Big data has become too complex, fast, or significant to process using traditional methods. However, if big data is structured and analyzed correctly, it can help businesses to accomplish many tasks. For instance, big data can help to create personalized marketing campaigns and enhance customer service. It’s no wonder that 97.2% of organizations are investing in big data in 2022.
Data lake and data warehouse are terms that are commonly associated with big data. But what are they, and do they mean different things? Here is what you need to know about data lake vs. data warehouse.
What Is a Data Lake?
A data lake is a repository system that stores a vast amount of data, including social, system, and sensor data. It can store unstructured, structured, and semi-structured data. Data lakes democratize data and effectively store all data types for later processing.
The main aim of a data lake is to enhance how executives, product managers, data scientists, data engineers, and business analysts access data. Other benefits include the following:
- Democratize Data: This is where everybody in the organization has access to data without involving IT experts. It helps companies by unleashing the value of data locked within organizational departments.
- Provide Quality Data: Data lakes improve the reliability, consistency, completeness, and accuracy of data. This makes it easier for a business to know if certain data can serve its unique needs.
- Keep Native format: You can store data in a lake while maintaining its structure as defined by the application that created it. This protects the integrity of the information and improves file retrieval.
- Support Scalability: A data lake can handle rapid changes in the growth of data, either in volume or traffic. This reduces costs because businesses only pay for what they use.
- Enhance Analytics: A data lake makes it possible for companies to analyze raw data and make conclusions. As a result, the business can answer questions and find trends to enhance decision-making.
- Boost Flexibility: A data lake ensures that data does not attain a maximum “breaking point.” This is important because business applications can continue operating even if the data sets change, shrink, or grow.
What Is a Data Warehouse?
A data warehouse refers to the process of gathering and analyzing data to identify meaningful business insights. The data originates from many operational sources, such as external partner systems, customer-facing apps, finance, sales, and marketing. Once the information is in the data warehouse, decision-makers, business analysts, data scientists, and engineers use it to generate reports and populate dashboards.
The main types of data warehouses are:
Enterprise Data Warehouse
An enterprise data warehouse (EDW) can be defined as a database or multiple databases that centralize an enterprise’s historical information. It can be housed in the cloud or in an on-premise server. An enterprise data warehouse offers structured data to businesses in one place.
Operational Data Store
An operational data store is a database that offers a snapshot of an organization’s current data. It ensures that data is integrated, subject-oriented, and time-variant. Since operational data storage holds very recent versions of business data, it is lighter and faster than other data repositories.
Data warehouses can be vast and overwhelming. As a result, it may be vital to create a data warehouse that provides a single functional data set. A data mart is an object-oriented database that focuses on a particular subject area, department, or line of business.
Data Lake vs. Data Warehouse: What’s the Difference?
Data warehouses and data lakes are both commonly used to store big data, but the two terms mean different things. Here are the main differences between a data lake and a data warehouse.
- Data storage format: Data warehouses store data in traditional relational databases, while a data lake stores data in its native format. As a result, a data lake can use cloud resources for analytics and even migrate an application to the cloud. On the other hand, a relational database allows data to be organized in a tabular form with defined relationships.
- Access: Data warehouses use schema-on-write, while data lakes offer schema-on-read access. Schema-on-write is where data is stored in a structure that is known in advance, such as a table. While this increases precision and query speed, it is impossible to upload data until the table is created. On the other hand, schema-on-read prioritizes data collection instead of data organization. You can upload data as-is without having to follow any internal structures. This makes it easier to store unstructured data in a data lake.
- Data coupling: Data warehouses use coupled computing and storage, while data lakes use decoupled computing and storage. A tightly coupled system means that programs and modules can only operate in a single system and are dependent on each other. Consequently, every data warehouse is purpose-built and can’t deviate from the standard. On the other hand, data decoupling is when data applications work with each other without being directly connected. This allows organizations to unlock the value of data stored in operational systems.
- Analytics: A data warehouse uses online analytical processing, while a data lake uses raw data analysis. Online analytical processing enables executives, managers, and analysts to quickly and consistently gain insights into data with the help of multidimensional structures. On the other hand, raw data analysis allows data to be processed in its original form. This is important because you can always go back to the data to ascertain its authenticity.
Which One to Use
Data warehouses and data lakes are important systems, but they have different benefits. Data lakes are ideal for businesses that store large amounts of raw historical data for later use and data exploration.
Data warehouses cost-effectively provide structured and customized data. They are ideal for business reporting with defined business rules.
Best of both worlds can be achieved with a product such as Azure Synapse Analytics, which combine the power of data lake and a somewhat traditional data warehouse.
Work With a Pro
If your business handles big data, it needs to implement a data lake or data warehouse. Softlanding can help you to choose between the two and implement the solution for you. Contact us for information about our services.