1. What is Big Data and why is it a challenge?

Big Data refers to the enormous amounts of data available to organisations today. Its challenge lies in its volume and complexity, making it difficult to manage and analyse using traditional business intelligence tools. The sheer scale of data, measured in zettabytes, surpasses the capacity of typical data warehouses.

2. What are the limitations of traditional data storage methods?

Traditional data storage often relies on scaling vertically, meaning increasing the hardware capacity of a single server. While this works to an extent, it becomes costly and inefficient as data volumes grow exponentially. The finite capacity of physical servers cannot keep up with the rapid increase in data.

3. How do distributed storage systems address the challenges of Big Data?

Distributed storage systems, like the Hadoop Distributed File System (HDFS), offer a solution by dividing large datasets into smaller chunks and storing them across a cluster of computers. This allows for horizontal scaling, where more computers can be added to the cluster as needed, providing flexibility and cost-effectiveness.

4. What is MapReduce and how does it enable distributed processing?

MapReduce is a programming framework that facilitates parallel computation of data in a distributed system. It divides processing tasks into smaller jobs that are executed concurrently across different nodes in the cluster. The individual results are then combined to produce the final output.

5. How does Apache Spark improve upon MapReduce?

Apache Spark is an enhancement to MapReduce that significantly boosts data processing speeds. While MapReduce processes data stored on disk, Spark can retain data in memory. This in-memory processing capability enables Spark to achieve speeds up to 100 times faster than MapReduce.

6. What are the advantages of horizontal scaling in data storage and processing?

Horizontal scaling offers several advantages over vertical scaling. It allows for:

• Cost-effectiveness: Adding more nodes to a cluster is typically more affordable than upgrading a single server’s hardware.
• Flexibility: Clusters can be easily scaled up or down depending on the data volume and processing requirements.
• Fault tolerance: If one node fails, the system can continue operating using the remaining nodes.

7. What are the key benefits of distributed systems in handling Big Data?

Distributed systems are crucial for handling Big Data due to their ability to:

• Store massive datasets: Distributed storage allows for virtually unlimited data storage capacity by distributing data across multiple nodes.
• Process data efficiently: Distributed processing frameworks like MapReduce and Spark enable parallel computation, significantly reducing processing time.
• Scale dynamically: The ability to add or remove nodes as needed ensures the system can adapt to changing data demands.

8. How does the development of distributed systems impact data analysis?

The evolution of distributed systems has revolutionised data analysis. It has made it possible to:

• Analyse data at scale: Distributed systems allow for the analysis of datasets that were previously too large to handle.
• Gain insights faster: Faster processing speeds enable quicker analysis, leading to faster decision-making.
• Explore complex data relationships: Distributed systems facilitate the analysis of complex data relationships and patterns that would be challenging to uncover with traditional methods.