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In recent years, there has been much discussion about data lakes and data warehouses, with data scientists and business leaders from all over the world chiming in and providing their two cents’ worth regarding how each of these types of data repositories can be of benefit to organizations for high-level data storage.
Let’s take a closer look into the primary differences of data lakes and data warehouses before considering which one might be most beneficial to your business.
Two different technologies
A data warehouse is an integrated repository of all the data generated and collected by an enterprise which can be used for data analysis and reporting. This “warehousing” is a form of technology that accumulates structured data from various sources, which can then be processed or used for business intelligence purposes.
A data lake, on the other hand, can be defined as a huge pool of data in its raw or unprocessed format. By its nature, raw or unprocessed data is very ductile, which makes the, ideal for many types of analytics applications. However, businesses must ensure that appropriate data governance policies are in place so that data lakes don’t turn into an out-of-control data pools that are inaccessible to their intended users.
Understanding the main differences between a data warehouse and a data lake will be central to any decision you will make as it pertains to high-level data storage for your business.
As previously mentioned, data warehouses only aggregate data that has been processed and are thus structured in nature. Such data typically come from transactional systems. Data lakes, on the other hand, is data agnostic, storing any type of data, whether they are structured, semi-structured, or unstructured—all in their raw or unprocessed form. The data in data lakes can be consolidated from various sources, including business applications, internet of things devices, social media, smartphone applications, and various websites.
Data Analysis Strategy
Data warehouses follow the schema-on-write approach in which schemas for data are created before writing into the database. This means that the data is provided structure before it is loaded into the warehouse. With data lakes, schemas are written at the time of analysis—that is, only when the raw data is ready for use. This approach is called schema-on-read.
Purpose of Data
When raw data is processed, it means that it has been used for a particular purpose within the enterprise or organization. When the data used and profiled, it often results in the data becoming very structured as to be useful for analysis and reporting. In such a process, not all data are retained, which gives organizations the advantage of streamlining their data model, saving on storage space, getting rid of data that may never be used in the first place.
With data lakes, on the other hand, all types of data flow into the big pool of data and are retained for possible future use. This presents the benefit of businesses having access to historical or original data, when opportunities to take advantage of them arise.
Since data warehouses are highly structured repositories of data, businesses can end up spending significant amounts of time getting their structures right. Conversely, data lakes lack this complexity in structure, which makes them easier for data scientists and developers to access the information they need to answer their organization’s most pressing questions.
It’s true that data lakes can be easier to access, but the condition is for one to be familiar with the practice of navigating raw, unprocessed data. For the most part, this is only possible if one is a data scientist who is proficient in using specialized analytics tools.
Which Is Best for Your Business?
Given the complexities that surround data warehousing, it becomes incumbent upon business leaders to decide whether they should completely embrace data lakes instead.
The general consensus among data experts, however, is to instead adopt a hybrid approach that not only eliminates the disadvantages of each type of data repository but also combines their advantages. Such a setup presents tremendous opportunities for greater efficiency, cost savings, and better business intelligence for enterprises.