What Is Data Warehouse and Data Lake & Why Businesses Need Both? Business data from various sources can be converted into digital format with the help of a data entry service and stored in a data warehouse or data lake.
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All business decisions and activities are data driven and it helps to understand the customers and market better. Data can be gathered from various sources like face to face to chatting with customers, or taking surveys via phone, mail, or in-person, social media monitoring, in store monitoring, transactional data, collecting subscription and registration data and so on. All these can be organized and converted into digital format with the help of data entry services. Accurate data helps to streamline the business and remove inconsistencies in any record. It enables business leaders to focus on key work instead of wasting time on correcting data, improves efficiency and productivity, and boosts customer satisfaction. As the business starts expanding, the amount of data also expands and proper management of data becomes crucial. 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. So to address these challenges new technologies are developing new data streams – Data Warehouse and Data Lake. Data warehouse plays a crucial role in the IT field whereas Data Lake changes the data management domain drastically. Data Lake Data lakes are created for Big Data analytics as it can handle today’s fast-growing data demands. It is a large data repository that can hold terabyte or petabyte or exabyte range of data and it also helps analyze the data as per the client’s requirements in the future. Some of the key features of data lakes are as follows:
It stores unstructured or semi structured or raw data.
Data lake does not reject any data format. It accepts unstructured data generated through social media, digital images, sensors, emails etc., and can be used when required.
It has endless capacity to store any volume of data.
It stores 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 and it is also known as schemaon-read.
With the unstructured nature of data in data lake, it is highly flexible and can be configured when needed.
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Due to its unstructured data nature, it is used for advanced analytics by data experts and data scientists to generate valuable insights.
When big data came into the picture, the importance of Hadoop data lake also emerges. It is an open-source programming framework that offers storage and processing of extremely large data sets in a distributed computing environment.
Data Warehouse Data warehouse is a data storage architecture that can store data from operational data stores, external sources, and transaction systems. It combines data as per a business’ data analysis and reporting needs. It has features like the following:
In data warehouse, highly structured data format is stored that is used for creating predefined reports and allows to conduct analysis with these data.
It has large storage capacity but not as much as data lakes as they have to perform tasks and run queries quickly. It excludes data like graphical data, images, social media data, sensor data etc as these occupy larger space.
Its functions are based on Extract, Transform and Load process which means that the structure and schema are applied when the data is being written. It is also known as schema-on-write.
It is not flexible as data lake because of its structured data and requires the involvement of expert IT professionals.
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.
It maintains excellent security of data and businesses can stay assured that their data is safe in the data warehouse.
Why Businesses Need Both Data Warehouse and Data Lakes Data warehouse is a large data storage facility with organized database that contain extracts of production data, including historical information. It is built with labelled rows and columns that
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uses structured data that helps business operations to run more quickly as there is no ambiguity. Data lake is also similar to data warehouse but it is much larger with non-relational data. It enables organizations to hold onto much larger quantities of data than they could before, and contains a much greater variety of data than warehouses. As businesses start collecting various information from different sources and exploring more possibilities with it, data lake becomes a crucial addition. When the business stakeholders need certain pieces of information, or analyze specific data sets or metrics regularly, data warehouse is sufficient and gives necessary answers. In simple terms data lake can be considered as a preparatory environment to process large data sets before feeding them into your data warehouse. For efficient use of data, it has to be converted into digital format with the help of data entry companies so that businesses can use the information for better decision making. The ability to leverage data in innovative ways gives a competitive advantage to the business. If enterprises want to stay ahead, they will have to realize the complementary functions of data warehouses and data lakes, and work towards a model that gets the best out of both.
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