The influx of digital technology in our day-to-day lives has resulted in gathering a huge chunk of data daily. Now, keeping this information safe and sound is imperative for future uses. Now, organizations use different frameworks to maintain this data, and two popular ones are data warehouse and data lake.
A data warehouse is like a traditional storage space. Here the information is kept in an organized manner to ensure that they are readily available whenever needed. Contrarily, in a data lake architecture the information floats in it without any segregation or organization.
What are data warehouses?
Simply speaking, data warehouses refer to storage systems or data management systems that are responsible for storing and managing data. The kind of data stored by these warehouses involves data drawn from a variety of sources, such as: -
- CRM or customer relationship management systems
- Data about the various accounting departments
- Sales-related data
- Marketing data etc.
A data warehouse essentially demarcates and stores data in the form of levels or ‘tiers’ that is: -
- 1st tier - wherein the data used and required frequently by the organizations or companies is presented to the clients through the help of data mining, reporting etc.
- 2nd tier – wherein the necessary processes or engines that access the data are housed
- 3rd tier – the lowermost tier wherein the data is sent by the various sources from and stored until requested or accessed by the organization in question.
The types of data warehouses are: -
- Operational Data Store (acts as a data source for the enterprise or corporation mentioned above)
- Enterprise Data Warehouse (usually stores data and manages them for an enterprise or corporation)
What are data lakes?
Data lakes refer to central storage systems that allow organizations and users to store semi-structured data, structured data and unstructured data without allocating or demarcating them as per their volume.
Data lakes generally receive data from the following sources: -
- Mobile applications (usage, functions carried out etc.)
- Social media applications (metrics etc.)
- IoT-based cloud systems and devices
- Corporate applications
As data lakes generally store raw data, or data that is not in its final or processed form, the data stored within them must processed and analyzed before being sent to the clients.
Why are data warehouses and data lakes used?
Data warehouses store structured data or data that has been processed and ready for client usage. They make searching for and releasing data easy as they demarcate and store their data according to parameters.
Data lakes, on the other hand, store all kinds of data that is raw, structured etc. They are used as repositories or storehouses of data.
Differences between Data Lake and Data Warehouse
|Data lake||Data warehouses|
|Used to store all types of data in a cost-effective manner||Used to store and present data to the clients after analyzing and processing them|
|Generally used to store data that is only used as reference/queries (that is, read-only data)||Generally used to store data that is used for analytics-related functions, or even analytics-based data|
|Nature of the data is dynamic||Nature of the data stored is mostly historical|
|Generally used by data engineers and data scientists||Generally used by business analysts and data analysts|
What are the challenges of using data warehouses and data lakes?
Challenges of using data warehouses
- The data stored in data warehouses is not secure and can be leaked across the various levels of the enterprise.
- The economic costs of maintaining data warehouses are not beneficial for the enterprises and organizations that use them. In other words, the cost-to-benefit ratio is not high.
- As the nature of the data stored is not generally dynamic, the time required to process the data into dynamic data reduces the efficiency.
- The processing of setting up a data warehouse architecture is a time consuming process, especially when the data is not stored accurately.
- If a particular project requires users to request more data queries from the warehouse, it could lead to performance issues.
Challenges of using the data lake
- The data stored here cannot be demarcated due to the nature of the storage. This lack of demarcation increases the complexity of the data stored, as it is not easy to allocate or utilize data.
- The data placed in a data lake could face security risks as some of the data could have restricted access, which could potentially get revoked due to the nature of the storage location
- Data stored in data lakes tend to lose either its quality or usefulness after being stored over a long duration of time, similar to a battery leaking after long use, which renders the data stored in the data lakes redundant and unfit for use.
- The long-time storage of data in data lakes leads to increasing costs to maintain the same
- As there is no particular chain of data, the governance of the same becomes difficult for both the contributors and sources of the data as well as the organization or data controlling the same.
What are the purposes of data warehouses and data lakes?
Data warehouses are generally used to store and present data that is required at a high frequency by the members of the organization or their clients. In contrast, data lakes are used to store data for easy perusal and reading.
The particular purpose of data warehouses is to have data that can be analyzed at a moment’s notice. Contrarily, the purpose of data lakes is to have a cost-effective method of storing and reading data.
Why are data warehouses and data lakes important?
Data warehouse architecture plays a critical role in managing large volumes of data, and its benefits include:
- They are stable repositories of data; that is, they are non-volatile
- It can focus on specific areas and classifications of data as per the demands
- It can map the various changes that take place in the data over time
- Data Storehouses can integrate various types of data from multiple sources
- They assist their parent organizations in organizing their data better
Importance of data lake architecture
Data lakes are important as they have several benefits, namely:
- It allows organizations to interact with and communicate with their customers better due to their ability to store various kinds of data of various volumes
- Data Lakes can optimize the processes of companies that rely on cloud-based frameworks such as IoT-based frameworks etc.
- They allow testers etc., and other related professionals to test their programs better, contributing to a more effective product design
- Data lakes are important to a wide range of users due to their capacity to store multi-faceted information
- The low scale of data due to the low cost of the hardware required makes these systems cost-effective
What are the applications or use cases of data warehouses and data lakes?
Applications of data warehouses
- In the world of banking, for purposes such as market research, monitoring market exchange rates etc.
- In the FMCG companies for analyzing consumer trends, etc.
- To manage systems such as the payroll systems that allow organizations to pay their employees
- In the world of hospitality, wherein organizations map the trends or results of their various advertising, professional etc. campaigns
- In the world of healthcare, store and manage various kinds of data such as financial, clinical, etc.
Applications of data lakes
- In the Gas and Oil industry, it stores vast volumes of data related to the quantities of the same, the safety regulations etc.
- In the world of the various life sciences, which use them for mapping the various dynamic data and measuring the changes etc., by comparing the read-only data
- For marketing, where large volumes of data related to campaigns is generated on a daily basis
- In the world of cybersecurity, the lack of precise order of storing data and the ability to store volumes of data will prevent data thefts etc.
- To be able to build integrated data systems to manage groups such as smart cities etc.
Summing up, data warehouses refer to those structures that help store data precisely and efficiently to ensure easy presentation and delivery of the same after analyzing and processing them. Data lakes, on the other hand, are storehouses of various kinds of structured, unstructured data etc., that are primarily used as read-only data sources.
Thus, learning about data warehouses and data lakes can help you understand how to store, manage, and retrieve data when working on enterprise data projects.
Hero Vired offers programs across data science, machine learning and data science, data engineering along with business analytics. You can learn more about data warehousing and data lakes through these programs, as they all help you learn various key concepts from the world of data.