Lakes form part of the Earth's water cycle. Lakes lie on land and are not part of the ocean. It is located in a basin, surrounded by land, apart from any rivers or other outlets. Anything can enter it freely.
Like it, the data lake is in a mess.
A data lake is a place where new data can tress pass without any hurdles. Since any data can reside in a data lake, it is a fountainhead of new ideas, and you are free to experiment with data. Due to this liberalism, it suffers from an absurd structure.
In data warehousing, you try to match dimensions and measures into questionable components that are consistent. Here the scalability traits of the data warehouse gain meaning and depth. Warehousing makes it easier for a thirsty audience to consume this data.
Data lakes and warehouses are widely used for storing big data, but they are not interchangeable terms. The two types of data storage are often confused but are much more different than they are alike. The only real similarity between them is their high-level purpose of storing data.
There are certain key differences between a data lake and a warehouse.
Operation
While a data warehouse is used for Online Analytical Processing (OLAP), a data lake performs raw data analysis.
OLAP includes running reports, aggregating queries, performing analysis, and creating models based on whatever you want to do. These operations are carried out after your transactions. If your client's data is stored in a denormalized format, you can fetch the data easily and bring the required report.
Data Lake is a storage repository that stores huge structured, semi-structured and unstructured data. At the same time, Data Warehouse is a blending of technologies and components which allows the strategic use of data. Data Lake stores all data are irrespective of the source and structure, whereas Data Warehouse stores data in quantitative metrics with their attributes.
The raw data in XML files, images, pdf, etc., are gathered for analysis in a data lake. You may not know how this data can be used in the future. You can freely apply analytics to get insights.
Low-Cost Data Lake
In a warehouse, the cost of data storage is high. The software used by these warehouses is expensive. The cost of maintenance is also high. Since a data warehouse contains large amounts of data in a denormalized format, it takes up a lot of disk space. In data lakes, the cost is low. You use inexpensive open-source software. Data lakes can scale to high volumes of data at a low cost.
Data warehouses use schema-on-write, while lakes employ schema-on-read. You need to know the purpose of the data before importing it into the Warehouse. In the first instance, before storing the data, it has to be transformed and provided for application in analytics and reporting. You may have to reevaluate the models later.
In data lakes, without the necessity of a single schema, users can store any data. They can discover the schema later while reading it. Different teams can store data in the same place without relying on IT people.
A data warehouse will have high-quality data. The data undergoes curation before storage; it is the absolute truth. A data lake is filled with raw data.
Data analysts use lakes. Large corporates with tons of data opt for the first. Business professionals use warehouses. Warehouses store sensitive data. Data Lake is a new technology, and security is still developing. The Hadoop ecosystem is aligned to the data lake.
Data lakes empower users to access data before it has been transformed, cleansed and structured. It allows users to get to their results more quickly compared to the traditional data warehouse. Warehouses offer insights into predefined questions for predefined data types. Any changes to the Warehouse needs more time.
Data Lakes use of the ELT (Extract Load Transform) process. The Warehouse uses a traditional ETL (Extract Transform Load) process. The main complaint against warehouses is the inability, or the problem faced when trying to change them. Lakes integrate different data types to answer new questions as these users are not likely to use data warehouses because they may need to go beyond their capabilities. In the Warehouse, most users in an organization are operational. These types of users only care about reports and key performance metrics.
Data Lake is ideal for those who want in-depth analysis, whereas Warehouse is ideal for operational users.
A data lake is the starting point. It is the stage at which the Warehouse structures its data. An organization that incorporates both showcases entrepreneurship and strategy.
Different Spheres
Data warehouses have been used for many years in the healthcare industry. Still, it has never been hugely successful. Data warehouses are generally not ideal because of the unstructured nature of much of the data like physicians notes, clinical data, etc. Data lakes allow for a combination of structured and unstructured data, which tends to be a better fit for healthcare companies.
In recent years, the value of big data in education reform has become enormously apparent. Data about student grades, attendance, and more can help to fail students get back on track and help predict potential issues before they occur. Flexible big data solutions have also helped educational institutions streamline billing, improve fundraising, and more.
Much of this data is vast and very raw, so many times, institutions in the education sphere benefit best from the flexibility of data lakes.
In finance and other business settings, a data warehouse is often the best storage model because it can be structured for access by the entire company rather than a data scientist.
Big data has helped the financial services industry make big strides, and data warehouses have been a big player in those strides. Such a model may drive away a finance company because it is more cost-effective but ineffective for other purposes.
In the transportation industry, especially in supply chain management, the prediction capability that comes from flexible data in a data lake can have huge benefits, namely cost-cutting benefits realized by examining data from forms within the transport pipeline.
Four Differences
There are four key differences between a data lake and a Warehouse:
1. Lake is raw, Warehouse is processed
2. In the lake, the purpose of data is not determined; it is currently in use in the Warehouse.
3. Data scientists use the lake; business professionals use the Warehouse.
4. Lake is highly accessible and quick to update; Warehouse is complex and costly to make changes.
© Ramachandran
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