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Data Warehouse vs. Data Lake: What’s the Difference?
Both a data lake and a data warehouse offer businesses smart storage solutions that will help you integrate large amounts of data and gain better business insights accordingly. A data lake is a large amount of raw data with no defined purpose. A data warehouse is a repository for structured, filtered data that has already been processed for a specific purpose. Big data analysis involves distilling a large volume of data — often collected by businesses and organizations — into an easy-to-understand report. This data pool is often analyzed by data scientists for insights that lead to strategic business decisions. The data itself usually consists of complex information that is difficult to process using traditional methods, so it is often stored in data storage systems called "data warehouses" and "data lakes." These two terms are often used interchangeably, but there are actually some notable differences between them. Both data storage systems allow businesses to make smart, data-driven decisions, reduce costs and optimize offerings. By analyzing the data stored in these systems, businesses can also determine the root causes of failures in real time, calculate risk portfolios, detect fraudulent behavior and more.
AWS data lakes are increasingly popular because it allows companies to manage multiple data types from a wide variety of sources, and store this data, structured and unstructured, in a centralized repository. AWS cloud services are built to help companies to manage multiple data types from a wide variety of sources, and store data, structured and unstructured, in a centralized repository. AWS data lakes are becomming more popular in 2021 as more organizations implement AWS cloud solutions therefore making this compatibility a huge benefit to the way they collect and store data.
Businesses of all sizes integrate many types and sources of data, build an analytics strategy and unlock insights for a better business plan. Almost every industry uses data warehouses or data lakes (AWS data lakes for example) because these systems allow for the isolation of hidden patterns found in structured and unstructured data. Additionally, since advanced technology, such as artificial intelligence, usually requires a large amount of data, it often must be stored in data warehouses and data lakes.
Data Warehouses vs. Data Lakes
Data warehouses and data lakes are used for widely different purposes and are unique in their structure and flexibility. Data lakes serve to store raw, unstructured data that can be quickly analyzed by data scientists. The information stored is easy to access and update. Data warehouses, on the other hand, store large amounts of processed data that has been refined by data scientists. Business analysts structure this refined data in visuals such as charts, tables, spreadsheets and graphs, which makes it easier for the average person to interpret, understand and analyze, eliminating the need for any background knowledge in data science. Additionally, because this data has been refined, a smaller volume needs to be stored, so businesses can save on costly storage fees by deleting data that is no longer useful. The data that is kept in a data warehouse is actively used within the business for a specific purpose.
While data warehouses store processed data, data lakes are used for the storage and analysis of raw data. This requires them to store a much larger volume of data, increasing the amount of storage space needed. And unlike data warehouses, data lakes store information that is highly flexible and can be used for almost any purpose. Changing or modifying the information in data lakes is therefore cost-effective. In addition, the data stored usually does not serve an immediate purpose and, rather, is simply recorded in the system for potential future use. However, this means that data lakes tend to be less organized than data warehouses, which can make it difficult for a beginner to navigate and analyze the data.
Interpreting the information stored in a data lake often requires a data scientist armed with several tools. Data scientists gather data and simplify any problems in it in order to develop predictive models. In addition, they interpret the data using statistical analysis and, sometimes, machine learning. Alternatively, data warehouses can be used by anyone who is familiar with what the data represents. Since the data is structured, it can easily be turned into visual charts and graphs. Therefore, many business analysts use data warehouses daily.
There are several benefits and limitations of data warehouses and data lakes. Since they both have features that make them unique, many businesses often use both. Regardless of what data storage system a business uses, they are both instrumental in company growth and business strategy.
Benefits of Data Warehouses:
- Can be trusted for optimal data delivery due to the structured information it stores
- Well established, with many advanced tools available for analysis
- Flexible and accessible on the cloud
- Storage space is not wasted, since all data is structured and refined
Limitations of Data Warehouses:
- Must wait for each process component to be built before analyzing data sets
- Due to the limited nature of the structured data, it may be costly and/or difficult to manipulate
- Not designed for high volumes of data with several dimensions
Benefits of Data Lakes:
- Infinitely scalable and able to support high volumes of data with plenty of variety and velocity
- Data is highly accessible and inexpensive to alter, allowing users to quickly retrieve copies of data and its subsets for sharing
Limitations of Data Lakes:
- Usually requires a highly competent data scientist to gain any value from the data
- Higher volumes of data require more storage space and fees
- Works much more efficiently when stored on the cloud, as they often do not align with infrastructure on business premises
Data Lakes in AWS
The AWS cloud has the building blocks that help you implement a secure AWS data lake. The AWS solution automatically configures the AWS services to help you easily share, search, tag, and analyze specific data across an organization or with external users. The data lake solution deploys a console that users can browse and search datasets for their organization's needs. It also has a federated template that allows you to integrate the latest version of the solution with the Microsoft Active Directory. AWS data solutions are robust and popular, especially in 2021 with security and having clear and accessible data solutions is important to large organizations.
Businesses across North America are in need of data scientists to analyze data lakes and data warehouses. Kick-start your career as a data scientist by enrolling in a virtual training data science course from ExitCertified.