Streaming databases are essential tools for businesses that want to keep up with the times in today’s data processing and analytics world. These databases can handle data generated at high volumes and in a continuous fashion. They are ideal for applications such as financial trading, social media analytics, and the Internet of Things (IoT). It can be difficult to choose the right streaming database among the many available.
This article will help you to understand SQL streaming, the streaming database and when it is best used. It also discusses key considerations that you need to consider when selecting the right streaming database for your business.
What is Streaming Data?
A stream is a collection of data elements or events that are made available over time. Data streaming allows you to process and analyze data in real time, using various sources, such as sensors, eCommerce purchases, mobile apps, social networks, and other resources. It is the continuous and persistent collection of, processing, delivery, and delivery of data in the form of events or messages.
Data can be ingested from many data sources, such as Redpanda message brokers, Redpanda, Kinesis, and Pulsar, or databases MySQL or PostgreSQL via their Change Data Capture, (CDC), which is the process for identifying and capturing data changes.
What is Streaming SQL?
Once data is collected, it can be stored in a streaming database (in the next section it will be explained), which can then be processed using SQL streaming queries. This is a method of processing real-time data streams with SQL queries. This allows businesses to use the exact same SQL language that they use to batch process to query and process real-time data streams.
Data can be transformed, filtered, and aggregated from real-time streams into more useful outputs, such as materialized view, to provide insights and allow for automated decision-making.
SQL streaming offers many key benefits. It allows businesses to use their existing SQL skills and infrastructure for real-time data processing. This is more efficient than learning new programming languages like Scala or Java, or the tools needed to work with data streams.
Also read: 5 Best Tips To Optimize Database Performance
What is a Streaming Database?
A streaming database (also known as a real-time database) is a database management software that can handle continuous streams of data in real-time. It can store and process large amounts of data in a continuous stream.
A streaming database uses the exact same declarative SQL and abstractions (tables columns rows views indexes) that a traditional database. Data is stored in tables that match the structure of the write (inserts and updates) and all computation work takes place on read queries (selects). Streaming databases work continuously by processing data as they arrive and saving it to persistent storage in a materialized view. This allows businesses to quickly analyze and respond to real-time events. Businesses can make decisions and take action based on the most current information.
Streaming databases often use special data structures and algorithms that optimize data processing for speed and efficiency. They support complex event processing (CEP), and other real-time analytical tools that help businesses gain insight and extract value out of the data in real-time.
One of the unique features of streaming databases is the ability to incrementally materialize views.
What can you do with the Stream Database?
Here are some things you can do using a streaming database.
- You can collect and transform data from various streams/data sources such as Apache Kafka.
- Materialized views can be created for data that is incrementally aggregated.
- Simple SQL syntaxes allow you to query complex stream data.
- Once you have analyzed and aggregated real-time data streams, real-time analytics can be used to trigger downstream applications.
Top 5 Streaming Databases
There are many streaming databases to choose from, each offering a variety of features.
Below are the top 5 streaming databases (both SaaS and open-source) that I share. Please note that they may not be in the exact order of popularity or usage.
- RisingWave.
- Materialize.
- Amazon Kinesis
- Confluent.
- Apache Flink.
Also read: Top 15 Database For Web Applications
How to Select Your Streaming Databases
It can be difficult to choose the right streaming platform because there are many factors to consider. These are the key points to remember when choosing a streaming platform.
- Data sources: Think about the data sources the platform can process and ingest. You need to ensure that the platform can manage the data sources you require. Kafka and Redpanda are used mainly as stream source services/message brokers. Oder databases like PostgreSQL and MySQL.
- Scalability: The platform’s ability to scale with your data requirements growth is important. Some platforms are limited in their ability scale while others are capable of handling large data volumes and multiple concurrent users. Scaling should be done quickly and without interfering with data processing. RisingWave, an open-source project, dynamically divides data between compute nodes using a consistent hashing method. These compute nodes work together by computing their own unique portions of the data, and then exchanging output with one another. Streaming data platforms that are cloud-based support auto-scaling, so it’s not a problem.
- Integration: The platform’s ability to integrate with other tools and systems, such as data analytics and BI platforms, is something you should consider. Check that the platform supports all protocols and APIs you require to connect to your other systems. RisingWave integrates with many BI services, including Metabase, Grafana, and Apache Superset.
- Performance: Take into account the platform’s speed and efficiency. Some platforms perform better than others when it comes to query speed, data processing, and analysis. A streaming database must be able to quickly extract, transform and load millions of records. For streaming data platforms, the key performance indicators (KPIs), are event rate, throughput, latency, reliability, and the number of topics (for pub sub-architectures). A platform built with Rust, a low-level programming language, can be faster than JVM-based systems.
- Security: Take into account the security features of this platform such as access controls and data encryption.
- User-friendliness: Take into account the platform’s ease of use, including its documentation, user interface, and support resources. You should ensure that the platform is simple to use and offers adequate support for your team.
- Price: Take into account the costs of the platform including maintenance and licensing fees. You should ensure that the platform is within your budget and offers a good return.
Summary
Streaming databases provide unique features such as real-time data processing and event-driven architecture. They also support scalability, flexibility, support for different data formats, low latency, scalability, flexibility, and scalable scaling. These features allow for faster insights and better decision-making. They also make it easier to use data in real-time applications.
Your specific requirements will determine the best streaming database to suit your needs. This includes supported data sources, volume, velocity, data structure, and scalability. To find the right fit for your company, it is important to evaluate each option using these criteria.
