Databases are one of the most important parts of any workload. Every organization spends a significant portion of its budget on databases and their management. Traditional databases have been around for decades, and SQL-based databases have been the standard for a long time. However, with changing times, the expectations organizations have from databases have evolved. Modern workloads and infrastructures require databases to be faster and more flexible. Traditional SQL-based databases tend to provide stability and decent speed. However, for distributed, cloud-native workloads, you need databases that can keep up with the speed at which different services will query the data. Here’s a look at the most common database types and what they are meant for.
Traditional databases store information in a secure, structured, and stable way. This has its advantages like security, guaranteed speed, and reliability. However, conventional, SQL-based databases aren’t compatible with modern databases and may pose issues during migration if you ever want to venture into the NoSQL world. Also, scaling SQL databases can be a pain because they don’t allow for sharding automatically. This means that if you want to simplify your database into smaller, more efficient subsets, you will have to spend weeks doing it. RDBMS also doesn’t work well with unstructured and semistructured data. This can be an issue if you want to store data collected from IoT devices. Modern SQL-based databases help bridge the gap between traditional databases and NoSQL databases by providing better compatibility with cloud-native applications. While there are plenty of new SQL-based database offerings available in the market, NoSQL database adoption is constantly on the rise.
NoSQL databases are the new rage in the market. These databases help users store heterogeneous data that cannot be stored in SQL-based databases. The rigidity of an RDBMS makes it incompatible with unstructured or semi-structured data. NoSQL databases tend to be schema-agnostic, which allows them to store unstructured data. This unstructured data can be consumed by powerful business intelligence tools to provide insights. Organizations can also temporarily hold unstructured data in NoSQL databases so they can structure it later. NoSQL databases are also extremely scalable because they’re fragmented and distributed across different data centers. With NoSQL databases, you don’t have to prepare your data in any way. Since the data is stored in such a loose fashion, you can add anything to your database, which helps when it comes to web apps and Big Data use cases.
NoSQL is an option worth exploring, but it has a few snags that should be considered beforehand. The biggest problem is the newness of these kinds of databases. Finding solutions to some issues or creating POCs for particular use-cases may become a challenge for developers because it can be hard finding documentation. Another critical pitfall to be aware of is “eventual consistency.” Eventual consistency refers to the lag between different nodes of a NoSQL database. Since NoSQL doesn’t perform ACID transactions, changes in a database may take some time to reflect across various nodes. You run the risk of fetching stale data from a node that hasn’t yet been updated.
Let’s take a look at some NoSQL databases meant for modern use cases.
This type of non-relational database stores data in the form of key-value pairs — the key acts as a unique identifier of the value it holds. Values can be scalar data types like integer and string or complex objects like JSON, arrays, and BLOBs. You can only query the key and never the value. Key-value stores allow you to add and delete key-value pairs quickly. This is extremely important if your use-case requires you to constantly perform read and write operations. Key-value stores also provide fast in-memory access, which is quite important in many use-cases. These databases are also easily partitionable and can be horizontally scaled if needed.
Document databases are another type of NoSQL database. These databases allow you to store data in lightweight, human-readable document formats that hold diverse data that doesn’t have to be modeled in a rigid way. Document databases store data that is often queried together at the same location, so the performance is always top-notch. With document databases, your data can easily map to objects inside your application code, and there is no need to run expensive queries. Document databases are also easy to format and can be configured easily as the application’s needs evolve. Another essential feature document databases bring to the table is ACID transactions. So you can rest assured that all your nodes will fetch a consistent copy of data. Documents are independent storage units, so they can be easily distributed across different data centers and geographical locations, which is extremely important when working with modern workloads. Document databases are also resilient as they provide self-replication functionality to ensure high availability.
Graph stores are purpose-built databases that give the relationship between data the same importance as the data itself. Unlike traditional databases, the relationship between the data is stored inside the database itself and isn’t calculated at query time. This makes data retrieval faster as only the data that is needed gets accessed and unnecessary data is never even touched, which saves time. Graph store consists of two components, the nodes, and the edges. Nodes contain the data, which can be key-value pairs, and the edges hold relationship information like start node, end node, and direction, among other information. A query traverses through the graph store based on these relationships that define parent-child relationships between data. Graph stores are useful in social media, recommendation engines, and fraud detection because these use cases require you to create relationships between your data and to query these relationships quickly.
Column databases, as the name suggests, store data in columnar format instead of the traditional row-based storage options. This allows you to store more data in less storage size. Columnar databases use keyspace to hold the data. Keyspace is a logical container that holds several rows. Each of these rows has a unique key that acts as an identifier and multiple columns that hold diverse varieties of semi-structured and unstructured data. This data can then be queried using row keys. Columnar databases are highly scalable and can be distributed to different locations and across your entire cluster. They provide massive parallel-processing speed, which makes them ideal for analytical projects. Columnar databases are also quite flexible as the columns don’t have to follow any rigid rules. You can store anything in a column and add and remove these columns whenever you need. However, adding a new record requires you to make changes to all the tables in your database.
Database types: It’s clear NoSQL is the future
NoSQL databases are the future. Their growing popularity will eventually lead to a rise in new use cases for these databases. With constant innovation and enough theoretical and practical knowledge, organizations can use these databases to improve their workload performances. However, some organizations are still not on board with the NoSQL databases due to decades of trust in traditional RDBMS. Ultimately, it will be the need to have more distributed databases that can be easily queried without having to extensively define data and data types that will make organizations consider these modern alternatives.
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