Peter Sondergaard once said that information is the oil of the 21st century and analytics is the combustion engine. Nowadays it is difficult to disagree with him.
Like large capacity tanks for storing oils, databases are needed to store information. Due to the increasing amount of information, databases have evolved too much since they were first available.
In this article, we will explore databases by analyzing the answers to fundamental questions. Then, we will discover the current popular databases by dividing them into meaningful divisions. Buckle up and let's get started!
Let's start with an overview of the varied database landscape. In this section, we will summarize the many databases accessible for different purposes and circumstances into five different categories:
- Lightweight databases
- Enterprise-grade relational databases
- NoSQL databases
- NewSQL and distributed databases
- Specialized and niche databases
Let's start with lightweight databases.
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In this section, we will explore lightweight databases, vital elements for applications that operate on a smaller scale.
They are known for their effectiveness and simplicity. These databases are ideal for businesses that do not require a sophisticated and heavy database system.
mysql
MySQL is all the rage, especially for websites. It is fast and has many useful features. A large community supports you, there is a lot of help available. However, getting MySQL to handle all that extra work can be a challenge as your application grows. Might be better for complicated data analysis.
SQLite
This simple and small database is great for small programs or applications. It's easy to move around because it's just one file. But, if many people use the application simultaneously, SQLite might need help keeping up. There are better options for really large or complex applications.
PostgreSQL
PostgreSQL is free to use and has many interesting features. It's great for handling complex data and doing complicated things with that data. But, if your application needs to write a lot of data all the time, PostgreSQL might slow down.
mariadb
MariaDB improves the performance and security of MySQL. Since MariaDB has similar features to MySQL, you can make the transition quickly if you know MySQL. However, it is somewhat less common than MySQL.
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Enterprise-grade relational databases are suitable for large and complicated applications. They offer enhanced security and extensive data management, which are business needs of companies.
Microsoft SQL Server
Microsoft SQL Server is a good choice if you build applications using other Microsoft products, such as .NET. It is known to be remarkably safe and reliable. The downside is that it primarily runs on Windows and can be expensive.
Oracle Database
Oracle is known to be very reliable and robust. It is the best option for large companies. It has advanced security and can handle a lot of data well. But Oracle is expensive, has many complex rules to use, and needs to be learned.
IBMDB2
IBM DB2 is made for large companies. It's great for analyzing data and learning from it. It is reliable and can handle a lot of work. But it's difficult to manage and is typically best for large organizations or unique business needs.
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NoSQL databases offer flexibility and scalability. This sector covers databases for unstructured and semi-structured data that meet current and dynamic data needs.
MongoDB
This flexible database does not need a fixed structure, which is great for managing many different types of data. It can grow to handle more work and has a powerful way to find data.
But it might be better for tasks that need complex connections between data, as some traditional databases do.
cassandra
Cassandra has been designed to handle large amounts of data on many computers. It is very scalable and reliable. But planning how to store your data in Cassandra can be complicated, and it's harder to learn if you're used to traditional databases.
sofadb
CouchDB is suitable for web applications that need a simple, scalable database that uses JSON, a popular data format. It has a great web interface and can copy data between places well. However, it might be better than others for very complex searches or large amounts of data.
DynamoDB
DynamoDB is part of Amazon cloud services. It is good at adapting to changing workloads and can handle a lot of traffic. But your options for searching and organizing data are limited. Therefore, it can be expensive.
neo4j
Neo4j is great for connected data, such as social networks or recommendation systems. It is special because it can handle complex relationships between data well. But it's a niche and can be difficult to set up.
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They combine the stability of conventional databases with the scalability of NoSQL systems; Let's start discovering them.
Hive/Hadoop
Hive, part of the Hadoop ecosystem, is great for processing large data sets using simple queries. It is designed to handle big data and works well with complex data analysis. However, Hive can be slow with real-time queries and may not be the best choice for fast, interactive applications.
Apache Kafka
Apache Kafka is primarily a streaming platform that is excellent for processing and analyzing data streams in real time. It is highly scalable and reliable for managing large data flows. However, Kafka is more of a data processing tool than a traditional database, so it is complex to set up and requires specific expertise to manage effectively.
green plum
Greenplum can handle big data analysis very well. It can grow to handle more data and works well with machine learning tools. However, configuring and managing it can be complex and requires a lot of computing resources.
CockroachDB
It's solid and consistent, even across many computers. It can easily grow and handle transactions like traditional databases. However, its design is complex and may be too much for smaller applications.
amazonian auroras
Amazon Aurora is part of the Amazon cloud. It works fast and is compatible with MySQL and PostgreSQL. Designed for the cloud, it is reliable and can handle a lot of work. However, it can be expensive if used more, and for the most part it is only in the Amazon cloud.
Amazon Aurora is part of the Amazon cloud. It works fast and is compatible with MySQL and PostgreSQL. Designed for the cloud, it is reliable and can handle a lot of work. However, it can be expensive if used more, and for the most part it is only in the Amazon cloud.
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Finally, we explore specialized and niche databases. These databases are designed for specific types of data and offer features that regular databases cannot offer. From real-time analytics to complicated data models, this section covers custom technologies.
elastic search
Elasticsearch is ideal for text search and analysis. It can handle a large amount of data and grows well. However, it can be difficult to manage in large configurations and is usually not the central database.
Rethink DB
RethinkDB is designed for real-time web applications. Allows flexible data organization and easy updates. However, its development has slowed down, so it is less advanced than others and support may be limited.
ArangoDB
ArangoDB supports different types of data such as documents and graphs and works well for various needs. It works well, but it could be better known, which could mean a more difficult learning process and less help from the community.
DBInflux
InfluxDB is optimized for data that changes over time, such as in IoT. It is excellent for real-time monitoring and analysis. However, it is specialized in time-based data, so it is not ideal for all database needs.
Redis
Redis is super fast because it stores data in memory, making it great for quick access to real-time data and applications. However, the amount of data is limited to the size of the memory, and ensuring that data remains secure over time can be difficult.
If you want to discover database interview questions, check out this one, Database Interview Questions.
We have just explored even the deepest corners of the database world by showing its strengths and weaknesses and dividing them into categories.
Zig Ziglar once said, “Repetition is the mother of learning.” His words also apply to this knowledge. So if you want to solidify your understanding, remember to practice repetition.
Nate Rosidi He is a data scientist and in product strategy. He is also an adjunct professor of analytics and is the founder of StrataScratch, a platform that helps data scientists prepare for their interviews with real questions from top companies. Nate writes about the latest trends in the career market, provides interview tips, shares data science projects, and covers all things SQL.
