As business analytics becomes omnipresent, the need of managing data in the best possible way to generate faster insights and develop data governance processes that remain versatile and scalable for the future has come into the picture. Companies have formed data management ecosystems that they find most suitable for their needs, but the challenge is that the business and technology landscape keeps evolving with every passing minute. To address the changing needs, new technologies are adopted giving rise to new data streams that must be integrated. Data warehouses have long been an integral part of the IT landscape of companies and now data lakes are creating ripples and changing the data management domain in a huge way. Let us examine some of the key differences of data lake vs data warehouse.
A data lake is a large repository of unstructured data that can flow in from any source. The data is not altered and stored in the same way as it comes. This helps in analyzing the data as per the client's requirement anytime in the future.
Data lakes have been gaining popularity at a rapid rate in the past few years. The main reason for this growth is the numerous benefits it has to offer. Some of the key features of data lakes are listed here -
Although there is some structure in order to make sense of data, the data in a data lake exists in its raw format or in a semi-structured format
Data lake does not reject any data format. All types of data sources can be plugged into a data lake and can be used when needed. Unstructured data generated through social media, digital images, sensors, emails, etc., are all taken in and stored
Being a cloud-based solution, data lakes are usually endless in capacity and can hold any amount of data
Since data lakes exist in low-cost storage solutions, the overall cost of maintaining a data lake is lower as compared to a data warehouse
The purpose of a data lake is to store data of any format and structure, and when the data is required for analysis, a structure is put in place to extract only the required data. This is also known as schema-on-read
Due to the unstructured nature of a data lake, it is highly flexible and can be used as needed. It can be configured quickly as when the need arises
A data lake is used for advanced analytics that is explorative in nature. Since the data is highly unstructured, it can be used only by data experts and data scientists in order to generate insights
When big data came into the picture, the importance of Hadoop data lake also rose. Hadoop is an open-source programming framework that offers storage and processing of extremely large data sets in a distributed computing environment
The security of a data lake is still in the maturing phase and more can be done to achieve 100% confidence
Data lakes exist because data warehouses did not fulfill the need of enabling deep dive analytics. As opposed to the restrictive schema of data warehouses, data lakes offer an unrefined view of data which can be used by skilled data scientists to conduct analytics with any techniques they like, irrespective of the structures defined traditionally. This enables them to generate insights that are unique and more wholesome, as a variety of data is used to generate those insights. The importance of data lakes came into perspective when businesses started needing real-time data to be analyzed quickly.
A data warehouse is a data storage architecture which is designed to store data from operational data stores, external sources, transaction systems, etc. The warehouse then combines the data as per the businesses' need for further data analysis and reporting.
The data warehouse has been in use for a long time now. The highly structured form of data was preferred by businesses. Some of the key features of a data warehouse are listed here -
The data is stored in a highly structured format so that it can be used for creating pre-defined reports and enable users to conduct analysis by playing with the data in a safe setting
Data formats are fixed based on the analytics tools used or based on the purpose of data. Only the data that is needed for analytics is plugged into a data warehouse and a lot of data is left out as well. A great deal of thought goes into what data is used for the type of queries that might come up
The storage capacity of data warehouses is also huge, but since they have to perform tasks and run queries quickly, they cannot become as massive as data lakes. Graphical data, images, social media data, sensor data, etc., are left out because they need greater space for storage
Data warehouses are often costlier to maintain as compared to a data lake. Depending on the analytics needed and the complexity, the initial investment of defining the schema and need for IT professionals to generate queries also adds up
A data warehouse functions are based on an ETL process (Extract, Transform, and Load). This means that the structure and schema are applied when the data is being written. It is also known as schema-on-write
Due to the highly structured nature of a data warehouse, it is quite cumbersome to change and requires the involvement of expert IT professionals. Often simple changes are also hugely time-consuming
A data warehouse is something on top of which analytics applications are implemented. These analytics tools have simple user interfaces and can be used by business users who are not skilled in IT. This means that a data warehouse makes advanced analytics, data, and reports available to any user in the organization
A data warehouse is essentially a relational database management system which has rows and columns of data for which schema and rules are defined
Data warehouses have reached a maturity level in maintaining security and businesses can rest assured that their data warehouses are safe.
When businesses need a single version of the truth, they make use of a data warehouse, where the data is structured, homogenized, subject-oriented, and ready for use. A data warehouse can become the base on which different types of Business Intelligence (BI) tools can be loaded in order to consolidate, slice or dice the data, and generate timely reports that can be used for making everyday business decisions.
To summarize, let's look at the top differences between data lake and data warehouse -
Any data source or format
Predefined data source
Flatworld Solutions has been providing data warehouse and data lake management and a series of other data management solutions for over a decade. Whether your business needs a data warehouse or a data lake, or you wish for them to coexist so that all the data requirements of your business are met - we can make sure that you attain the best data solution. Although data lakes are flexible and unstructured, they can quickly become chaotic if not monitored and maintained. We have helped many companies implement highly structured data warehouses to meet their data analytics needs.