Understanding Data Model Repository Simplified 101

Ofem Eteng • Last Modified: December 29th, 2022

Data Model Repository Featured Image

In our lives today, data has become an integral part of decision-making as the world is producing data more than ever before. So, having the right tool to collect, store, and analyze these data has become the topmost priority of every organization. In this context, organizations are constantly on the look to master Data Collection, Storage, and Manipulation to gain a competitive advantage over their contemporaries. This is where Data Model Repository comes in.

The sheer volume of data being collected by businesses today goes beyond what traditional Relational Databases can handle, giving rise to what is known as a Data Model Repository. As more and more businesses adopt Data Repositories to store and manage their ever-increasing data, it is imperative to know and understand all about a Data Model Repository hence, this article is aimed at defining what it is, the types of repositories we have, its importance and disadvantages, and ultimately, useful considerations to observe before setting up a repository.

Table of Contents

Definition of Data Model Repository

Data Model Repository
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A Data Repository can be defined as segmented data that is isolated for reporting and analysis. A Data Model Repository is also referred to as a data library or archive that comprises a large set of Database infrastructure made up of several Databases that gather, manage, and store diverse datasets to be distributed, analyzed, and reported.

The Data Model Repository can also be shared as the repository stores revisions for multiple branches, having each branch share one specific repository for multiple revision storage.

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Types of Data Model Repositories

There are several types of Data Model Repositories, they include a Data Warehouse, Data Lake, Data Marts, Data Cubes, Metadata Repositories, Operational Data Store, and RDBMS. Below is a description of each type of Data Model Repository listed above.

Data Warehouse

Data Model Repository: Data Warehouse
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A Data Warehouse is essentially a large central repository that collates data from multiple sources. The gathered data can be used for supporting Business Intelligence activities such as analysis, enterprise reporting, ad-hoc querying, etc. to enable users to make the right business decisions based on the insight obtained.

A Data Warehouse runs on specialized hardware either for on-premise platforms such as Teradata, Greenplum, IBM Netezza, etc, and on cloud platforms like Hevodata, Snowflake, Google BigQuery, Microsoft Azure, Amazon Redshift, and lots more. Cloud-based Data Warehouses offer users the ability to scale up their Database infrastructure on-demand avoiding extra expenditure when undergoing routine maintenance, unlike the on-premise model.

Data Lakes

A Data Lake is a unified Data Repository where structured, semi-structured, and unstructured data can be stored at any time. The data stored in a Data Lake can be in its raw/native format as it accepts data of any kind, for example, when data that can not be properly cataloged, categorized, or classified is collected by an organization, it can simply be dumped into a Data Lake temporary to be reviewed later or permanently.

The Data Lake has no predefined schemas or structures, they are easy to set up and require little or no maintenance. Data Lakes are normally built on top of No-SQL Database platforms such as Apache Hadoop as they do not require a pre-defined schema and do not adhere to ACID characterization. Vendors such as Amazon, Microsoft, Oracle, Teradata, MongoDB, and Cloudera offer different variations of data lake solutions with proprietary data management add-ons.

Data Marts

Data Model Repository: Data Warehouse
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This is a subset of a Data Warehouse, as it is a segregated section of the warehouse limiting users to specific data sets. A user cannot access all the data found in the Data Repository because it is more targeted to what the user needs. It holds a subset of data in tune with the specific needs of users in departments such as Marketing, Sales, Finance, Support, or other Business Departments.

With Data Marts in place, it is easier to access needed information and gain insights from your data to make tactical decisions that might impact a specific business process or department thereby fast-tracking business procedures since it holds only relevant data specific to that need. A Data Mart can be built from a Data Warehouse or other sources but it is always highly curated to meet particular needs.

Data Cubes

This can be defined as a list of data with multidimensions stored as a table. They are usually made up of three or more dimensions as seen in a spreadsheet describing the time sequence of data.

Each dimension can be used to specify particular Database characteristics like daily, monthly, or annual sales from Clients, Sales Representatives, and Products. It also gives you the leverage of analyzing information, identifying trends, and monitoring performance quickly.

Metadata Repositories

This is used to store information about data and Databases. It is used to state the source of data, how it is stored, and the content of the data. It may also define the arrangement of the data and show who the data is being shared with, therefore helping in the administrative management of such data.

Operational Data Store (ODS)

This is used to store detailed transactional data from different operational systems normally on a short-term basis. It accepts data continuously from other systems through real-time replication or through batch extract-transform-load processes serving as some type of an operational system or an interim staging area before the cleansing, processing, and loading of data into a Data Warehouse. ODS are generally ideal for the querying of small datasets to satisfy real-time or near real-time reporting or ad-hoc querying for the conduct of your day-to-day business needs.

Relational Databases (RDBMS)

Data Model Repository: RDBMS
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This is used to store traditional structured transactional data from applications such as CRM, ERP, HR, manufacturing, and financial applications. Data is stored in rows in a table using normalization, primary keys, foreign keys, and constraints to ensure reliability and the basic function of the RDBMS is its ability to create, read, update, and delete referred to as CRUD.

Structured Query Language (SQL) is the querying programming language used to access and manipulate data stored in a Relational Database System.

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Benefits of Data Model Repository

Using a Data Model Repository can help in useful decision-making to improve the efficiency of your organization. The benefits you can obtain include the following:

  • Easier and better analysis can be gotten from your data as a result of the data being isolated in a confined space.
  • Data accessibility is increased for Database Administrators so they can easily track problems in a repository since data is segmented into different sections within the repository.
  • Data can be preserved and archived for future use.
  • Streamline reporting and analysis are made possible through a Data Model Repository since data is stored in a unified location. 
  • It is a lot easier to implement security protocols on a single data storage location than when data is stored in multiple locations.

Disadvantages of Data Model Repository

Some challenges occur with Data Model Repository that must be managed effectively to avoid data failure, they include:

  • The speed of your entire system can be reduced/slowed when there is an increase in your dataset. This can be avoided when there is a scale-up in the Database Management System when there is an increased data expansion.
  • System failure or crash can result in the loss of your entire data as all your data is stored in a unified repository. To avoid such scenarios, it is advisable to maintain a backup of all of your Databases and reduce access to control system risk.
  • Sensitive data can be easily accessed by unauthorized operators as the data is stored in a central location unlike when it is stored in different or dispersed locations.

Best Practices for Working with Data Model Repository

To have an efficient Data Model Repository, several points should be considered and put in place, some of these considerations include:

  • For a small-scale business, when starting, keep the scope of the Data Model Repository modest, only build and expand upon it as your Database users increase and more data is being collected. This will help in the management of your repository and allow users to learn about the system in no time.
  • Identify the right tools for extraction and migration of data to ensure data quality during an Extract-Transform-Load process. Different Data Model Repository tools offer additional features to create, maintain and control the repository so finding the right tool that suits your business requirement is important.
  • Data marts should be built after building a Data Warehouse. Data Marts form the building block of Data Warehouses as it is said that all Data Warehousing is simply made up of Data Marts.
  • The repository should be flexible enough to accommodate evolving data types and volumes as your business expands.
  • The volume of your data should determine how often you load data into the Data Warehouse.
  • Metadata is necessary for quality data analysis and reporting since it contains detailed information about data.
  • Ensure to have sound security procedures on how to manage your Data Repository as this will guarantee the overall well-being of your data. This can mean you have to state access rules to authorize legitimate users that will access the data to modify, transmit, share, etc.
  • Automating the process of loading and maintaining the Data Model Repository will save the time and effort of users and also eliminate errors that would have occurred during a manual process.


This article has covered a lot of ground on the Data Model Repository. It has become essential that as businesses increase their dependence on data, they should have an avenue where data can be collected and stored for further analysis. To secure your enterprise data, creating robust access rules to grant only authorized operators the ability to change or transfer data will help in the management of your data.

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