The interest in Data Lake has been rising over the past few years, and Data Lake services have been on the rise. We’re writing this today to offer the reader a better understanding of the difference between Data warehouse and Data lake, which have essentially become pivotal in managing the data of an organization. These two types of data storage are often interchangeably referred to or confused; when in reality the only similarity is that they are useful for storing data.
Data Warehouse
The standard definition of Data Warehouse is “… central repositories of integrated data from one or more disparate sources. They store current and historical data in one single place that is used for creating analytical reports for workers throughout the enterprise”.
Sounds complicated? Let’s simplify.
Data warehouses store data in one place which has applications in Analytical Reporting. One key process of a data warehouse is that it may require data cleansing before it is used for additional operations to ensure data quality.
Data Lake
According to Wikipedia, Data Lake is usually a single store of data including raw copies of source system data, sensor data, social data, etc. In other words, it is used to store data in its raw format such that it can be accessed as it is and insights can be generated out of it. Data Lake is like the storage room of your house, where things are stored without much order or knowledge about when will the use arise, whereas Data Warehouse can be taken as the more organized living room or the library (sorted according to the genre) etc.
Now, if you are thinking about Why Data Lake when we have a Data warehouse or Why Data warehouse when we have a Data Lake, then let’s understand it by taking the example of the healthcare industry.
In this sector, a large volume of structured, semi-structured, and unstructured data is collected in the form of inpatient records, clinical data, personal health records, etc., and the insights that are generated are needed in real-time like evidence-based care or predicting patient flow or their costs. Data lakes take healthcare analytics to the next level by supporting high-end and complex analytics with a faster turn-around time – thus providing higher value and greater ROI for companies.
But with Data warehousing the approach to generate insights from this has failed as DWHs need structured and pre-processed data, which are more useful for day-to-day clinical needs, financial, and operational reporting than generating insights. So, the need or requirement between the Data warehouse and Data Lake depends on the end-user and the purpose of the data.
Before we go into the detailed differences between Data Warehouse and Data Lake, a small overview of a Data Mart, in case you were wondering about it. Data Mart is essentially a Data warehouse but in a simple form but is controlled by a single department (or a functional area) in an organization. Because data marts generally cover only a subset of the data contained in a data warehouse, they are often easier and faster to implement and require less memory than a data warehouse.
Differences between Data Lake and Data Warehouse
There are various differences between a Data Lake and a Data Warehouse, mainly related to the Type of data Stored, the purpose, and the end-users. The differences can be summarized below:
Source: Polestar Solutions
- Type of data storage
In data lakes, the raw data is stored in its original format, therefore it can be useful for storing .csv, .json, emails, cloud documents, CRM, or any form of data as it is created. This causes an increase in the required storage capacity and the flexibility to model the data. For the development of a data warehouse, analyzing the data sources and profiling the data takes a considerable amount of time and resources. So, the end result would be a highly structured data model. But might have some data removed to simplify the data model.
- Architecture
A data lake uses a flat architecture as it is needed to store a huge amount of raw data in its native format until it is needed. The data elements in data lakes are assigned unique identifiers and tagged with extended metadata tags, which can help in querying and analyzing the data. On the other hand, a hierarchical data format is used in a Data warehouse which stores data in files or folders with a defined schema. The information in a data warehouse is stored by the subject in order to assist management make quick decisions.
- Purpose and Usage
As the data lake stores data in its original format, it is pliable and easy for data transformation and analytics. Therefore, is mostly used by Data scientists or those who perform deep analysis on data with the help of analytical tools and statistical modeling techniques. But data lake can also be useful for all the users in the organization but needs more technical knowledge for transforming the data. Whereas, Data warehouse already has structured data that can be used by Business users or anyone without much technical knowledge also. Also, the data warehouse is made with a specific purpose in mind whereas the raw data format of data lakes gives user the flexibility to remodel the data as per your requirements.
So, in case you are wondering which one to choose for your organization, as yourself what do you use the data for? And how much insights do you want to generate from your data.
In conclusion, Data lakes are a game-changer. It saves IT a considerable amount of money and also supports high-end analytics use cases, giving businesses a significant ROI. Data warehouse, on the other hand, allows for more strategic use of data. Organizations typically look at data lakes as additions to their existing data warehouse. With industry expertise and experience find the right implementation partner for Data Lakes, such that you can leverage the maximum potential of your data. With Polestar solutions, offer better governance with modern ingestion technologies that support all forms of data and metadata integration for your business.
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