In today’s fast-paced and technology-driven world, data has become a critical asset. 90% of the world’s data was generated within the last two years. This sudden surge in data production has created a major need for data storage. Data lakes, data lake houses, and data warehouses are some of the formats of data storage that Data Architecture specialists are familiar with. These storage architectures aid in democratizing the use of data within an organization. All of these formats have their own set of distinctions and specifications, which will be explained in this blog. 

Data Lake: A Versatile Repository 

Data lakes are versatile centralized data storage used mostly in advanced analytics and machine learning. They are used popularly due to their ability to handle a wide variety of data types and formats. Data lake was created to store data in its raw form and to accept data from various sources. Data lake was initially designed to solve certain constraints that were observed while working with data warehouses. Here are some significant features of data lakes: 

  • Data lakes support both structured and unstructured data  
  • Data lakes are flexible and scalable since they store data without a unified schema. 
  • Data lakes can manage data volumes larger than one petabyte. 
  • All types of data can be retained in a data lake indefinitely. 

Data Lakehouse: A Centralized Data Repository 

Data lakehouse is a data storage format that was created to handle the challenges created by using data warehouses and data lakes. Even though data lakes don’t enforce a schema, deriving insights from the stored data proved to be difficult. Therefore, data lakehouses were designed as a new approach to data management with the best aspects of both data warehouses and data lakes. Data lakehouse is a unified platform that can integrate structured, unstructured, and semi-structured data into centralized storage. There are significant advantages to using data lakehouses, they are; 

  • Data lakehouses have the benefits of both data lakes and data warehouses without limitations. 
  • Data lakehouses aid in the quick analysis of large data in real-time. 
  • Data lakehouses are well-known for maintaining data integrity and security 
  • Data lakehouses have higher scalability and flexibility.

Data Warehouse: An Analytical Data Repository 

Data Warehouses were designed to store data to support the decision-making process of organizations. Data warehouses don’t just store data, they also structure it. Data warehouses are considered a good storage option when the stored data consists of pre-processed data which is required for queries. Data warehouses have been proven to improve the productivity of professionals like data analysts.  Here are some definite advantages of using data warehouses; 

  • The data quality and consistency are enhanced when using data warehouses. 
  • Data warehouses facilitate and streamline the flow of information within an organization. 
  •  The application process of data warehouses is known to be easy. 

Data Mesh: A Decentralized Data Architecture 

Comparatively, data mesh is a new data architectural approach in the works which was designed specifically to handle the challenges of data management faced in complex and large organizations. In large organizations, the notion of a single team handling all the centralized data has proven to be inefficient. This served as a reason for the creation of data mesh which proposes a decentralized approach towards data. In data mesh, data is owned and managed by the teams which produce the data. Which indicates that each team is responsible for managing its data. This allows the teams of the organization to be responsive and agile to their needs. Data mesh boasts a lot of advantages which include; 

  • Data mesh acts as a self-service platform for individual teams. 
  • Data mesh maintains data integrity and data security while providing flexibility to the users. 
  • Data mesh improves the speed of data delivery. 
  • Data mesh improves the quality of data which results in increased innovation. 

Tech-stack to implement data warehouses, data lakes, and data lakehouses. 

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Data Storage Type Purpose  
Data warehouse 
Oracle, SQL, server, My SQL, etc. Were used in the early days.

Teradata, IBM Netezza, Oracle Exadata, etc. were popular later.
Data Extraction, transformation, and loading to data warehouses (ETL) 
Pro C, Perl, etc. were used in the initial days. 
Informatica, Data Stage, SSIS, Abinito, etc. used later

Data Lake



Hadoop 2.2 released in 2013 was popularly used in the initial stages 
Later, Simple storage service (S3), Azure data lake storage (ADLS), and Google Cloud Storage (GCS) were used

 Data Extraction, transformation, and loading to data warehouses (ETL) 
Squoop, Flume,  NiFi, Kafka, etc. were used to extract and load data 
New versions of tools like Informatica Big Data Edition, Talented Big Data Edition, etc. were used to support Hadoop and other public cloud services
Cloud Native ETL and data processing services were used in the management

Data lakehouse 


Raw layer same as data lakes 
Hive is used to manage structured data
Management process supported by AWS, Redshift, Azure Synapse, and Google Bigquery.
  Data processing and transformation from raw layer to processed layer  
Hive Query language through SQL is used in processing. 
Big data edition of tools was used to support Hadoop and public cloud services

Hoard Data and Relish Every Byte  

At the end of the day, data lake, data lakehouse, data warehouse, and data mesh are data storage innovations that focus on increasing productivity and getting the most use out of existing data. Data lakehouse and data mesh are comparatively the latest approaches to data management. However, choosing the perfect data management approach is a herculean task on its own. But there exists an easy way out and that is data analytical consulting services that help organizations uncover potential and suggest intelligent data management systems tailor-made to the organization’s requirements.

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