Anytime we talk about storing data, it’s in a database. Therefore, databases are perhaps the most critical component of the Internet.
Data has never been more crucial than it is today. Therefore, storing, managing, and protecting data is of utmost importance. Due to this, databases have evolved rapidly over the last decade to cater to the need for rapidly growing data and to support data in different sizes, shapes,s, and formats.
Top Database Trends
In some industries, primary databases used have barely changed in the last decade. However, new ways to store and retrieve that data are needed with the ever-increasing amounts of data our customers store in their organizations. We are at the forefront of this need. Here are some database trends you should understand if you sell in the tech industry.
Traditionally, database servers were run in data warehouses owned by the organization using the database. In recent years, public cloud providers’ solutions for hosting databases have become the standard. Customers who host a database in the cloud do not need to worry about the infrastructure backing the database. Instead, they can use tools to scale, configure, and manage it. Businesses use cloud databases to host information they want multiple team members to have access to—mainly if they are a hybrid workforce—and to easily expand the amount of storage they need quickly instead of buying new servers.
Database automation refers to different processes and tools used to make the administrative tasks of a database more efficient. Instead of manually creating backups, scaling infrastructure, and updating the schema, there are many different tools to automate these types of tasks. Most cloud hosting providers include automation tools in their offerings. When it comes to our customer’s business, an example of database automation is continually organizing their data when anything new is added. Another form of automation is running automatic backups to ensure their data collection doesn’t go missing in case of a server failure.
As business scales, so will the data they manage, maintain, and secure within their company. In the past, this might mean they add a new table to the enormous relational database that holds all of their business data. Today, better solutions depend on the type of data they want to store. For example, they might need a NoSQL database for unstructured business data that they need to access in a highly performant way. This new database can live in parallel with other specialized databases, and different applications can access the data as required. In practice, this could mean rethinking how they scale their data storage. For instance, our customer’s businesses could scale up and buy more physical storage. However, they can also scale horizontally, adding more memory instead of physical storage.
Increase in Database Security
Information security has never been as relevant as it is today. The many leaks we have seen from huge companies have impacted millions of people and increased the awareness of security concerns for the average person. As a result, many new tools for database security have emerged in the last few years. For example, many businesses rely on encryption provided by cloud providers and SaaS products to help protect their data. There are also more traditional ways our customers can safeguard their data, like limiting how many people can access it.
What Do Databases Do?
Spreadsheets process numbers, and databases process information—precisely, structured information. Databases can be designed to do just about anything with the information, such as:
- Track, organize, and edit data
- Collect data and produce reports, or
- Be the foundation for information-rich, dynamic websites
- Provide the storage and retrieval of data for applications
The most common database technology today is the relational database. Relational databases store data in a normalized way—that means the data is split up into different tables to avoid redundancy. While relational databases have been around for a while, they offer a versatile data storage and management tool. For example, a relational database can back both user-facing applications with high demands on performance and reporting software.
However, there are cases when relational databases might not be our customer’s first choice. One case is when the rigid structure of the data in a relational database does not fit the data you want to store. For example, you might want to keep a JSON document without a specified schema. It could, for example, be a configuration file or some form of user-generated data. This is when NoSQL databases are helpful. The data is usually queried using an API or SDK instead of SQL, hence the name NoSQL. These databases usually also provide high-speed access to the stored data. Instead of the database engine having to parse SQL and join together the data you specified in your query, NoSQL databases are tuned to fetch the requested data through their API instantly.
Data at Rest, Motion, Use
Kind of like the states of matter, data has states associated with it, too: data in motion, data at rest, and data in use. Understanding these states of data can help organizations appropriately handle sensitive information. Data in action, for example, is an essential concept for businesses regarding data protection and keeping up to date with regulatory guidelines like GDPR.
Relational Databases (RDMS) and SQL
Relational databases consist of two or more tables with related information, each with columns and rows. These connected tables are called database objects; you need a relational database management system (RDBMS) to create and manage them. RDBMSs allow relational database developers to create and maintain a database program, including tools to:
- Query data
- Edit data
- Design the entire database structure
- Produce reports
- Validate data points and check for inconsistencies
They often include a built-in programming language to automate some functions, such as SQL.
Querying for data in a relational database is, as mentioned, most commonly done using a variant of SQL. Using a query language such as SQL, you can fetch data based on the value of a specific column, join related data onto the result, perform advanced calculations, format the data in the way you prefer, and more.
Editing data can also be done using SQL. By executing a query, you can create, update, and delete based on the criteria you define. For example, if you want to update the shipping status of all orders that were placed on a specific date, you could use the below query:
UPDATE orders SET status = “SENT” where date = “2023-01-23”
Designing the structure of the database is also done using SQL. The system of a relational database is made up of tables, columns, indices, and constraints. A table stores data of a particular type, for instance, orders in the e-commerce example above. Columns are the properties of the entities in the table. A table that holds orders might have columns like date, status, and customer_id. Indices optimize the way you can query the data. For example, if the customer_id usually queries orders, you most likely want to define an index for the customer_id column. Constraints are rules for the data stored in the columns. For example, some columns might not be allowed to be left empty. For the orders example, customer_id again might not be allowed to be empty so that we make sure all orders are connected to a customer. Constraints are helpful to make sure no invalid data is allowed to be stored in the database.
SQL vs. NoSQL
The foundation for modern database technology began in the 1970s with the first “relational data model.” Its emphasis was on diligent and precise structure and organization. Today, relational databases remain essential to how websites are built; any website that displays data from a database has to have the following:
- Server-side scripting
- HTML and CSS
- SQL, a database language
- A database management system (DBMS)
For example, if we go back to the e-commerce site above, a page on that site might display a customer’s orders using the example SQL query. The page would be rendered to the user with HTML and CSS while the server side handles the connection to the database, runs the query, and returns the data to be displayed. The database management system is the engine that runs the database and enables the server-side code to communicate with the databases.
Structured Query Language (SQL) is a standardized programming language for accessing and manipulating databases. In an RDBMS like MySQL, Sybase, Oracle, or IMB DM2, SQL writes programming that can manage data and stream data processing. SQL is like a database’s own version of a server-side script and is responsible for:
- Executing queries, which are “questions” asked of the database
- Retrieving data
- Editing data: inserting, updating, deleting, or creating new records
- Creating views
- Setting permissions
- Creating new databases
SQL is a standard programming language but has several variations—including some databases’ own proprietary SQL extensions.
Engineers have decided on the information they will store in your database; they define a data model or schema. This means they connect the different kinds of information into tables and columns. The goal of the schema when working with a relational database is to ensure no data is stored twice in the database. Instead, one piece of information should be stored in its table and referenced by other tables.
Relational databases are great at organizing and retrieving structured data, but what happens when your data is inconsistent, incomplete, or massive? In these cases, you need a more flexible database solution. As the kinds and amounts of data we gather have exploded, the NoSQL database has evolved to solve the challenges of Big Data. These databases are non-relational and distributed. They deviate from the traditional relational model, addressing that most current data harvested from the web is not structured information. NoSQL lends flexibility, scalability, and variety—significant advantages from a business standpoint when you consider that growing data directly results from a growing business.
How does a NoSQL database work? Instead of tables, NoSQL databases are document-oriented. This way, non-structured data (such as articles, photos, social media data, videos, or an entire blog post) can be stored in a single document that can be easily found but isn’t necessarily categorized into a bunch of pre-set fields.
While NoSQL databases provide a lot of flexibility when developing modern applications, they might not always be your first choice. Relational databases, with their strictly defined schemas, enable easy querying of the data in almost any way you can think of. You can query any column in the database, join any related data onto the result, and use the result set in your application or export it as a spreadsheet. NoSQL databases are usually limited to querying by a predefined primary key that retrieves the entire document stored using the key. Some NoSQL databases, however, enable you to define multiple keys you can query and sort. However, they do not reach the query flexibility that a relational database does. The upside of this technical design is that performance is highly optimized and can reach single-digit millisecond response times if you define your keys and queries according to best practices. Read more about the difference between SQL and NoSQL databases.
Here are a few major database characteristics that are helpful to know when weighing one type of database against another—aspects like how databases grow, protect against failure, and duplicate data for speed, safety, and accessibility.
Scalability refers to the ability to scale out or scale up your database so you can hold more data without sacrificing performance. Some questions to think about when deciding how you scale include:
How much do you expect your data to grow (and how soon)?
Do you need a highly scalable database that will be reliable even as the amount of data you’re processing grows exponentially?
Will one server be enough, or do you anticipate needing to add additional ones?
Do you need horizontal scaling or vertical scaling?
When considering scalability, make sure to research the possibilities that you have with your hosting provider. Suppose you are using one of the largest cloud providers. In that case, you should be able to automatically scale your database from a single cheap instance for your development workloads to multiple large instances for your production workloads.
Related to horizontal scaling, sharding is a technique for storing massive databases across multiple servers. It achieves this by splitting different rows into different tables. For instance, a database of customer names might keep customers with last names starting with letters A through M on one shard, while N through Z is stored on another. Sharding can help minimize query response times while allowing data to be held across many cost-effective servers.
Replication is frequently copying data from one database onto other databases on other servers. Using replication, you could, for example, have one database instance used for writing data and one model used for reading data. This can significantly improve your database’s response times and capacity if your site needs real-time access to update and synchronize data.
Latency refers to the time it takes for data to complete a “round trip” between the database server and the application server. When an app queries its database for data, this is how long it takes the server to return that data. The lower the latency, the better, but low latency often comes at a cost to other features, like consistency and availability. A simple way to ensure that the latency is as low as possible between your application and your database is to ensure your database server is as close to your application server as possible. Another way is to investigate how your application queries data in your database and optimize the database schema accordingly by using indices, for example.
When writing to a database, it’s vital that changes to the data don’t violate the rules of the database. Surface ensures that transactions don’t produce errors that can invalidate the entire database. An entirely consistent system means that as soon you successfully write a record to a database, you’ll also be able to request it. This is especially important for things like financial transactions. Consistency comes at a cost to speed and availability, however. Many NoSQL databases opt for an eventually consistent model that allows for faster reading and writing.
Availability refers to whether the system can respond quickly to requests, even when failures occur. For example, you could set up a secondary database instance periodically synced with your primary instance. This second instance could be the primary instance should it go down. The downside is that databases spread across multiple servers can result in out-of-date or incorrect data being displayed, especially in an eventually consistent system. Depending on your business needs, however, slightly out-of-date data may be preferable to delays that prevent the whole system from functioning.
Failures are inevitable, but plenty of contingency plans can be implemented to ensure that data is still available and your app doesn’t crash. Having “no single point of failure” provides that an app can keep functioning without interruption, usually through replication or redundancy. Databases do this differently, with varying degrees of cost and footprint.