Data Warehouse, Data Lakes, Data Marts: The difference and Intersection
Unarguably, the new age technology has been dubbed the 4th industrial revolution. It’s ravishing how advanced these technologies are and how much more revolutionary technology can accomplish.
Structured data/databases like the Data warehouse, Data marts & Data lakes play a crucial role in technological advancement. Also, they offer cutting-edge precision for drawing inferences and strategically implementing trends to organizations or companies who understand how to imply databases. But understanding structured databases is not so cut-and-dry. The complication often arises in their use case and stored procs.
This article explores the intersection, tangents, and use cases for each data collection (Data Lakes, Data Warehouse, and Data Marts). Helping you make better inferences and implementation of these databases.
Why Databases?
As data exchange and automation claims organizations, they bring the need for precision in data interpretation, inferences, and strategic organizational planning. As a result, businesses prioritize data utilization now more than ever.
Across industries –data is core. These data can be structured and stored electronically to exist as databases having multiple accessibilities and genres (more on that later!).
By definition, databases are any organized collection of data that can be stored electronically or accessed in a computer system. Databases are used to store, maintain, and access any genre of data. As units, data is useless and adds no value to your toolbox. When organized as databases, they become a core basis for inferences, decisions, and understanding trends.
An even more organized class of databases exists in Data Lakes, Data Warehouse, and Data Marts. These database types offer cutting-edge precision for drawing inferences and strategically implementing trends. However, operations and use cases for each scenario are quite similar but different.
Organized databases (Data Lakes, Data Warehouse, and Data Marts) have differences that dictate their similarities and differences in use cases or implementation. We must consider their use cases to better understand the relationship between these structured databases.
What are the Use Cases for Databases?
Deciding when/how to use databases is no easy feat. It takes understanding how databases work and how to use them in real-time scenarios.
Databases are used for everything. It predates any decision or implied action. It could be simply storing pictures on your computer, a Netflix movie recommendation, or purchasing items online. Databases essentially allow computers to store information in any organized and accessible form.
There are twelve varieties of databases. Each exists with unique pros & cons, making them specialized for different scenarios and use cases. It’s not one size fits all. You must first determine the databases you are dealing with to understand their use cases and application.
Understanding Data Warehousing, Data Lakes, and Data Marts as a Concept
Ordinarily, databases are not structured or organized enough to draw conclusions or organizational modus operandi. A complex operation or decision-making process requires a more comprehensive collection of relevant data that contributes to your goal.
Although the terms “Data Warehouse,” “Data Lakes,” and “Data Marts” are sometimes confused and used interchangeably, they each are a unique collection of data with notable specialization. While Data lakes are better suited for predictive & advanced analytics, Data warehouses are better performance analytics & multi-purpose operation. Data marts have peak specialization in business-specific reporting and analytics.
Data Warehouse
In most cases, a data warehouse only stores already modeled or structured data. Data Warehousing is designed for multi-purpose use cases and all different use cases. It doesn’t pinpoint specific requirements for any business unit or function. Simply put, data warehousing does a lot more of generalizing organized data. By design, warehousing cannot specifically solve unique or business-specific decisions.
Data warehouses are better utilized for performance analytics & multi-purpose operation. Let’s consider finance departments at any company as an example of their use cases. The goals of any finance department are finance-specific. They’d care about metrics like Profits, Costs, and Revenues. Also, they’d offer finance-specific advice to management on monetary decisions. In this case, any implied decision from a data warehouse would not give attention to specific marketing & sales data but rather generalize based on financial tenets like profit, loss, and revenue.
The best use case of a data warehouse includes;
● Drawing inferences from analyzing large stream data
● IoT data integration
● Merging data from legacy systems
● Team performance evaluations
● Evaluating the effectiveness of marketing/sales
Data Lakes
A data lake is a dump for all forms of business-generated data. Meaning it includes data generated from all aspects of your business. It’s a mix of all data like your chat logs, pictures of invoices, receipts & checks, structured data feed, and emails. Data is not task-specific; it does not filter or discard information; rather, it includes everything.
Uses Cases for Data Lakes
The use cases for data lake are best defined in two ways;
- Large Organizations with Multi-purpose Product
- Undefined Use of unstructured Data
Data lakes work for both scenarios because the inferences you can derive from their databases are not exclusively defined. There’s more than one possible way to analyze databases and draw conclusions. Date lakes work based on collect-first, analyze later, and it’s a cheap form of storing data.
Since the volumes of data collected can be sky-high, it takes hours/days for traditional DBs single query. Using a Massively Parallel Processor (MPP) infrastructure is better suited for analyzing such high volume in relatively lesser time.
Data Marts
Data Marts are generally a subset of a Data warehouse. They are the fundamental component that makes up a data warehouse. Unlike a data warehouse which is an aggregation of databases, a data mart is more specialized by narrowing down to single functional areas of an organization.
Since data marts are focused and geared towards single functional areas of organizations, they can be used to pinpoint unique solutions to specific problems. As such, they are not focused on general but on single decisions and problems. Mostly used for case studies for specific business operations.
Data Warehouse, Data Lakes, and Data Marts: Their Differences and Intersection
Data Marts are a subset of data warehouses focusing on single or independent decision applications. In comparison, a data lake is even more generic than a data warehouse. In view of the other, each of these data aggregation is different from the other, and here’s how;
Conclusion
Databases are quintessential and also the foundation for any other type of organized data. They can be engineered into task-specific databases like the Data marts or generic ones like the Data warehouse and data lakes.
Before deciding which of these databases fits your organization, it’s important to understand why you need this data and to align the objectives of your organizational goal to the offering of each structured data.
