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• Product development: Big data analytics can be used to develop new products and services that meet the needs of customers. This is done by analyzing data on customer preferences, trends, and market research.

The advent of big data has led to a rethinking of the traditional data warehousing approach. Data warehouses were designed to store and manage structured data, but big data is often unstructured or semi-structured. This makes it difficult to store and analyze big data using traditional data warehousing methods.

However, there are a number of new technologies that are being developed to address the challenges of big data analytics. These technologies include Hadoop, Spark, and NoSQL databases. These technologies make it possible to store and analyze big data more efficiently and effectively.

As a result, it is unlikely that we will witness the end of data warehousing in the era of big data. However, we will see a shift towards more flexible and scalable data warehousing solutions that can handle the challenges of big data.

Here are some of the reasons why data warehousing is still important in the era of big data:

• Data warehouses provide a central repository for data from different sources. This makes it easier to integrate and analyze data from different systems.
• Data warehouses can be used to store historical data, which can be used to track trends and identify patterns.
• Data warehouses can be used to create reports and dashboards that can be used to monitor performance and make informed decisions.
• Data warehouses can be used to comply with regulations, such as those governing financial reporting.

Overall, data warehousing is still an important part of the big data ecosystem. It provides a foundation for big data analytics and helps businesses to make better decisions.