Is bigdata going to end rdbms?

Big Data and relational database management systems (RDBMS) serve different purposes and are often used in conjunction rather than as direct substitutes. While Big Data technologies have gained prominence for handling massive volumes of diverse and unstructured data, RDBMS continues to play a crucial role in managing structured data and supporting transactional applications. It's essential to understand the characteristics and use cases of each to appreciate their coexistence:

1. Use Cases:

   • RDBMS: Ideal for structured data, transactions, and applications requiring ACID (Atomicity, Consistency, Isolation, Durability) properties. Commonly used in traditional business applications, finance, and systems with well-defined and consistent data structures.
   • Big Data Technologies: Suited for processing and analyzing large volumes of diverse and unstructured data, often in a distributed and parallelized fashion. Use cases include analytics, machine learning, and handling data types like text, images, and streaming data.

2. Scalability:

   • RDBMS: Generally scaled vertically by adding more resources (CPU, RAM) to a single server. While some RDBMS systems offer horizontal scaling options, they may not scale as seamlessly as distributed Big Data solutions.
   • Big Data Technologies: Designed for horizontal scalability, allowing organizations to scale out by adding more nodes to a cluster. This is particularly beneficial for handling large datasets and processing tasks in parallel.

3. Data Variety:

   • RDBMS: Primarily deals with structured data and may not be as flexible when it comes to handling unstructured or semi-structured data.
   • Big Data Technologies: Excel in handling a variety of data types, including structured, semi-structured, and unstructured data. They are well-suited for scenarios where data schemas may evolve over time.

4. Latency and Real-time Processing:

   • RDBMS: Designed for low-latency transactional processing. Real-time analytics may be challenging, depending on the complexity of queries and volume of data.
   • Big Data Technologies: Can handle real-time processing and analytics, especially with technologies like Apache Spark and streaming frameworks that enable processing data in near real-time.

In summary, rather than replacing RDBMS, Big Data technologies often complement them in modern data architectures. Many organizations adopt a polyglot persistence approach, using different data storage and processing tools based on the specific requirements of each use case. RDBMS remains a critical part of the overall data landscape, especially for applications with structured data and transactional requirements.