In the new era of big data, maintaining data is one of the most crucial parts of a company. The method of storing, processing, and retrieving data is equally as important. Data could be a type of structured, unstructured, or semi-structured. However, there are many different ways to store your company’s data these days. Typically, the choice boils down to data lake vs data warehouse.

Now, a data warehouse is a structured and complex storage space for data. While the daily transactional data of a business is stored in the database, a data warehouse is more of an archive of sorts. This means that a data warehouse will store all the records from a business and form the “single source of truth” for a company’s data. An important use of data warehouses is for analytics, reporting, and other Business Intelligence (BI) purposes. On the other hand, a data lake is a lot less structured. Within a data lake, raw data is stored in whatever form it may be. This means that data lakes can be used to store large amounts of data in various forms – from files, images, tables, audio, videos, and any size of large datasets. Now that we know what these two storage solutions are, let’s focus on data within a business.

Data In a Business

In a business, the data generated comes from the transactions made. The sales, customers, products, and partners will all generate data over time. For the company to progress, that data needs to be managed properly.

Data Lake vs. Data Warehouse

Data management is the collecting, processing, storing, and protecting of data. The data also needs to be quickly accessible to the right people while still secure enough to not get into the wrong hands. Businesses will then use this carefully curated and accumulated data to make informed decisions for the company. The records of transactions can guide advertising, product lines, or policies within the business.

To manage and store data effectively, a business can make use of:

  • Data Governance – These are the tools and practices that are used to manage the way data is organized, protected, and stored. This ensures quality analytics and data retrieval later on.
  • Business Intelligence (BI)These BI tools are essential for large amounts of data and help to sort and structure the data for improved analysis.
  • Data Integration – This will help to compare and consolidate relevant data to form a better picture for analysis – even if the data is in different formats.

Data storage is a constantly fluctuating asset for a business. This means that your company needs to have a storage system that is flexible and scalable. Cloud infrastructure offers these qualities and more. As a business generates data daily through transactions, correspondence, and other files. As a result, the data storage solution needs to accommodate the expanding amount of data.

Flexibility and scalability in data storage also present multiple other benefits, including:

  • Faster data retrieval rates
  • Improved security
  • Cost efficiency
  • Data backup

What is the difference between Data Lake vs Data Warehouse?

All companies are different and that means that their data storage needs will be different as well. While some might choose the structured data warehouse solution, others might opt for the less daunting data lake instead. Alternatively, some companies can invest in a Data Lakehouse. This is essentially a combination of the two options that include the low storage costs and broad data access of a data lake with the data structures and management features of a data warehouse.

Some of the key differences between a data lake and a data warehouse can be seen below:

Characteristics Data Lake Data Warehouse
Data Storage Unstructured raw data from IoT devices, websites, applications, and social media Structured data from transactional systems, operational databases, and business applications
Availability Data is available for use much faster by keeping it in a raw state Complete data available quickly for analysis
Cost low-cost storage and operations higher costs for storage and operations
Performance Fast query results Fastest query results
Processing ETL (Extract. Load. Transform) is taken directly from the source and structured when needed ETL as well but this time structured and prepared immediately for analysis
Data quality Any data that may or may not be curated – such as raw data Highly curated data.
Users Due to its raw data, is typically used by data scientists, developers, and engineers Used by managers and business analysts who require structured and detailed data
Schema Defined after data is stored which makes capturing and storing the data faster Defined before data is stored which takes longer.
Analysis A broader range of data can be analyzed in new ways for Machine Learning, BI, predictive analytics, data discovery, and profiling Unified and structured data offers a single source for businesses to reference through batch reporting, BI, and data visualizations


Data Lake vs. Data Warehouse: Which to Choose?

So, now that you know the main differences between a data lake and a data warehouse, which solution should you choose? The answer is the type of business requirements you have.

Data Lakes can be more flexible in the type of data they store. Data lakes are also ideal for cloud deployment. This is usually because the cloud speeds up availability and offers improved security and elasticity. Data Lakes store huge structured, semi-structured, and unstructured data. They can be used to run in-depth analyses of broad-spectrum data gathered over a longer period. Examples of Data Lake use in different industries include:

  • Educational sectors: Large amounts of data from students in different forms can be stored to track attendance, grades, and more. It can also be used to predict academic trends for specific students. Students assignments are submitted in various forms such as files, videos, images etc. That all can be stored in a Data Lake.
  • Healthcare sectors: Within the medical field, various types of are generated – including visual data, different reports, PHR – Personal Health Record, EMR – Electronic Medical Record, EHR – Electronic Health Records etc. Data lakes offer an expandable storage solution that’s helpful for research, diagnostics, and insurance companies.
  • Transportation sector: Maintenance, trend predictions, and improvements can be made from the collected data of different vehicles.

Some examples of Data Warehouse use in different industries include:

  • Banking and finance sector: Given that these institutions are already structured and complex, data warehouses ensure structured data access by the entire company.
  • Public sectors: The data generated can be used to maintain and analyze tax records, health policies, and more. This helps these agencies to build individual profiles and group relevant records.
  • Hospitality industries: Data warehouses can help this sector to create targeted advertising and promotions based on the curated data.

Sangfor offers Data Lake, Data Warehouse for any kind of large data storage requirements for enterprises. Visit Sangfor aStor page to know more or contact us for more details. 

Business Intelligence and Application Development

Application development is the process of creating new computer programs that help a business process information faster and function better. App development does, however, involve:

  • gathering requirements
  • designing prototypes
  • testing
  • implementation
  • integration

As such, businesses need to have the right space for these processes to take place. Data lakes and data warehouses can both act as a foundation for app development in this sense. Application development values agility and scalability. This is because the process needs efficiency and space to properly develop its applications. Data lakes and data warehouses can be ideal for this function in app development. Sangfor’s Hyper-Converged Infrastructure (HCI) provides a robust infrastructure for these purposes. Some of the advantages of using a data lake or data warehouse in application development include:

  • Improved visibility
  • Enhanced security
  • Cost-effectiveness
  • Agility and Scalability
  • Informed decision making
  • Expanded access

Data Warehouses for Business Intelligence (BI)

Business Intelligence – or BI – is a framework of standards and policies used for storing data. Data warehousing is essentially a component of BI architecture in this way. Data integration helps to reduce the amount of data that needs to be processed in the data warehouse while data quality is crucial to ensure the validity and authenticity of the data being stored. A data warehouse also benefits a business by allowing it to make informed and precise decisions through:

  • Using high-level reporting and analysis.
  • Categorizing customers based on past transactions.
  • Providing tailored content or products.
  • Analyzing trends in customer sales, operational processes, and day-to-day activities
  • Predicting future trends based on historical data.
  • Creating a business plan for the next quarter of the business year.

Data warehouses are also ideal for the usage of analytics and reporting tools. A data warehouse will structure and store a company’s data into neat and easily accessible batches for analytics tools. In all these ways, a data warehouse contributes to the Business Intelligence (BI) capabilities of a company. Sangfor offers premium platforms and products that simplify cybersecurity, cloud, and IT infrastructure affordably and innovatively. For more information on Sangfor’s cyber security and cloud computing solutions, visit


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