Data mining repositories all have the same purpose: to store data for analysis and reporting purposes. The types of data stored and the access options for users will vary depending on which repository they are. This article will cover some of the functions and applications of data warehouses and data marts. Data Mart, Data Warehouse, and Data Lake are frequently used interchangeably. What are the differences between these terms? This article will help you understand the similarities and differences, as well as when to use each.

Related post – Why is Data lake is a viable solution in Big data space?

What is Datawarehouse?

Datawarehouse is a central database that combines information from multiple sources. It then consolidates the information through the extract, transform, and load process (also known as ETL) into a comprehensive database for business and analytical purposes. The ETL process extracts data from various sources and transforms it into a clean format that can be used in business processes. Finally, it loads the data into the data repository. The data warehouse holds current data as well as historical data that has been cleaned, categorized, and conformed. Data is structured and modeled before it is loaded into the warehouse. A data warehouse is also used to store data from transactional databases like ERP, CRM, and HR. With the advent of technology such as NoSQL technologies and new data sources, non-relational data can also be used for data warehouses. A data warehouse typically has a 3-tier structure.

The lowest tier of architecture contains the data servers. These could be relational, non-relational, or both. They extract data from multiple sources, consolidate it, and then create one.

The second-tier in this architecture contains OLAP Server. This software category allows users to process/analyze information from multiple databases servers. The Client Front layer is the highest level in this architecture. This layer includes all the applications and tools used for reporting, query analyzing data. Data warehouses, which used to be housed in physical locations, are now moving to the cloud due to their rapid growth and the use of sophisticated analytical tools. A cloud warehouse offers many advantages over an on-premise data center. These include unlimited storage, lower costs, and faster disaster recovery. A cloud-based data warehouse is a better option than an on-premises one. Teradata and Oracle Exadata are some of the most popular data warehouses.

What is Data Marts?

A section of the data warehouse that is designed for a specific operational task, a business function, purpose, or group of users, the data mart. Data marts are created for two reasons. The first is to be able to quickly access frequently updated data. The second is that end-user response times are improved. It is also easier to create a data mart. Creating a data warehouse takes a lot of work and resources. However, creating a data mart is much simpler than the data warehouse. Datamart is relatively inexpensive.

There are three types of data marts. They are dependent independent and hybrid. Data marts that are dependent on the central warehouse’s data source are created by drawing from it. Independent data marts, on the other hand, are built by drawing data from either operational or external sources. A dependent data mart provides analytical capabilities for restricted data warehouse areas. It also offers isolation security and solo performance. The hybrid data mart is a combination of inputs from an operational system, a data warehouse, and external systems. What data is extracted from the source and how it has been transformed to apply the changes, as well as how the data was transported into the hybrid data mart are all factors that make the difference. Dependent Data Mart pulls data directly from the enterprise data warehouse.

The data has been cleaned up and transformed. An independent data mart must cleanse and transform data that has come from external sources and operational systems. No matter what type of data, the primary goal of a data mart is providing data to end-users when they require it. Data marts are a way to speed up the business process and make data-driven decisions more cost-effectively and quickly.

What is a Data Lake?

A Data Lake can store all kinds of data, structured, semi-structured, or unstructured. A data warehouse stores large amounts of data in its native format. Data can be uploaded to a data lake without having to define the structure or schema.

A data lake can be described as a place that stores raw data straight from the source. This does not mean that data lakes are a place where you can dump your data without governance. The data is properly classified in the data lakes, but it is also protected and managed. Data lakes can be created using cloud object storage, such as Amazon S3, or large-scale distributed systems like Apache Hadoop. You can also deploy them on relational databases management systems or NoSQL data repositories. Data lakes have many advantages. They can store any type of data, including semi-structured, structured, and unstructured data. Another benefit is the ability to save time when defining and transforming data. Data can be imported directly into the data lakes in their raw format. Cloudera, Google, and IBM are some of the vendors that offer technologies, platforms, and reference architectures to data lakes.

These are the Top 5 Differences Between Data Lake and Data Warehouse

Data Mart is often confused with data warehouses. However, they serve completely different purposes. Here’s how:

1. Assisting with different data types: A data warehouse is usually made up of transactional data and includes quantitative metrics as well as the characteristics that describe them.

Non-traditional data types can be supported by a data lake system, such as web server logs and sensor data, social media activity, text, and images. These non-traditional data sources are often ignored. Consumption and storage can be costly and time-consuming.

2. Support for the user
Data warehouses are a great solution for users who need to manage their data, evaluate their performance metrics and maintain a spreadsheet. A data warehouse is best for “operational” users because it is easy to use and is designed to meet their requirements.

Data warehouses can be used to support users who perform a more detailed analysis of data. Data warehouse is used by these users as a central source of data for data preparation, data integration, and data analysis. A data warehouse can also be used by users to perform deep analysis. This may lead to the creation of completely new data sources. These users are mostly called “Data Scientists” and they use advanced analytical tools such as statistical analysis and predictive modeling.

All of these users are well supported by the data lake system. For example, data scientists may use the data lake system to access large data sets. Business users, however, can take advantage of more detailed views of the data.

3. Data Maintenance
A lot of time will be spent in creating a data warehouse by analyzing data sources, understanding business processes, and composing data. This involves making decisions about what data should be included and excluded.

However, data lakes maintain ALL data. Data lakes can store all data, not just the data currently being used but also data that could be useful in the future. You can keep data for a long period of time, so we can access it again and go back to analyze it.

This is possible only because of the hardware capabilities of a Data Lake, which often differ from those used in a Data Warehouse.

4. Adapting to Change:
Because of the complexity of data loading and the effort made to simplify analysis and reporting, a good data warehouse design is able to adapt well to changes. However, these changes will take a lot of time and resources on the part of such developers.

Today, many corporations question the amount of time it takes for data warehouse staff to adapt their system. Self-service business intelligence was born out of this ever-increasing amount of time.

A data lake is an opposite. All data is stored in raw form and can be accessed by anyone who needs it. Data lake gives users the ability to access data beyond what is possible in a data warehouse.

5. Rapid Insights
The difference between the two is due to the combination of the four components previously mentioned. Data lakes include all data types and can be accessed before they have been structured or transformed. This will enable users to access their data faster than traditional data warehouses.

This approach might not be as easy as it sounds. The work that the data warehouse team does may not be the same for every data source. Users will be able to access and make use of the data they choose, while business users may not wish to. The business user’s use-case is to access reports and KPIs.

These operational reports can be based on data lake and will use a more structured view of data in the lake to stimulate what they had previously in the data warehouse. This approach uses metadata to describe the data, rather than rigid tables that need to be modified by developers.

Which Approach should you choose?

This is a difficult question. We don’t recommend deleting a data warehouse that is already well-developed. We recommend that you create a data lake in addition to your data warehouse. You can continue to use your data warehouse as normal and start to fill your data lake by adding new data sources. It can be used to collect warehouse data, which you can roll off and make available for users who need it. You can either move all of your data to your data lake as your warehouse matures or continue the process. It’s a good idea to look at both options, especially if you are just starting to build a central data platform.

Data Mart

A data warehouse can be multi-purpose storage that is used for various purposes. However, a data mart is a sub-part of the data warehouse, which is designed and built for a specific department or business function.

A data-mart has some benefits:

• Isolated security: Because the data-mart is only for that department, there are no unintended accesses (finance, revenue) to your data.
• Isolated Performance In the same way, each data-mart only serves a particular department. The performance load is well managed, communicated within that department, and does not affect other analytic workloads.

Three types of data mart:

1. Dependent data marts – A dependent database mart is built from an existing data warehouse. It follows a top-down strategy that stores all business data in one central location and then pulls out a specific portion when necessary for analysis.

2. Independent Data Marts – An independent data marsh is a system that is built without the need for a data warehouse. It focuses on a single business function and is not dependent on any other data sources. Data is extracted from external or internal data sources and refined before being loaded into the data mart. It is then saved until it is needed for business analysis.

3. Hybrid Data Marts- A hybrid data mart integrates data from both a current data warehouse as well as additional operational source systems. This hybrid data mart combines the speed and end-user focus that a top-down approach offers with the support of enterprise-level integration.

Data Mart and Data Warehouse are two different things

1. A data warehouse is an application system that is independent, whereas a database mart is a support system for decision-making applications.
2. A data warehouse stores data in one centralised archive. Comparable to a data mart, where data is stored centrally in different areas.
3. A data warehouse is a collection of detailed data. A data mart, on the other hand, is a summary of selected data.
4. Data warehouse development is a top-down process, while data mart development is a bottom-up method.
5. A data warehouse is more flexible, information-oriented, and longer-lasting. Data mart, however, is more limited, project-oriented, and has a shorter life span.