How can I process big data by using a relational database in Hadoop?

 Big data is processed by using a relational database under Hadoop can be done by a distributed file system of Hadoop. This file system is mainly used for storing large data in a distributed manner. Data warehousing whose name Is Apache Hive can be built on top of Hadoop to provide a SQL-like interface for the data.

For instance, first, you have to ingest the data by using tools like Apache Flume or Apache Kafka in big data Hadoop. It will allow you to handle the streaming of large data sets. Then, you can employ the hives to define schemas and tables over these data sets. Here is an example of a table schema created for customer behavior data:-

CREATE TABLE customer_behavior (
    User_id INT,
    Event_type STRING,
    Timestamp TIMESTAMP,
    /* other relevant columns */
)

STORED AS ORC LOCATION ‘/path/to/hdfs/customer_behavior Once the data is structured you can execute SQL queries to extract the outcomes or insights from the data. Here is the example given:-

SELECT user_id, COUNT(*) as event_count
FROM customer_behavior
WHERE event_type = ‘purchase’
GROUP BY user_id
ORDER BY event_count DESC;

This above example retrieves the counts of events like purchases per user based on the purchase frequency.