Data Warehousing vs Data Lakes: Choosing the Right Approach

Data Lakes Vs Data Warehousing; Which Is The Best For Your System?

What Are Data Lakes?

An extensive collection of both organized and unstructured data is called a data lake. In order to leverage the data for advanced business intelligence, machine learning, and data discovery, it may store data in any format and from any source. In terms of the data pipeline, a data lake employs the extract/load/transform (ELT) technique, whereby the data is taken from the source or sources, put into the lake, and then transformed as required.

By using a schema-on-read methodology, structure is only added to the data at the time it is pulled for analysis. The possibilities for what kinds of documents and raw data may be stored in data lakes are endless. Examples include user and research data, media and video files, application and medical imaging data, and much more. Before being utilized for analysis, the data kept in a data lake has to be standardized, cleansed, and prepared.

What is data warehousing?

A data warehouse is a well-structured system designed to uphold superior standards. Customer records, for example, cannot be added unless they comply with data requirements (e.g., all US states must use two-character abbreviations). These limitations mean that the data in data warehouses is often of a high caliber. There are other trade-offs to take into account. A data warehouse works well for organizing structured data, although it does require significant continuous upkeep.

When accuracy and integrity of the data are top concerns, data warehouses are the ideal answer. For instance, to provide your auditors with access to high-quality data, you may store financial data in a data warehouse. A data warehouse works well for recurring tasks like "produce a standard report each month."

Data warehouses are already in existence at the majority of large firms. Such systems have a purpose. They do, however, necessitate a large initial investment in data management. Additionally, the types of data they can handle are restricted; unstructured data is typically not desired. The next step is to build up a data lake if you want to take things a step further. A standard-issue database is not nearly as powerful as a data lake.

Why Should You Opt For A Data Lakes System?

1. Less time-consuming and higher flexibility:  Your data may go beyond the clouds when you use a data lake. You can put everything you want into a data lake. Text, structured data sets, customer reviews, invoice data, and more may be included. You don't need to do intricate settings for each new type of data you add, in contrast to a data warehouse. You'll have more time to extract insightful information from your data when labor-intensive data administration and cleansing tasks are eliminated. Because data lakes are free-form, you are no longer limited by your ideas. The information you add to the data lake now could come in handy in four or five years. Next, we'll see an illustration of it with natural disasters.

2. Quick gain of insights using ml:  When using a data warehouse, you must first identify the final purpose of your data analysis. A data lake is not like that! You may leverage machine learning to find new correlations by inputting a wide range of data. Walmart, for instance, found a correlation between snack purchases and hurricanes! The corporation discovered that Pop-Tart sales surged sevenfold prior to a hurricane! Few people would have thought that storms could forecast a certain kind of sale, which makes this association a perfect illustration.

To conclude; Take a step back before selecting between a data warehouse and a data lake. These are data instruments that must function in support of your plan. The majority of sizable businesses will employ both data lakes and warehouses. For situations like financial reporting, when data integrity and accuracy cannot be compromised, a data warehouse is required. A data lake is a great option when you want to promote creativity, experimentation, and fresh concepts. In actuality, sales, marketing, and customer support teams are typically the ones who use data lakes the most.