MENU

Best Use Cases for Data Warehouses and Data Lakes

When it comes to the best use cases for either a data warehouse or a data lake, the possibilities are only limited by the imagination. And while some see flaws in the data warehouse model, it doesn’t mean there isn’t a place for cleansed and packaged and structured data marts. Some use cases for data warehouses include:

  • Great for online businesses interested in analyzing user behavior in order to make better business decisions.
  • Perfect for conducting market research by analyzing vast amounts of data in-depth.
  • Excellent for handling data mining used to obtain new insights from information contained in scores of large databases.
  • An ideal option for those wanting to access and act on data in real-time.
  • Perfect for perceiving data lineage as a way to ensure regulatory compliance.
  • Exceptional at bringing data together into a single place.

Data lakes have some very interesting use cases as well. Some of them include (by industry):

  • Used with marketing and customer data platforms (CDPs) to create a centralized customer database that extracts data from various behavioral and transactional sources including brick and mortar systems, web and mobile behaviors, service center data, and customer profile data.
  • Proactive cybersecurity systems combine data lake(s) with streaming analytics, data collection, artificial intelligence, event notifications, among other things.
  • Machine-learning algorithms are being used in life sciences, allowing scientists to examine portions of the human genome. By applying machine learning algorithms to data lakes, scientists and doctors hope to more precisely predict and prevent certain types of illnesses like sepsis.
  • Data lakes and Big Data analytics are assisting in smart city initiatives. From intelligent power grids to connected vehicles, data lakes will play a big role in building the cities of the future.
  • The oil and gas industry has long since employed digital transformations brought about by IoT devices. One example being geologists using data science and GPS to steer drill bits horizontally instead of vertically. In this way, oil and gas companies have been able to increase their production by 20 times.

Just as data warehouses and data lakes are inherently different, the type of storage used for either one is a separating factor as well. High-performance storage devices like RAID Inc. + Lustre over ZFS are critical for data warehouses; slower, low-cost devices cannot support schema-on-write (creating a schema for data before writing into the database) required by data warehouses.

Due to a data lake’s flat architecture, it offers vast scalability up to the exabyte scale. Traditional storage devices aren’t up to the task for this as well. This is very important to keep in mind, as you have no way of knowing in advance the volume of data your storage system will be required to hold. For this, RAID Inc. + Lustre is perfect for those looking to form large data lakes and content repositories to deliver High-Performance Computing (HPC).

To learn more about this subject and about our storage solutions, talk to one of our experts today.