NoSQL Database Overview

 As organizations began using modern applications that have flexible data requirements, they have turned to using a NoSQL database because it provides schema-less architecture that makes it easier to store data in several formats (Jadon, 2022). Relational databases are predefined and can’t perform with data in different formats. This is because websites and modern applications use different data structures that relational databases can not identify. Because of this, organizations have turned to using NoSQL databases.

NoSQL databases are used for real-time applications and big data because they offer flexible schema that deals with changing data. NoSQL databases have horizontal scaling for clustering machines that limits the object-relational impedance mismatch (Jadon, 2022). Because NoSQL databases use different data structures from relational databases, it makes operations faster. NoSQL databases are made to be scalable, flexible, and capable of responding quickly to data management demands. This makes NoSQL databases valuable to most organizations.

Aggregate Data Models in NoSQL

Data aggregation is a process when data is gathered and put into summary form (Mullins, n.d.). Aggregated data is gathered from multiple sources and replace with summary statistics and totals. Data warehouses is where aggregated data is found, and aggregation is done on a large scale through tools. Aggregated data can be queried quickly. As data volume is increasing constantly in organizations it is important that data is accessible, and data can benefit from aggregation which will make it easy to access. Data aggregation enables organizations to examine large volumes of data. Therefore, organizations invest in NoSQL databases. A NoSQL database can handle complex data in different formats because it contains Aggregate Data Models which makes it easier to manage data storage. Retrieved data comes along with the Aggregate Data Models in NoSQL (Jadon, 2022).

Aggregated Data Models have advantages (pros) and disadvantages (cons). There are advantages such as providing easy replication and providing horizontal scalability and fast performance. Aggregated Data Models can be used as an online application primary data source and handle semi-structured, unstructured, and structured data with ease. Large volumes of data are handled easily and efficiently. Aggregated Data Models have disadvantages such as there are no standard rules, and they have limited query capabilities. Also, when data values increase it is difficult to maintain values that are unique. Aggregated Data Models do not work well with relational data.

Types of NoSQL Databases with Aggregate Data Models

 The logical structure of a database management system (DBMS) is called a data model, which tells the relationship between entities and how data is connected to each other (Pedamkar, n.d.). NoSQL databases have four categories. Each of those categories have unique limitations and attributes. This area of the document will focus on those four NoSQL database categories.

Key Value Pair Based

This NoSQL database category is when data is stored in key/value pairs. This means that the database is made in a way that it can handle a heavy load of data. The key/value pair data is stored as a hash table so each key is unique, and its value can be a string, JSON, etc. An organization would use this category of a NoSQL database to for dictionaries, associative arrays, and a collection, etc. Shopping cart contents are a good example of how this category of database works. An advantage of this database is that developers can store schema-less data.

Document-Oriented

In the NoSQL Document-orientated database data is stored and retrieved like the key/value pair-based database except the value part is stored as documents. The database stored documents in JSON or XML formats and can understand queries of that data. Organizations use this type of database for real-time analytics, blogging platforms, and e-commerce applications. This kind of database is not good for complex transactions that require queries of varying aggregate structures (Taylor, 2023).

Column-Based

Column-based NoSQL databases are known to deliver great performances on aggregation queries like AVG (average), SUM, COUNT, etc. because the data is in column form. This category of databases is based on BigTable paper by Google (Taylor, 2023). All the columns are treated separately, and values of single columns are stored contiguously. Organizations use column-based databases in business intelligence, CRM (Customer Relations Management), and library card catalogs. 

Graph-Based

A graph-based database is known for storing entities and relations amongst them. Graph-based databases are multi-relational by nature. A traversing relationship is easy because they are already in the database and no calculation needs to be done. An organization will use this category of databases if they store data for logistics and social networks.

NoSQL databases are very beneficial to organizations. An organization should know the type of data they store to choose the right NoSQL aggregate data model database so it provides the right technique the organization should use to produce the results it needs to make good business decisions. Choosing the right NoSQL database is important for businesses to stay competitive in their market.

Data Migration in NoSQL Databases

Data Migration Overview   

The process of moving data from one system to another is defined as data migration, and the change happens in a database storage or application (Data Migration Strategy, n.d.). Any data migration will consist of at least the transform and load steps in the ETL (extract/transform/load) process. Organizations might need to update their entire system, or to migrate data to another source such as going from a SQL database to a NoSQL database. Organizations may even want to deploy another system that will sit alongside existing applications in their system. This area of the document will focus on principles of data migration in NoSQL databases. Two types of data migration use cases will be listed.

Organizations have several reasons for data migration, but the most common reasons are to stay competitive with competitors and enhance system performance. Whatever reason an organization has, it needs to have a complete data migration strategy. Not having a good data migration strategy can cause missed deadlines, higher costs, and migration projects will fail. The data mitigation strategy should consider things such as the organization knowing its data, cleaning up any known issues in the system, having a present maintenance and protection plan and following governance and state and federal laws.

 An organization should protect data during a migration by backing up all the data, understanding where the data lives and what form it is in, extracting, transforming and deduplicating the data before moving it, and documenting the data migration process (Groves & Eaves, n.d.). The documentation should be done in a data migration strategy/plan. This plan should include data sources, cost, and migration strategy. A list should be made of everyone that will participate in the migration. Data design and inspection should be included showing an organized plan and map for where data is being moved to. A list of migration tools and software that will be used in the migration, and a list of how the old system will be shut down or decommissioned (Gillis, n.d.).

NoSQL Data and Schema Migration

Relational databases have strict schema and can be moved in data migration by preserving schema development in a version-based order (NoSQL migration , n.d.). Databases without schema do not have strict schema and requires thorough migration because of the inherent schema in programming languages obtaining the data. However, NoSQL databases can interpret the data in a way that’s receptive to variations in data’s inherent schema. They can utilize growing migration and modernize data.

 When migrating from a SQL to a NoSQL database, there are some important factors to consider (Tol , 2021). The schema should be redesigned. According to a NoSQL optimized model, only data layer and schema should be changed. No changes are required in business logic. Refactorization should be performed to refactor RDBMS (relational database management system) and data logic into a NoSQL optimized model. Figure 4 shows the process of migrating from a SQL to a NoSQL database in popular vendors.

A schema migration is when database schema is adjusted to a new or prior version of a database to make migration easier. As organizations usually have a legacy database and file system format, using data transformation steps are important in migration. A good data transformation phase needs to be added in the data migration plan/strategy.

Use Cases in Data Migration

Data migration has the goal of ensuring that data is transferred accurately and remains accessible and usable once the transfer is complete. Data migration involves special techniques and tools like data migration APIs and data migration software or scripts. Another way to look at data migration is to consider it re-platforming data. For instance, if an eCommerce store owner wants to go from one shopping platform to another. Data migration can be time-consuming and complex, especially if a large data volume is involved, or if the destination system is different in format or structure. Two different use cases of data migration are listed below (Jena, 2022):

1. Replacing or upgrading - This is a common use case in data migration. Organizations use the data migration process to transfer their data from an old system into a new system. Often, they replace an outdated system. Upgrading to a new technology increases productivity and provides better data security. More likely it is a business that has expanded, and their data volume has grown. Also, if it is a social media business, they are likely to move data from a SQL to a NoSQL database as the data will be in different formats due to postings of documents and videos from their users. A NoSQL database will be able to handle all forms of data.
2. Going to the cloud - often businesses that have a tight budget and don't want to have to maintain servers will go to the cloud from their physical server(s). Some organizations have a massive amount of data, and they go to the cloud because they can easily scale the size of data storage. Organizations also go to cloud services because the cloud service provider will set all the data migration up and control it. Some businesses feel that cloud providers offer better data security than they can themselves. A use case of this would be any size business that wishes to go to a cloud provider due to any or all the reasons listed above.

In conclusion, NoSQL databases are known for their scalability and ability to handle structure, semi-structured and unstructured data. Whether or not an organization wants to invest in a NoSQL database depends on what kind of data they store. If the organization wants, or already has, to store all formats of data, a NoSQL database will suit them. The organization should then decide if they want a physical server or cloud services. Once that is decided, the organization should put together a data migration plan/strategy and document the whole process. Data migration can be complex, but if it is well planned, and the organization backs up all data before the migration, then the data migration will be easier and successful.

References

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Gillis, A. S. (n.d.). Data migration. Techtarget. https://www.techtarget.com/searchstorage/definition/data-migration

Groves, M., & Eaves, F. (n.d.). NoSQL migration essentials. Dzone. https://dzone.com/refcardz/nosql-migration-essentials

Jadon, A. (2022, April 27). Understanding aggregate data models in NoSQL simplified 101. Hevodata. https://hevodata.com/learn/aggregate-data-models-in-nosql/

Jena, M. (2022, December 20). Data integration vs data migration: A comparative study. Hevodata. https://hevodata.com/learn/data-integration-vs-data-migration/

Mullins, C. (n.d.). Data aggregation. Techtarget. https://www.techtarget.com/searchdatamanagement/definition/data-aggregation#:~:text=Examples%20of%20aggregate%20data%20include,age%20of%20customer%20by%20product.

NoSQL migration . (n.d.). How NoSQL migration works. Bytescout.  https://bytescout.com/blog/nosql-migration.html

Pedamkar, P. (n.d.). NoSQL data models. Educba. https://www.educba.com/nosql-data-models/

Taylor, D. (2023, January 3). NoSQL tutorial: What is, types of NoSQL databases & example. Guru99. https://www.guru99.com/nosql-tutorial.html

Tol, S. (2021, January 14). SQL vs NoSQL and SQL to NoSQL migration. Dzone. https://dzone.com/articles/relational-vs-nosql-databases-and-rdbms-db-to-nosq