A Brief Comparison of Database, Data Warehouse, Data Mart and Data Lake and these services in Azure.

Every organization needs to process data. Choosing whether, a data mart, data warehouse, database, or data lake is the best option for your organization will depend on the type of data, its scope, and how it will be used. 

In this article we will discuss the key differences between a database, a data warehouse, data mart and a data lake. Database is a storage used to capture data. There are two type of database which are relational database and No-SQL database (non-relational database or unstructured data). A relational database can capture and store data via an OLTP process which stands for online transactional process, so when a company completes a transaction and sells an item it will record that within a database and that data can be live real-time data. Data in this database is going to be stored in tables which has columns and rows, and this will be highly detailed which means you're going to be able to go in and see every single aspect of the data and databases also have a flexible schema which means you can go in there and kind of change things. A data warehouse is also a database, but it is used for analytical processing or OLAP. OLAP stands for online analytical processing, and it's created to basically analyze huge amounts of data. From databases, data is aggregated and sent to data warehouse via an ETL (extract transform and Load) process which is where it extracts the data and transforms and loads it exactly how they need it in this data warehouse and that's how data is put into the data warehouse it isn't getting it directly from the source but it's being put into a database and via the ETL process is being updated as it goes or whenever the ETL process runs a data warehouse will always have the historical data but it won't always have the current data unless the ETL process is running every single day or very frequently  into the data in the data warehouse.  

Data lake was basically designed to capture any type of data. It could be a video, a picture, an image, a document, a graph and  anything you could imagine that you would want to put in a database or store in some way you can store it in a data lake now there are a tons of use cases for a data lake for people who work with machine learning and AI get to use it or benefit from it the most they can use all that structured and unstructured data and create models to really use it in its raw form. The data can be used for analytical purposes, typically you are going to have to clean it up a little bit and do a little bit more work to make it usable. 

In Azure, you can find various data-related services, including databases, data warehouses, data lake and data mart. The overview of each data services is as follows: 

Database

 A database is a structured collection of data organized in a way that makes it easy to manage, access, and update. It is a fundamental component of most applications and systems. 

• In Azure: Azure offers a range of database services including Azure SQL Database (a fully managed relational database service), Azure Cosmos DB (a globally distributed, multi-model database service), Azure Database for PostgreSQL, MySQL, MariaDB, and more. 

Data Warehouse

A data warehouse is a large-scale storage system designed for analysis and reporting. It's optimized for querying and reporting on large volumes of data rather than transaction processing. 

• In Azure: Azure Synapse Analytics is a powerful analytics platform that includes an integrated data platform (formerly Azure SQL Data Warehouse) which allows you to ingest, prepare, manage, and serve data for business intelligence and data science needs. 

Data Mart

 A data mart is a subset of a data warehouse. It contains a specific slice of data focused on a particular business function or team within an organization. Data marts are often used to provide specialized views into data for specific departments or groups. 

• In Azure: In Azure, you can implement a data mart using the capabilities of Azure Synapse Analytics or by leveraging Azure Analysis Services, which provides enterprise-grade analytics services. 

Data Lake

 A data lake is a storage repository that can store vast amounts of raw data in its native format. Unlike a data warehouse, which requires data to be structured before storing, a data lake allows you to store data in its raw form and apply structure as needed when performing analysis. It can contain Images, Tables, Files etc.

• In Azure: Azure Data Lake Storage (ADLS) is a scalable and secure data lake that allows organizations to store and analyze large amounts of data. It integrates with a range of Azure services for data processing, analytics, and machine learning. 

Purposes of these services in the data ecosystem: 

• Databases are used for transactional operations and structured data storage. 

• Data Warehouses are optimized for analytical queries and reporting on structured data. 
• Data Lakes are made to store large amounts of raw, unstructured or semi-structured data in its raw or unprocessed form for later analysis. 
• Data Mart: In Azure, you can create data marts using Azure Synapse Analytics by segmenting the data warehouse into specific subsets. 

Let's compare them in more detail:

Data Structure: 

• Database: For structured data with predefined schemas. 
• Data Warehouse: Works best with structured data but can also handle some semi-structured data. 
• Data Lake: Mostly for the storage of unstructured, raw, or semi-structured data. Because the data is schema-on-read, you can apply a schema as you query the information. 
• Data Mart: Scales based on the underlying data warehouse technology. 

Use Cases: 

• Database: Applications for managing transactions, structured data, and OLTP (Online Transaction Processing). 
• Data Warehouse: Complex querying, analytics, reporting, OLAP (Online Analytical Processing). 
• Data Lake: Handles big data processing, data exploration, machine learning, handling large volumes of raw data. 
• Data Mart: Business unit-specific reporting and analytics, focused on a particular area or function. 

Scalability: 

• Database: Scales vertically (upgrading resources of a single machine). 

• Data Warehouse: Scales horizontally by adding more resources to a cluster. 
• Data Lake: Scales horizontally by adding more storage. 
• Data Mart: Scales based on the underlying data warehouse technology. 

Cost: 

• Database: Typically, it is more cost-effective for transactional workloads. 
• Data Warehouse: Can be more expensive, especially for large-scale analytics workloads. 

• Data Lake: Cost-effective for storing huge amounts of raw data, however processing could cost more. 
• Data Mart: Cost depends on the scale and complexity of the underlying data warehouse. 

Query Performance: 

• Database: Enhanced for quick, low-latency queries on structured data. 
• Data Warehouse: Perfect for complex analytics queries on large datasets. 
• Data Lake: Due to its schema-on-read design, it can be slower for ad hoc queries but excels at managing enormous amounts of data. 
• Data Mart: Performance depends on how it's designed and implemented within the data warehouse. 

Security and Compliance: 

• Databases, data warehouses, and data lakes are just a few of the Azure services that offer a variety of security features and compliance choices. Azure offers capabilities for compliance reporting, auditing, and access control. 

For working with various data components, Azure offers a wide selection of tools and services, such as Azure Data Factory for data orchestration, Azure Databricks for big data processing, Azure Stream Analytics for processing data in real-time, and Power BI for visualization and reporting. To further assist you in efficiently managing and securing your data, Azure provides a number of security and compliance tools. 

Please watch out for my upcoming post where I would like to discuss about each of this Azure Services in more details.

Thank you for Reading!