Final answer:
Relational databases are a tool that can struggle with handling very large datasets. Specialized tools like Hadoop and Apache Spark are often used for working with large datasets.
Step-by-step explanation:
Dealing with very large datasets can be challenging for many tools, but one tool that often poses problems is relational databases. Relational databases are commonly used to store and manage data, but they can struggle to handle datasets that are too large to fit in memory or that require complex joins and queries.
When dealing with very large datasets, it is often necessary to use specialized tools and technologies such as distributed systems like Hadoop or NoSQL databases like Cassandra. These tools are designed to handle massive amounts of data and can scale horizontally to distribute the workload across multiple machines.
Another challenge with large datasets is the need for efficient data processing and analysis. Tools like Apache Spark can be used to perform fast and parallel processing on large datasets, enabling tasks such as data cleaning, transformation, and analysis.