The world is driven by data, organizations are submerged in data, and it is the call of data everywhere!

Data storage is a major task, especially for organizations handling large amounts of data. For businesses garnering optimal value from big data, you hear about data storage terminologies – ‘Databases’, ‘Data warehouses’, and ‘Data lakes’. They all sound similar. But they aren’t! Each of them has its own unique characteristics that make them stand apart in the data landscape.

Through this detailed article, we attempt to introduce the three terminologies, their salient features and how different are they from each other. Hope it throws some light into how data can be managed and stored with databases, data lakes, and data warehouses.

What is a Data Warehouse?

In computing, a data warehouse (DW or DWH), also known as an enterprise data warehouse (EDW), is a system used for reporting and data analysis and is considered a core component of business intelligence.

A data warehouse is majorly a huge database that is leveraged for large-scale data analytics. They encompass many records that come from disparate sources to be centralized into a uniform location and then help data scientists/business analysts/users in performing analysis on the consolidated data, through data analytics and reporting tools. A data warehouse, being the core analytics system, is competent enough to offer insightful analytics for the organization.

Since a data warehouse makes use of transformed data, it is fit for detailed analytics. It makes use of tables, indexes, views, keys, and data types to organize data for the generation of reports and dashboards. It acts as a strong foundation for business intelligence activities, making organizations more productive in take data-driven and insightful decisions. It stores a huge amount of data that could consist of raw ingested data to perfectly curated and filtered data.

The data is then processed through relevant ETL/ELT processes to be moved to the data warehouse. Usually, a data warehouse has a fixed and pre-defined relational schema and hence it gels well with structured or semi-structured data. It is preferred when there is a need to store a large amount of historical data on which detailed analytics is to be performed. 

Key Features of Data Warehouse

• Stores and manages large quantities of historical data
• Compatible with Online analytical processing (OLAP) and BI tools
• ETL support with integrated data
• Ability to garner insightful information through dashboards/reports
• Data cleansing and transformation

What is a Data Lake?

A data lake is a system or repository of data stored in its natural/raw format, usually object blobs or files.

A data lake stores structured, semi-structured, and unstructured data without the need to process it at that time. This gives users the flexibility to handle all kinds of data but at the same time, does not guarantee the validity of data. It is best fit for big data cases where data is to be included from disparate sources. It helps users manage complicated BI scenarios and extracts the optimal information from them.

A data lake is turning out popular in the modern world since it is flexible and cost-effective. It stores any type of data be it images, PDFs, lists, videos, etc. Just like the data warehouse, it analyses information and extracts the output in the form of insightful reports and dashboards. It is also used in synchronization with machine learning algorithms garnering complex outputs. It does need programming skills and knowledge of data science techniques.

The main users of a data lake are data scientists and engineers who wish to research and test huge volumes of data. It is ideal for dumping data till further processing is to be done and hence is more flexible. It stores data in a range of formats like CSV, JSON, TSV, BSON, ORC, etc. Data need not be transformed for getting added to the data lake. It is a cost-effective way to store huge amounts of data.

Key Features of Data Lakes

• Supports all types of data
• Flexible, scalable, and compatible
• Leverages machine learning for further processing
• Archives operational data
• Supports the ELT process

What is a Database?

In computing, a database is an organized collection of data (also known as a data store) stored and accessed electronically through the use of a database management system.

A database is a single-purpose repository of transactional data that is used to capture a specified situation. It could be structured, unstructured, relational, RDBMS, or NoSQL. Data that comes to a database is processed, managed, organized, and then stored in different tables, as needed. It performs online transaction processing since it is closely linked with transactions.

A database is optimized for accessing and retrieving data easily. It has a single source of information for each element and hence is ideal for smaller transactions not heavy or bulky processing. It is simple to create and with the help of SQL, queries or reports can be generated. It can be open source or proprietary and hence it is easy to install or use for data analytics.

As a singular storage location, a database can host several tables for a particular project, but it is tough to have data from multiple projects. It can be availed electronically with the support of online transaction processing. The type of storage of data could differ based on the type of database, it could be a relational database or a non-relational database. Relational databases stores data in fixed rows and columns, and non-relational stores in different models like JSON, BSON, etc.

Key Features of Databases

• Flexible data storage
• Structured according to requirements
• Indexes to optimize query performance
• Supports CRUD – create, read, update, delete
• Transaction and concurrency control

Comparing Data Warehouses Vs Data Lakes Vs Databases – The Key Differences

All three major data storage types – data warehouse, data lakes, and databases have one common objective – centralizing data in a uniform place to empower businesses to analyze and garner insight. But they have their own differences too, here are they:

Till Later

It must have been clearer by now what databases; data warehouses and data lakes signify. Overall, it depends upon organizational requirements while finalizing the data storage type. It may even be possible that businesses may choose a merger of two choices. Some of the key considerations while concluding are structured/unstructured/semi-structured data, data processing and storage needs, budget estimates, target audience, technology exposure, etc.

Taking support from an experienced IT service provider can term advantageous and profitable for organizations. Ridgeant’s data warehouse services include consultation, implementation, migration, and managed services to help organizations consolidate data in efficient DWH solutions.

Our data warehouse consultants help you build, design, and implement a scalable and performant data warehouse. We understand your data needs and design an effective data warehouse strategy that includes choosing the best model, building a complete DWH solution, and performance optimization.

Our skilled data engineers design, build and implement data lakes that meet your enterprise needs. Get benefited from a centralized data repository that acts as a foundation for collecting unstructured, semi-structured, and structured data. Create a data foundation that facilitates data ingestion, data cleaning, extraction, and discovery.

We develop data architecture and maintain it to collect, store, and analyze data at scale efficiently and effectively. Our data analytics services help enterprises unlock newer insights that fuel faster and better decision-making. Contact us for any kind of data-related requirement and we will be all set to assist you.