Data Lakes and Data Warehouse: Their Role in Data Processing

Data lakes and data warehouses both are the terms that people use in the data processing.
In this article, we explain their concepts, their benefits, and differences, as well as what is their role in data processing.

What Is a Data Lake?

Data Lake is a repository of large quantities in varieties of data. It includes things like unstructured data, semi-structured data, and structured data at any scale.

So, what does that mean?

Data Lake is a place to save a large variety of data in any file format. You can also store your data without having any structured data and can execute different data processing and data analytics from visualization and dashboards to real-time big data processing, machine learning, and real-time analytics. There are many processes within a Data Lake. They include data cleansing, data quality checking, and data security, to name but a few.

What Is a Data Warehouse?

Data Warehouse is a special kind of database that entails historical, customers, and analytical data. The Data Warehouse helps to build a large report and provides the business value to the company. It also provides efficiency to the data scientists and data analysis. In the Data Warehouse, relations are used by “star schema”. The Warehouse uses “schema-on-write” to process the data.

Let’s understand both of the schemas.

Star Schema

With Star Schema, the tables are divided into facts. In dimension tables, a fact contains numerical values or measures and obtains the primary key and contains the primary keys of the dimension tables. Similarly, dimension tables contain primary keys with their attributes of the dimension.

But, there is a limitation to what a Data Warehouse can do in terms of how the schema is not schema ties. Information is a structure in the data warehouse using this schema.

Start the Schema Image.

Schema-on-write: This is a process where the data schema is defined first before the data is stored in the tables in the database for further use. There is a limitation though, on changes businesses can make the data datasets because there is the involvement of foreign and primary key in all the tables. So, for instance, another attribute has to be added to the table. It would consume a lot of changes and is too much time-consuming.

The key concept of Data Lake

We have a store that is nothing but the data lake store. In it, you store practically unstructured data, structured or semi-structured data. Unstructured data is nothing, as it does not have any structure. Semi-structured data probably has JSON data and XML data. Structured data is nothing but relational data.

Data Lake is built on top of open-source technology called web HDFS. It has a really good interface to communicate with the data which is stored in your data lake store. On top of the data lake, which is called analytics, it can further be called ADLA (Azure Data Lake Analytics). It helps in big data jobs in ADLA, for example, U-SQL (Analytics Service) job or a Spark job. Or, you can also provision Hadoop clusters which are called HD insights.

What analytics service is and cluster (HD Insight)?

Analytic service is nothing but the job as a service and using HD insight is nothing but cluster as a service. In cluster as a service, you need to take care of individual clusters and you need to also maintain the clusters and patch them. In addition to writing your own code to fetch big data, you basically need to do other stuff as well. On the other hand, in a job as a service, you only think about submitting the job and the code that basically does aggregation, filtering, and so on.

The architecture of Data Lake

The figure defines the complete architecture of a Data Lake. The lower levels define data that is at rest period whereas the upper levels define the real transactional data. There are six major tiers as follows:

Ingestion Tier:

This Ingestion tier depicts the database sources. In this stage, data gets loaded in batches like Micro batch, Mega batch, and real-time bach.

Insights Tier:

This tier defines the research side of batches. Insight system is used for NoSQL, SQL queries, and SQL MapReduce for data analysis.


It stands for Hadoop Distributed File System. This is a cost-effective part of data lake architecture that provides both unstructured and structured data. It is also known as a rest zone for all generated data.

Distillation Tier:

It takes all of the information from the data storage and moves further to structured data for data analysis.

Processing Tier:

This processing tier runs user’s queries and analytical algorithms with varying interactive, real-time, and batch to generate data for data analysis. It picks up data from both In-memory and MPP memory (Massively Parallel Processing).

Unified Operations Tier:

This tier defines system monitoring and system management. It includes auditing and policy management services, data management services, proficiency management, and workflow management.

The Architecture of Data Warehouse

A data warehouse is an RDBMS (Relational Database Management System), that is well developed for query analysis. A data warehouse maintains huge data in the database to make the changes in historical analysis. Below is the three-tier architecture model of the data warehouse. This three-tier architecture contains three layers as follows.

Bottom-Tier Layer

The bottom tier layer contains all of the database servers in the data warehouse which relates to the relational database system. Data is extracted from external and operational databases with some backend tools. Then, the data is fed into the database of the warehouse by performing ETL process (Extract, Transform, and Loading). Basically, we use the ETL process to load data into the bottom layer of the data warehouse architecture model.

Middle-Tier Layer

In the middle-tier, there is an OLAP server that works as a single processing combination of ROLAP and MOLAP database systems. This OLAP server provides the multidimensional way to get the data model. For this reason, analysts and managers can provide consistent and fastest way to get the information.

ROLAP stands for Relational On-Line Analytical Process that manipulates the stored data in the relational database. ROLAP model can easily maintain large data. MOLAP stands for Multidimensional OLAP system that maintains dynamic multidimensional operations and the data. Data can be stored in the multidimensional cube. It provides the fastest data retrieval due to MOLAP cubes. The whole structure of the middle layer is to represent the abstracted view of the data warehouse. As a result, this layer contains common behavior between database and end-user.

Top-Tier Layer

Top-Tier is a front-end client-side layer in a three-tier model of the data warehouse. This contains reporting & query tools, data mining & data analysis tools. Here, reporting tools contain report writers & productive reporting tools. Analysis tools are also used to make charts based on the data mining result. Data mining tools define especially relevant information from the hidden patterns. Now, we have completed the three-architecture model of the data warehouse.

Difference between Data Lake and Data Warehouse

Data Warehouse Data Lake
DW is a structured process Data Lake is not structured, but it is kind of semi-structured or raw unstructured
It works on Schema-on-write while processing It works on Schema-on-read while data processing
Expensive for big data volumes It contains low-cost storage
Less agile and fixed-configuration Highly agile and can configure or reconfigure as required
Data warehouse users can be business professionals Data Lake users can be data scientists
Data types are cleansed and structured Data types are Raw and unstructured
DW provides rationalization of big data from different sources in a single data enterprise. A data lake provides parallelization of multiple programming languages like as Perl, C++, and Java for storage of unstructured and structured data.
A schema is defined just before the data is completely stored. A schema is defined just after the data is completely stored.
The data warehouse needs to work at the beginning of data processing. Data lake needs to work at the end of the data processing.
Data warehouse offers integration, security, and performance, etc. Data lake offers ease of data capture and provides robust agility.
DW can map large datasets. DL can map extreme datasets.
A data warehouse is very time consuming while developing new content. It provides fast ingestion while introducing new content or data.
Data warehouse requires limited flexibility in tools while using SQL. Data Lake requires flexibility in tools but it can be mapped with advanced analytics and open source.
Data at the summary and an aggregated level of detail Data at a low level of granularity or levels.
Tight SLAs with production schedules Loosely defined SLAs.

Benefits of Data Lakes and Data Warehouse

Below are the benefits of using data lakes and data warehouses.

Data Lake: Benefits

  • Helps fully with product ionizing & advanced analytics,
  • Offers cost-effective scalability and flexibility,
  • Offers value from unlimited data types,
  • Reduces long-term cost of ownership,
  • Allows economic storage of files,
  • Quickly adaptable to changes.

The main advantage of Data Lake is the centralization of different content sources

Users from various departments that are scattered around the globe can have flexible access to the data in a data lake.

Data Warehouse: Benefits

  • Data warehousing saves a lot of time and keeps high-quality data,
  • It has better enterprise intelligence and potentially high returns on investments,
  • It increases system and query performance,
  • Data warehouse contains better customer service and solution,
  • A data warehouse can also deliver advanced Business Intelligence and provide historical intelligence.

Classification Difference Between Data Lake and Data Warehouse

Let’s have a look into few key points to present classification differences between the Data warehouse and Data lake as follows


Data Lakes retain and embrace all kinds of data like texts, relevant or irrelevant, sensor data, images, unstructured or structured, etc. On the other hand, a data warehouse is quite invincible and only store processed and structured data. In the development stage of the data warehouse, every decision is made on the ground level of which data management sources are to be used and which business management process is important.

On another hand, A data Lake comes with a business’ end-users to analyze data with all types of data modeling and data transformations before allowing a new schema.


Data lakes are very useful for all end-users who build the data to get access to the reports and later analyze them for generating actionable insights. Data lake users can be data scientists or data miners who do an in-depth and in-memory analysis of generated data by mixing up all types of data, extracted data from all types of sources- to develop new answers to all the queries.

A data warehouse provides all types of business professionals who can use it as the data source and later can access the data source for data analysis. Hence, a data warehouse measures predefined business requirements.


Data cost is another important aspect when it comes to data storage. In Data Lake, data storage is comparatively lower than the data warehouse because the data warehouse only deals with high data volume. Hence, the data warehouse contains high-cost storage and the data lake contains low-cost storage.


I hope you enjoyed this article. Data lake and data warehouse both are very beneficial for mid-late repositories and much more crucial for businesses that use analytics.

Go with your current needs for the data source, but let me inform you if you contain an operative data warehouse, then go for a data lake for marketing and data enterprise. You can use the data lake like an archive storage approach and let your all business end-users access the stored data. Hence, it is not worth to compare between both data warehouse and data lake, because their use depends on the business requirement.

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