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Mobile apps have become the backbone of several businesses for delivering their services around the clock. Applications on the phone are responsible for generating revenues north of over $935 billion, as per a projection made by Statista. The size of the mobile applications market is almost as big as a large developed nation.
If you are in the process of developing a mobile app, you might already be aware of these statistics. You can become a substantial app developer in this market only if your app is delivering its services 24 x 7 x 365. While that kind of performance standard might seem nightmare-like to most developers, that is the baseline expectation for most mobile app users. Such expectations can be met with a robust database that provides access to critical data, is efficient in storing it, and can be scaled as the mobile application grows. Thus, developing an app that is designed for scale is the same as database-driven mobile app development.
It is easy to find the most popular databases. Any professional developer or data engineer can help you understand that Oracle is the most popular database, followed by MySQL, Microsoft SQL Server, PostgreSQL, MongoDB, IBM Db2, and several others. But that piece of information will not give you any perspective on which database platform is apt for your next project.
Hence, our team of experts has created this mobile app development guide to walk you through selecting the best database for app development that stands tall on your, your app owners’, and your users’ expectations.
Database 101: Understanding the Types of Databases
A database is a structured setup for storing and accessing data. A mobile application uses a database to access the precise information required to deliver a service, functionality, or feature when demanded. For instance – once you have created your account on a mobile app, the app will categorize and store that data along with your credentials. The next time you want to log in, all you have to do is use those credentials, and the app will produce whatever information has been logged in the underlying database.
Instead of diving into cloud-native databases and other advanced concepts, we can now work with this elementary definition. Next, you can dive into the types of databases generally used by developers and data engineers to create seamless applications.
As the name suggests, the concept of a relational database is dependent on ‘relationships.’ These relationships are nothing but dependencies between different data points stored in the format of different tables.
Such databases have two central elements – a key and the attributes. A key is unique across all the tables and is usually the subject matter on which we need more information. The attributes are data points stored in relation to the key. The key can never have duplicate values across relational databases. This allows the application to quickly sift through each data table and locate the keys alongside the relevant information.
Operational databases have the inherent characteristic that allows the developer to edit or remove data in real-time. Most other database formats are dependent on batch processing – meaning the edits are made only beyond a certain threshold in time and resources. Instead, operational databases can run data operations in real-time. This makes them the ideal database type to store customer data, employee data, and even payroll data. They can use both SQL or NoSQL; the latter is preferable for real-time operations.
The best way to understand a data warehouse is by imagining a business that has stored log files, historical data, and even transactional data dating back decades. A young analyst has been tasked to sift through the data and provide insights in a particular context.
This is precisely the kind of use-case where a data warehouse can come in handy. Instead of carrying functionalities of editing or removing data, a data warehouse aggregates data from multiple sources and provides efficient query and analytical functionalities. Generally, a data warehouse consists of a relational database, native statistical analysis capabilities, data visualization capabilities, and client interface features to make the solution accessible.
When a database has its inherent components stored across geographically distant locations with no physically shared resources, it is termed a distributed database. Such databases can be of great help when the users are dispersed across geographies.
Distributed databases are of two types – heterogeneous and homogeneous distributed databases. The former is a type where the schema, software, and data structures used across locations are different, while in the latter type, they are uniform. Some distributed databases run on the replication approach, where each relationship is stored at each location. This creates redundancies and can be expensive, but it ensures all the data is always available. The other approach focuses on fragmentation, where all the relationships can be realized only when all the data from all the locations are pulled together. No one location stores all the data relationships – hence the approach is called fragmentation.
Most other databases are limited to storing data in columns & rows, resulting in tables. NoSQL has the capability of storing data in non-tabular formats based on the type of the data model. The most commonly used data models include documents, graphs, key values, and wide columns. If you are designing an application that will be rapidly scaled with several users across locations, NoSQL databases can be of great help.
The Criteria for Selecting the Right Database
Now you have a good understanding of the types of databases at your disposal. But, the question remains – how do you choose the correct database?
Here is a unified set of criteria that will ensure that your chosen database fits your use case:
- Data Structure: The first thing you must examine is the data structure – the approach you take to record the data. The most commonly used data structures are arrays, stacks, linked lists, ques, graphs, etc. observe the use-case of mobile applications and the data structure befitting it. This will help you eliminate the database types which do not support efficient querying, editing, and storage of that particular data structure.
- Data Size: Each database comes with a threshold for storing data. Beyond this threshold, the database will not operate at its benchmarked optimum levels. This is the key reason you cannot store all the data across all the locations with all the variants in plays. It consumes compute resources and reduces app performance. Thus, focus on the database type that best supports the size of data you plan to store for your app.
- Speed and Scale: These are two seemingly distant but intertwined concepts. Speed focuses on the quick accessibility of data when queried, based on the scale of the data. Take, for example – the contrast between MongoDB and MySQL. The former is great for large-scale unstructured data, but MySQL is faster. So, depending on the scale and required speed for your data, choose the database fitting your needs.
- Data Modeling Flexibility: Build a data model – an abstract representation of your data structures and the required features for your mobile application. This is a crucial exercise if your app is providing any form of querying or geo-sensitive features. For instance – the same application can use two different database types for two different use-cases. Uber was once using MongoDB for their content delivery network since it was highly scalable and used MySQL for programming how data will be stored, edited, and manipulated.
- Security Needs: You can easily understand this criterion by assessing your data in its three states – at rest, in transit, and edited. When the data is at rest, you will need a database that provides encryption at both the file and the data level. If the data is transmitted across nodes in a network, it must be moved only through SSL or TLS. And finally, the database must provide absolute flexibility to the users for editing their data at their peril.
- Supporting Multiple App Platforms: This is where your product map will come in handy. If you plan to introduce your app across different platforms like Android or IOS or even as a native app on desktop or a browser app, you will have to choose a database and a web framework that syncs with that particular use-case.
- Seamless Data Conflict Resolution: If you are creating a mobile application with decentralized data writes, you will have to take care of seamless and automated conflict resolution across the cloud, devices, and even systems. If you are developing a cloud-based application, being mindful of this criterion will be of great help later. You must have at least two functionalities taken care of – traceability of system synchronization and replication flexibility at scale to create custom rules for updates.
- Accurate Partition Syncing: Establish a topology early in the process, and you will have a good idea of how to select a database that fits perfectly on this topology. Once you update a value in a specific part of the database on an application development platform, it should get symmetrically updated in the other parts. If you are working with a star topology, you might want the devices to be able to access certain functionalities even in offline mode. For cloudless topologies, P2P information syncing might be required.
- Low Latency: In the simplest terms – latency is the time difference between a command being launched and the point where it is executed. Some databases are designed for high performance despite low latency, like MongoDB, PostgreSQL, and a few others. Remember – lower the latency, lower the compute resource consumption.
- Compatible for Updates & Changes: Just because you are updating the app, it does not create room for letting go of the legacy APIs and data structures. If a user skipped an update, the database must handle the creation of new elements to make room for the current update.
An Inventory of Databases
Here is a comprehensive inventory of databases that can narrow your search and increase the probability of the right database being selected for your app:
- MySQL: It is an open-source database that has been around for over two decades. If your app has consistent and high traffic, MySQL might be the ideal choice to deliver a fast and frictionless experience.
- PostgreSQL: It is one of the fastest-growing database management systems, primarily rising to popularity for its ability to handle complex queries at a high volume. It gives you a ton of flexibility in defining data types and index types.Companies Using This: Apple, IMDB, NASA, Instagram, etc.
- MongoDB: MongoDB is an excellent choice for CMS and business intelligence applications where data is frequently created, updated, and access at a geographically distributed scale.
Companies Using This: Uber, Lyft, Delivery Hero, etc.
- Memcached: It is a neat solution that caches objects. This decreases the load on the database and provides better performance.
Companies Using This: Facebook, Shopify, Slack, Instacart, etc.
- Cassandra: If you are storing a large scale of unstructured data with scalability and good performance, Cassandra can be a great alternative.
Companies Using This: Activision, CERN, Fidelity, eBay, etc.
- ArangoDB: ArangoDB uses NoSQL and is excellent for applications requiring graph and geospatial features. It has become a popular choice for fraud detection.
Companies Using This: AresRPG, Brainhub, etc.
- CouchDB: If your application has users geographically dispersed and will only occasionally require syncing, CouchDB can be an efficient choice. In the long run, such alternatives can reduce your cost of app development.
Companies Using This: Verizon Wireless, GrubHub, Boeing, etc.
- Redis: Redis is one of the best solutions for storing key values and caching data frequently accessed by users on the application. It is known primarily for its low maintenance.
Companies Using This: Twitter, Pinterest, Craigslist, etc.
Choosing a database can be a decision that leads to making or breaking your mobile application. Follow the structured approach mentioned here, and you will have a database or the last alternatives suitable for your app. And remember – there is no one best backend database for a mobile app that works for all use-cases. Hence, before looking into popularity trends, look into the use-case, data structure, and requirements of your users.