The answer is:

False

Explanation:

1. Relational databases are not particularly suited for big data analytics. While they are excellent for storing structured data and ensuring data integrity, they struggle with the volume, velocity, and variety of big data.

2. Big data often includes unstructured or semi-structured data, such as text, images, and social media posts, which do not fit neatly into the table-based structure of a relational database.

3. Relational databases also struggle with the high velocity of big data. They are not designed to ingest data at the speed that it is often generated in a big data context.

4. Finally, while relational databases can certainly store a lot of data, they are not designed to handle the massive volumes of data typically associated with big data. Scaling a relational database to handle big data often requires significant resources and can lead to performance issues.

5. For these reasons, other types of databases, such as NoSQL databases or Hadoop-based systems, are often used for big data analytics. These systems are designed to handle the volume, velocity, and variety of big data, and they can scale more easily to accommodate large data sets.

Question

The answer is:

False

Explanation:

1. Relational databases are not particularly suited for big data analytics. While they are excellent for storing structured data and ensuring data integrity, they struggle with the volume, velocity, and variety of big data.

2. Big data often includes unstructured or semi-structured data, such as text, images, and social media posts, which do not fit neatly into the table-based structure of a relational database.

3. Relational databases also struggle with the high velocity of big data. They are not designed to ingest data at the speed that it is often generated in a big data context.

4. Finally, while relational databases can certainly store a lot of data, they are not designed to handle the massive volumes of data typically associated with big data. Scaling a relational database to handle big data often requires significant resources and can lead to performance issues.

5. For these reasons, other types of databases, such as NoSQL databases or Hadoop-based systems, are often used for big data analytics. These systems are designed to handle the volume, velocity, and variety of big data, and they can scale more easily to accommodate large data sets.

Knowee AI · Accepted Answer

The answer is:

False

Explanation:

1. Relational databases are not particularly suited for big data analytics. While they are excellent for storing structured data and ensuring data integrity, they struggle with the volume, velocity, and variety of big data.

2. Big data often includes unstructured or semi-structured data, such as text, images, and social media posts, which do not fit neatly into the table-based structure of a relational database.

3. Relational databases also struggle with the high velocity of big data. They are not designed to ingest data at the speed that it is often generated in a big data context.

4. Finally, while relational databases can certainly store a lot of data, they are not designed to handle the massive volumes of data typically associated with big data. Scaling a relational database to handle big data often requires significant resources and can lead to performance issues.

5. For these reasons, other types of databases, such as NoSQL databases or Hadoop-based systems, are often used for big data analytics. These systems are designed to handle the volume, velocity, and variety of big data, and they can scale more easily to accommodate large data sets.

Relational Databases are particularly useful for big data analytics, since they allow to store lots of data with a relatively less complex data model.Question 5Select one:TrueFalse

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