processing. Data Processing Speed: RDBMS Data Warehouse: RDBMS systems are optimized for transactional processing and might not perform as well when dealing with large-scale analytical queries. Big Data Hadoop: Hadoop, especially with the use of frameworks like Apache Spark, can handle both batch and real-time processing, making it suitable for a broader range of use cases. Schema Flexibility: RDBMS Data Warehouse: RDBMS requires a predefined schema, and any changes to the schema can be complex and time-consuming. Big Data Hadoop: Hadoop allows for schema-on-read, providing flexibility in working with diverse and changing data without the need for a predefined schema. Use Cases: RDBMS Data Warehouse: RDBMS is suitable for traditional transactional applications and structured data analysis. It is often used for business intelligence and reporting. Big Data Hadoop: Hadoop is well-suited for processing and analyzing large-scale, diverse datasets, including unstructured and semi-structured data. It is commonly used in big data analytics, machine learning, and handling data from various sources. In summary, RDBMS Data Warehouses and Big Data Hadoop serve different purposes and are optimized for different types of data and processing models. While RDBMS is well-established for structured data and transactional applications, Hadoop excels in handling massive volumes of diverse data in a distributed and cost-effective manner. read less