What You Should Know About Data Lake and Data Warehouse

person using MacBook ProData serves as both the new oil and the industry’s lifeblood. Data should be safeguarded, maintained, and priced correctly just like any other corporate asset. Businesses frequently struggle to comprehend all the advantages that sound data management delivers, which costs them their competitive edge. 

Today, we review the key benefits of the data lake and data warehouses and their differences in characteristics. The article gives you a basic understanding of how it uses and in which cases it is better to accomplish, or which is better for your business. 

A data warehouse commonly referred to as an enterprise data warehouse (EDW), is a platform used in computers for reporting and data analysis. It is recognized as a crucial component of corporate intelligence. The integrated data from many sources is stored in DWs, which act as a central repository. They save both recent and old data in a single location that is utilized to provide analytical reports for employees throughout the whole company. 

The warehouse’s data is uploaded by the operating systems (such as marketing or sales). Before being utilized in the DW for reporting, the data may go via operational data storage and require data cleansing for extra activities to assure data quality. 

The following advantages of a data warehouse: 

  • Produces improved business intelligence. Decision-makers won’t have to rely on scant information or their gut feeling since they will have access to information from several sources through a single platform. Furthermore, data warehouses may be utilized in a variety of corporate operations, including segmenting the market, advertising, risk management, inventory control, and budget reporting. 

  • Cut down the time. A data warehouse centralizes and integrates data from many sources by standardizing, preserving, and storing it. All users have access to crucial information, enabling them to make well-informed choices on important factors. 

  • Optimizes the quality and accuracy of data. Data from many sources is transformed into a standard format in a data warehouse. 

  • Delivers a significant return on investment (ROI). Companies with a data warehouse receive more sales and cost savings than those without one. 

  • Offers a competitive edge. Data warehouses give businesses a competitive edge by assisting them in gaining a comprehensive understanding of their present situation and assessing possibilities and risks. 

A data lake is a system or repository where data is kept in its original/raw form, typically as files or object blobs. A data lake is often a single repository for converted data needed for functions like reporting, visualization, advanced analytics, and machine learning. It may also include raw copies of source system data, sensor data, social media data, etc. A data lake can contain binary data, semi-structured data (CSV, logs, XML, JSON), unstructured data (emails, documents, and PDFs), and structured data from relational databases (rows and columns) (images, audio, video). 

The following capabilities are offered by this model for use: 

  • Easiness of data storage. Data modeling is not necessary at the moment of storing the data since a data lake may consume all types of data. We can accomplish this when looking for and examining data for further analysis. As a result, whenever the necessity arises, we can filter and model them. 

  • Scalability. When comparing it to a traditional data warehouse, it offers scalability and is cost-effective. 

  • Versatility. A data lake may hold multi-structured data from various sources. To put it simply, a data lake may hold logs, XML, video, sensor data, binary, social data, chat, people data, and more. 

  • Flexibility. The data must be in a certain format to be used with traditional schema. While typical data warehouse solutions are schema-based, data lakes enable you to be schema-free or specify numerous schemas for the same data, which is good for analytics. These platforms include Hadoop, Databricks, Google BigQuery, Snowflake, and others. 

  • Different formats. While typical data-warehouse technology primarily supports SQL, which is appropriate for simple analytics, data lakes provide a variety of alternatives and language support. 

Data Warehouse vs Data Lake 


Data Warehouse 

Data Lake 

Relational information gleaned from operational databases, transactional systems, and business-related software 

All information, whether it is structured, semi-structured, or unstructured 


Most rapid query outcomes using local storage 

Utilizing inexpensive storage and isolating compute from storage, query responses are produced more quickly 


The business arrangement, data analyst, and data engineer 

Business arrangement (using curated data), data analysts, data developers, data engineers, and data architects 

Data quality 

Data that has been carefully selected to represent the main version of reality 

Any information, whether or not it has been curated (i.e. raw data) 


BI, graphics, and batch reporting 

Big data, streaming, operational analytics, exploratory analytics, data discovery, and profiling 

Building a Data Warehouse or Data Lake and Needing a Hand?  

Agiliway is an Eastern European software development outsourcing and consulting company with extensive tech expertise in domains including big data analytics, data science, AI & ML, cloud solutions, business process outsourcing, CRM Implementation Services, and more. Teams of Agiliway software developers work to provide solutions for companies in a variety of sectors, including finance, e-learning, retail, media, healthcare, and others. We assist a large number of clients in maximizing their data lakes and warehouses, managing their big data, enhancing business intelligence, and making the most of everything.