Business and IT leaders around the world are replacing outdated, on-premises technology with the cost-effective, scalable computing power available in the cloud.
Choosing to move a database to the cloud can have countless advantages for growing enterprises, including reduced IT costs, improved security, increased agility, consolidated data centers, digital transformation and accelerated growth. While the benefits of database migration are indisputable, it is essential to go into the process with cloud experience and a well-developed plan.
Database migration is a complicated, multi-phase process that generally includes an assessment, database schema conversion, data migration, script conversion, functional testing and other essential steps. One of the biggest decisions business leaders will have to make is to what type of database they migrate.
When comparing databases, look at both access characteristics and patterns of how the data is stored. Data can be segregated by size or scale, or structured as an SQL schema; or it could possess no defined structure, similar to a file name to file content relationship.
Common Types Of Databases Organizations Migrate To
Many organizations want to migrate their data storage to a more scalable and less costly environment. However, it is important to carefully consider what type of database is best suited for the organization.
A relational database refers to a database that stores and offers access to data points related to one another. This type of database is based on the relational model, which is a streamlined method of representing data in tables. Relational databases continue to dominate the database landscape today as they allow developers to define the database schema, as well as constraints and relations between tables and rows within a database.
The most common uses of relational databases include mobile and web applications, online gaming and enterprise applications. Startups often turn to versions of Amazon Aurora or Amazon RDS to achieve scalable, high-performance applications on AWS.
Structured Query Language (SQL) is the main interface used to communicate with relational databases and can be used to update, add or delete rows of data, retrieve subsets of data and manage all other aspects of the database. Data within the columns can be used in a variety of ways without the need to reorganize the database tables.
A key-value database is a type of non-relational database that uses a simplistic key-value method to store data. Data is stored as collections of key-value pairs where a key serves as a unique identifier. Values and keys can be anything, from complex compound objects to simple objects.
There are several characteristics of key-value databases that make them a popular option for businesses. They are highly partitionable and permit horizontal scaling that is not available with other types of databases. Data is written, including updated, inserted and deleted, and queried based on the key used to store and retrieve its value.
Key-value databases use a compact index structure to quickly and reliably locate a value by a key, making this type of database a good option for systems that need to find and retrieve data in constant time. An example of a popular key-value database is Amazon DynamoDB.
A document database is a type of non-relational database used to query and store data as JSON-documents. Document databases deliver a variety of benefits, including the ability to horizontally scale out, a flexible scheme that enables the data model to evolve and an intuitive data model that is easy and fast for developers.
Document databases have several key features that should be considered when selecting a database for an organization to migrate into. First, data is stored in documents, unlike other types of databases in which data is stored in structures similarly to tables or graphs. Document databases are also distributed, allowing for data distribution and horizontal scaling, which is generally less expensive than vertical scaling.
Document databases possess an API or query language that enables developers to execute CRUD operations within the database. This type of database also has a flexible schema that prevents all documents from having to retain the same fields. Developers often find working with data in documents easier than working with data in tables, making document databases a cost-effective option for organizations.
Graph databases are unique as they store relationships and nodes instead of documents or tables. Data is stored, with no restrictions, to a predefined model and is purpose-built to navigate and store relationships. A graph database is considered a specialized, single-purpose platform for the use of creating and manipulating graphs. Also referred to as graph analytics, this platform allows for the analysis of data in a graph format.
There are two main models of graph databases, including property graphs and RDF graphs. Property graphs focus on querying and analytics, while RDF graphs emphasize data integration. Each consists of a collection of points known as vertices and connections between these points known as edges.
There are several advantages to using graph databases. This type of database excels at analyzing connectivity to identify weak points within a network, finding the shortest path between nodes and analyzing the state of a community or network based on connection distance in a group.
An in-memory database is a purpose-built database that uses memory for data storage instead of storing data on disks or SSDs which is common with other databases. This type of database is designed to create lower response times that eliminate the need to access disks. Data in an in-memory database is stored and managed in the main memory, which creates a potential for data loss in the event of a server failure.
Web and mobile applications, financial services and eCommerce platforms have use cases such as session stores, leaderboards and real-time analytics that rely on rapid response times and the ability to handle large spikes in traffic.
There are several types of in-memory databases available on AWS, including Amazon Elasticache for Redis, Amazon MemoryDB for Redis and Amazon ElastiCache for Memcached. With an in-memory database, data is always ready and available for use.
A ledger database is a type of NoSQL database that offers a transparent and immutable transaction log that is owned by a central authority. NoSQL databases provide a mechanism for the storage and retrieval of data.
Data is stored as a document and with no natural relationships to other documents. It is also immutable, meaning it cannot be modified once created. Any changes to the data must overwrite existing data instead of being edited in the database.
Ledger databases are considered transparent as they allow users to see changes to data and records over time. Users can access a highly detailed audit log of changes that includes who performed the changes and what data was altered. Ledger databases are also cryptographically verified, meaning when records are recorded the entire set of data is hashed.
Of course, there are some limitations to ledger databases. This type of database is considered a service for storing data, meaning any data that may be stored in a traditional database can be stored in a ledger database but comes at the cost of updating the cryptographic record for each transaction.
Time Series Databases
A time series database allows developers to continuously and efficiently add, track and process large quantities of real-time data with precision and lightning speed. Although other types of databases have been used for these types of workloads, time series databases have the capabilities to perform these processes better due to the use of specific algorithms and architecture.
Time series databases store data as pairs of values and times, making it easier to analyze time series over time. This type of database offers a wide range of benefits, including more meaningful and accurate time series measurements. Users can easily view past, present and future datasets that are accurate and provide a more meaningful view of the information.
Understanding Which Database Your Company Should Select
Understanding the database chosen is critical to the success of the migration project. There are many aspects to consider when determining whether a specific type of database will be sufficient for an organization. For example, the size of the database that the business is trying to migrate plays a key role. It can also be useful to estimate how long the data copy is likely to take, as well as how many tables and schemas are going to be migrated.
Also, consider how many large tables exist in the current database. Tables are considered “very large” when they are more than 200 gigabytes and contain hundreds of millions of rows. The use of AWS and native tools, such as AWS DMS, can be used to load data in parallel if the tables are partitioned.
Also, consider if the database contains LOBs, or large objects. Keep in mind that migrating LOBs can be slow, extending the length of the migration and requiring more memory on the replication server. If LOBs are in a table without primary keys or other constraints, problems can arise if changes are to be made later.
Speak With A Maryland Custom Software Development Company
There are many reasons why a business may be interested in moving to a new database. A company may decide to save money migrating to a cloud-based database or may find that a particular type of database contains features that are critical for the business’s long-term needs. In many cases, organizations choose to undergo database migration because their legacy systems are outdated.
Regardless of the reason, the process of database migration can be highly complex, consisting of multiple phases and iterations. To learn more about how to select a database for migration for your organization, or to speak with an experienced custom software development company, contact the database migration professionals at Orases.