Data Lake

Data Lake vs Data Warehouse: Key Differences

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Summary: Data is the king of this new digital era and thus, all businesses are looking to make the most of it. Let us analyze how data lakes and data warehouses fit in this scenario and which of their features make them unique and distinct.

If you are reading this blog, you ought to be well aware of the importance of data and how it is being utilized by businesses to drive growth.

Data Lake and Data Warehouse are both terms of data storage. The major difference is between the way they store and handle data.

First, let’s try and understand these terms individually.

What is a Data Lake?

When a website, mobile app, IoT device, or any other digital asset collects data, it stores it in a ‘data lake’. So, a data lake is the repository of all kinds of data collected by a business. A data stored herein is in its as-is form, without any type of categorization, analysis, and distribution.

The basic function of a data lake is to allow users to easily scale  and access data of any size while saving time for defining data structures, schema, and transformations. The concept was  coined by Pentaho CTO James Dixon for  collection of data in its raw form (something like a lake of water).

The concept was designed to cater to the requirements of big data that benefits from the raw, granular structured data. Hadoop, Azure, etc. are companies that offer data-lake connected services.

What is a Data Warehouse?

The concept is derived from the example of collecting, cleaning, and packaging cleansed water into easy-to-use bottle packaging. When the data is categorized into various requisite fields and analyzed for usability, and then transformed into actionable form, it is called data warehousing.

The technology in itself is a blend of technologies and components that help overall in the strategic use of data. It is the electronic storage of a large amount of information by a business that is designed for query analysis and decision making, instead of transaction processing.

Snowflake, Yellowbrick, etc. are companies currently dealing in data warehouse services. For example, currently top social media apps are using data warehouses to keep the user information safe.

The key differences between Data Lake and Data Warehouse

Though data warehouse and data lakes are relative terms, there are several key differences that demarcate their scope and functionalities:

1.  Processed Vs. Unprocessed Data

The difference between Data lakes and Data Warehouses comprises the type of the data. If raw, the data goes to Data lakes, while processed data is stored in Data warehouses. This is the key difference between the possibilities and usage of these terms.

For instance,  when a business intends to develop a data warehouse, they need to finalize a lot of queries and work processes that will sieve through the data to exactly find the data that can be used by a business directly. Setting up such systems mainly includes facts about AI and complex machine learning-based algorithms that analyze and transform data into usable formats.

On the other hand, data lakes store all forms of data in variable formats. Though, initially, this might require the businesses to set up large storage; the concept helps businesses collect data that might be usable for growth or evolution at a later stage as well. Data lakes may also include data from other businesses that might not have the requisite infrastructure. This data can then also be used by the parent company as the data stored is uncategorized or demarcated.

2.  Type of Data

The data lakes include and accept  data in all forms (traditional as well as non-traditional ) within its architecture. In the data lake, ‘Schema on Read’ is followed and the developers are required to develop means to keep all the available data regardless of their source and structure.

Data warehouses generally consist of data extracted from algorithms and work processes and are thus generally in a pre-set form in terms of their quantitative metrics and attributes. Non-traditional data sources such as web server logs, sensor data, social network activity, text, and images are generally not included within the data architecture of data warehouses.

3.  Cost

Though data warehouses store structured data and even help in transforming them into actionable formats, the algorithms and processes required for their setup are quite expensive and complex.

On the other hand, though data lakes require large storage, storing data with big data technologies is relatively cheaper as they are designed to be installed on low-cost commodity hardware. As the data technologies are often open-sourced, so the required licensing and community support is generally free or very low in cost.

4.  Usage

Both data lakes and data warehouses have their own set of utilizations and cater to their purposes. For instance, in industries wherein predictions need to be made by sieving through eons of structured and unstructured data, data lakes are the most useful options. The education industry and transportation-related businesses mostly require these storage systems to understand how and why the markets shall react in a particular way at a particular time.

However, in industries like healthcare, wherein patient records are required to be structured in a certain set way to perform easy query functionalities or inclusions of data, data warehousing is the way to go. In organizations wherein the data is required by one and all employees like financial organizations, data warehousing, though costly, is the optimum solution for quality.

5.  Flexibility they offer

There is a whole lot of difference in terms of the flexibility that the two data structures offer. Since data lakes are huge consortiums of raw data, there are hardly any limitations set in their storage, access, or usage. Thus, any changes required to be made within them, are easy to make and inculcate. They might require some processing power, but the overall system is quite flexible to use.

Whereas data warehouses include set patterns of data in terms of inputs as well as their outputs. This tends to pose a lot of restrictions when making changes within the data as the changes would have to be shadowed in the basic architecture as well. However, the effort and cost spent in the same are generally awarded easy access.

Lookout for your requirements

Ideally, a business should include both data lakes and data warehouses in its overall infrastructure. But, developing and maintaining both entails a high cost. If you already have a data warehouse, you might want to upgrade it to current data standards for storage or may develop a data lake alongside to accept data in new evolving formats. For businesses with set data lakes, the step-by-step evolution of a data warehouse should be the next consecutive step!

Author Bio

Andrea Laura is a very creative writer and active contributor who love to share informative news or updates on various topics and brings great information to her readers. Being writing as her hobby, Andrea has come out with many interesting topics and information that attracts readers to unravel her write-up. Her content is featured on many mainstream sites & blogs.

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