Databases, Data Lakes, and Data Warehouses Explained

Data storage

Big data is ruling the technology world today. To stay ahead in the competition, companies are leveraging their data to derive business value from it. However, a successful data analytics strategy depends on which technology one uses for storing, searching, analyzing, and reporting data. 

 

The three of the common data storage are databases, data warehouses and data lakes. But the question still remains, which one of these should a company choose? 

 

Therefore, it is important to understand what is a data lake, a database and a data warehouse? How do they differ from each other and how do they work? Which should a business choose based on its current data strategy, business goals, and infrastructure? 

 

As the first step towards this decision making, let us understand the basic concepts. 

 

What is a database? 

The database is a highly structured form of data storage where the data is stored in known formats and types, making it easier to access and manage. 

 

The widely used databases are a relational database or SQL database, and a non-relational database or NoSQL. The latest addition is the cloud database which is designed to run on cloud applications. 

 

The database is ideal for use cases like report creation for financial and sales data, analysis for smaller datasets, auditing of data entry, automating business processes and so on. Compatibility is another factor to consider when a requirement arises for a database to work with various programs. This may include converting data to different formats, thereby making it less ‘agile’ than other data storage types. 

 

Some examples of common databases are: 

PostgreSQL, MySQL, MongoDB, Microsoft Azure SQL DB, and Elasticsearch. 

 

 

What is a data warehouse? 

datawarehouse
Image credit: databricks.com

 

A data warehouse is a data management system that derives data from several storages and operational systems within an organisation and acts as a single point of access. It is designed for the enablement and support of data analytics and business intelligence. It can be thought of as a large central repository that provides users with current and historical data that supports decision making.

 

Due to these features, it can be called an enterprise’s single point of truth’. An important component of a data warehouse is an ETL (extract, transform, load) solution that helps in preparing data for analysis. This analysed data is then further used for analytics, reporting or data mining. 

 

Some of the popular data warehouses offered are Snowflake, Yellowbrick and Teradata. 

 

What is a data lake? 

Data Lake
Image credit: databricks.com

A data lake is a central location that stores a huge amount of disparate and unstructured data in its raw format until it is later required for processing. The raw data, captured from various sources like mobile apps, IoT devices, social media, business applications, is structured and its data format is derived at the time of analysis. A data lake is usually used to gather insight for data science research and testing, which is why it is also known as ‘for machines’ data storage type.  

 

Hadoop, Amazon S3 and Azure data lake are the most commonly used ones. 

 

Comparison between Databases vs Data Warehouse vs Data Lakes 

 

Now let’s do a quick comparison between these three storage types based on the 6 key areas: 

Key Area  Database  Data Warehouse  Data Lake 
Data types supported  Stores only structured data  Stores only structured data  Stores data in its raw format, whether structured, semi-structured or unstructured 
Processing  Follows schema on write, i.e., it requires a data model before loading any data  They also follow schema on write  Accepts data in its raw form during storage. It follows schema-on-read, i.e., create the schema when you require data for usage 
Storage costs  Flexible options ranging from cheap storage to expensive ones  A data warehouse has a low storage cost but high operational and maintenance cost  Data lake is highly accessible and designed to support low storage and operational cost 
Agility  Fixed structure makes it less agile to configure  Lacks agility and flexibility  Lack of structure makes it highly agile and flexible. Easy to configure data models, queries and applications 
Security  Databases are highly vulnerable to hackers as they are generally on the same server or same network as the application or website itself  In a data warehouse, the data sources are known which enables it to follow a sandbox approach and define user access,  making it highly secure  The biggest security challenge for the data lake is the lack of data quality which could result in a corrupted or a malware file getting stored in the data lake 

 

How to select the right data storage for your requirements? 

In order to make the right storage selection, you need to understand the key areas versus your requirements. 

  • Data sources and types: One of the deciding factors in selecting data storage is the number of data sources you have and the data type or formats in those sources. Another point to consider is whether their structure or schema is known ahead of time and is it consistent throughout. 
  • Data processing requirements: While selecting the data storage, one needs to keep in mind the type of data model included in the data management strategy. It is also crucial to understand when the data model or schema is defined; whether schema-on-read or schema-on-write. Unlike Data lakes, Databases and Data Warehouses require ETL processes to transform raw data into a pre-determined structure. 
  • Budget constraints: The business value that big data provides to enterprises is generally represented in their data management budgets. With data growing in terms of volume and velocity, the cost of storing it also increases. Thus, the storage option a company selects largely depends on its cost constraints. For instance, while data lakes are highly efficient, data warehouses take more storage for processing and are therefore costly. Databases, on the other hand, can scale up and down according to the business requirements 
  • Technology & data ecosystem: Organizations differ in their attitudes toward open source and proprietary software, as well as the communities that support them. Because of the broad deployment of Hadoop and the surge in unstructured data from multiple systems across the firm, as well as real-time data streams, data lakes have become increasingly popular. Another aspect of technology to ponder is the system’s accessibility and fidelity with the change in data sources and structures. For example, it is expensive to update a relational database and data warehouse, however, updating a data lake is straightforward. 
  • Data usage and users: Who uses the data and for what purpose is another factor that helps in deciding which data storage to select. 

 

 

The “database vs data warehouse vs data lake” debate has likely just begun. The differences in their structure, process, use, and agility make each of the data storage unique. However, despite the differences, these three data storage types share the same goal i.e. centralizing data into a single location that can be further used for uncovering data insights and for decision making.  

By considering the key areas and evaluating them based on the factors stated above, you can successfully select the right data storage for your enterprise’s business and operational needs. 

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