I write about fintech, data, and everything around it
Data Lake vs. Data Warehouse vs. Data Mart
I write about fintech, data, and everything around it
Which database management system has the highest performance, and which is capable of data distribution? To put this question in perspective and help you navigate this world of databases, we have decided to summarize all the differences between these systems in this blog.
Every enterprise needs to process data to make better operational decisions. And for it, they need to select the best data bank/storage and data pipeline & data integration solution that meets the unique needs of the enterprise. Currently, Data Mart, Data Lake, and Data Warehouse are the top solutions available. However, factors like data type, scope, services etc. can determine which solution will be the best for you.
So, here we will discuss what each of these solutions represents and their capacities. People often use these three terms interchangeably because of their few similarities. But, each of these terms is different, and we’ll explore each of them through a detailed comparison among them.
This is the ultimate in-depth comparison to data storage in 2022.
So if you want to:
- Understand Data Lake
- Understand Data Warehouse
- Understand Data Mart
- Comparison between Data Lake vs. Data Warehouse vs. Data Mart
Then you’re at the right place.
Let’s get started.
Understanding Data Lake
A Data Lake refers to a place where all kinds of generated data across different business parts get dumped. Generated data could be chat logs, images (for receipts, invoices, checks, and so on), structured data feeds, emails, and videos. Data Lakes do not filter any parts of information out. In fact, Data Lakes even capture data of invalid, cancelled, and returned transactions. A Data Lake offers an affordable way to store huge quantities of diverse data that every business needs to analyse to enhance the business.
In addition, Data Lake works much quicker than traditional databases in terms of data analysis. Thus, ingraining it in a Massively Parallel Processor Infrastructure enables a business to monitor data faster and efficiently.
Important points to note about Data Lake
- It collects data from several data resources over a prolonged period.
- It uploads data without needing any predefined methodology.
- It can fulfil different user requirements across the business.
- It processes, cleanses, and collects the data.
Understanding Data Warehous
A Data Warehouse is used to store data that have been structured and modelled beforehand. It works as a core analytics framework of an organisation. It operates in conjunction with an operational data store (ODS) to collect the data obtained across several databases by the organisation.
For instance, in case a business maintains databases that support point-of-sales, customer data, online activity, and HR data, the Data Warehouse will capture the insights from these sources and make them accessible at a solitary location. ODS handles normalising and cleaning of data. Basically, it prepares the information for Data Warehouse storage.
Important points to note about Data Warehouse
- Stores huge amounts of historical data and prevents old data from getting erased at the time of new data additions.
- Efficiently uses various sources to collect the data.
- Works along with ODS to store cleaned and structured data.
- It is organised based on the subject.
- Works as a prime data resource for data analytics.
- Dashboards and reports can utilise insights from Data Warehouses.
Understanding Data Mart
Data Mart is defined as a subcategory of the Data Warehouse. It is built for a specific business or department function. As Data Mart facilitates data collection for a particular department, it assures an isolated security feature. It denies any unintended data access. Due to its isolated characteristics, performance management and communication are performed efficiently within the department. Thus, there’s no issue with analytical workloads.
The Data Mart comes in three different types:
Dependent Data Marts
The dependent Data Mart refers to a framework that builds from an already existing Data Warehouse. It follows a top-down approach for managing data. It uses a centralised location to store all your business data. Further, it pulls out only a defined data portion that is required for analysis.
Independent Data Marts
It is addressed as a stand-alone system. It isn’t built via an existing warehouse and focuses only on a single business function. The data is released through internal and external sources, processed, and updated on the Data Mart. Here, it is saved till business analysis and or until required.
Hybrid Data Marts
This type of Data Mart gets data from an existing Data Warehouse as well as additional functional source frameworks. It leverages the bottom-up business-level integration technique along with end-user focus and speed of a top-down technique.
Important points to note about Data Mart
- Puts its sole focus on a single business unit or subject matter.
- It holds aggregated data; hence, it works like a mini Data Warehouse.
- Data scope is limited.
- Usually, it employs a star schema or other similar structure.
Comparison between Data Lake vs. Data Warehouse vs. Data Mart
Below are given the key differences between a Data Mart, Data Warehouse and Data Lake.
Data Mart vs. Data Warehouse
| Feature | Data Mart | Data Warehouse |
| Size | These are smaller in size, generally less than 100 GB. | These are particularly larger in size. They can be a terabyte or even more than it. |
| Access | Data Mart maintains a repository of important insights for an entire subgroup. | Data Warehouse provides access to only a few users. |
| Overhead | Data Marts need lower overhead. | These comparatively need more overhead. |
| Speed | These are faster as they store only subject-based data. | In comparison, these are slower as the storage contains a wide range of data obtained from the various business areas. |
| Source | They get data through the Data Warehouse. | They receive their data through the databases. |
| Scope | Isolated data feature gives it a smaller scope. | As it contains a wide range of normalised and cleaned data across various business units. It tends to have greater scope. |
Data Lake vs. Data Mart
| Features | Data Lake | Data Mart |
| Data storage type. | It contains all kinds of raw and unfiltered data extracted from a business. | A Data Mart contains a subset of structured and filtered data specific to a department only. |
| Data Analysis | These perform deep and broader analysis of raw data obtained. | These perform analysis for a limited section of data which allows them to carry out faster and more effective analytics of relevant insights. |
| Scope | These work as an all-in-one solution, similar to the Data Warehouse. | These are single-use-solution and cannot perform ETL for any data. |
| Location | These have a centralised archive to store data. | These can be found in multiple user areas. |
Data Warehouse vs. Data Lake
| Features | Data Warehouse | Data Lake |
| Purpose | It stores cleaned data to create structured data reporting and models. | It stores data for the use of enterprises. |
| Hardware/software | It comes with its in-built DBMS, storage, operating system and software. | It uses multiple hardware types that allow cost-effective petabyte and terabyte storage. |
| Source | It uses ODS from transactional systems to collect data. | It can extract data from any kind of data type. It can also extract data from non-traditional data types such as social network activity, web server logs, sensor data etc. |
| Scope | It serves operational users who need to create analytics reports. | It performs deep analysis even beyond the data storage of a warehouse. |
| Speed | It takes comparatively more time in retrieving results. | As it stores accessible raw data that isn’t structured yet, it retrieves results quicker. |
Summary
Every enterprise is unique; they have specific challenges to overcome, resources to use, and goals to achieve. Therefore, it is important to evaluate the available options carefully to figure out which solution would suit the company best. It is recommended to consider your budget, need for data storage volume, and frequency of needed access while making a choice.
So, whether you are an SME or enterprise company, data tracking is the key to the success of your business. Schedule a 30-minute call and learn about Zuci’s Data Engineering Services to craft a single source of truth system for real-time data analytics, business reporting, optimization, and analysis.
If you are on the look out for a technology partner for a 360-degree data led transformation, you have come to the right place. Zuci prides itself on working with leading organizations of all sizes, by taking care of their technological needs and improving their operational firepower. Talk to us.
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