How To Choose the Right Streaming Database
This post helps you understand what SQL streaming is, when and why to use it, and discusses some key factors to consider when choosing the right streaming database.
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In today's world of real-time data processing and analytics, streaming databases have become an essential tool for businesses that want to stay ahead of the game. These databases are specifically designed to handle data that is generated continuously and at high volumes, making them perfect for use cases such as the Internet of Things (IoT), financial trading, and social media analytics. However, with so many options available in the market, choosing the right streaming database can be a daunting task.
This post helps you understand what SQL streaming is, the streaming database, when and why to use it, and discusses some key factors that you should consider when choosing the right streaming database for your business.
You will learn the following throughout the article:
- What streaming data is (event stream processing)
- What is the Streaming SQL method?
- Streaming Database features and use cases
- Top 5 streaming databases (both open-source and SaaS).
- Criteria for choosing a streaming database
Let's review quickly some concepts like what’s streaming data, streaming SQL, and databases in the next few sections.
What Is Streaming Data?
A stream is a sequence of events/data elements made available over time. Data streaming is a method of processing and analyzing data in real-time as it's generated by various sources such as sensors, e-commerce purchases, web and mobile applications, social networks, and many more. It involves the continuous and persistent collection, processing, and delivery of data in the form of events or messages.
You can ingest data from different data sources such as message brokers Kafka, Redpanda, Kinesis, Pulsar, or databases MySQL or PostgreSQL using their Change Data Capture (CDC), which is the process of identifying and capturing data changes.
What Is Streaming SQL?
Once you collect data, you can store this data in a streaming database (in the next section, it is explained), where it can be processed and analyzed using SQL queries with SQL streaming. It is a technique for processing real-time data streams using SQL queries. It allows businesses to use the same SQL language they use for batch processing to query and process data streams in real-time.
The data can be transformed, filtered, and aggregated in real-time from the stream into more useful outputs like materialized view (CREATE MATERIALIZED VIEW) to provide insights and enable automated decision-making.
Materialized views are typically used in situations where a complex query needs to be executed frequently or where the query result takes a long time to compute. By precomputing the result and storing it in a materialized view (virtual tables), queries can be executed more quickly and with less overhead. PostgreSQL, Microsoft SQL Server, RisingWave or Materialize support materialized views with automatic updates.
One of the key benefits of SQL streaming is that it allows businesses to leverage their existing SQL skills and infrastructure to process real-time data. This can be more efficient than having to learn new programming languages such as Java, and Scala, or tools to work with data streams.
What Is a Streaming Database?
A streaming database, also known as a real-time database, is a database management system that is designed to handle a continuous stream of data in real-time. It is optimized for processing and storing large volumes of data that arrive in a continuous and rapid stream.
A streaming database uses the same declarative SQL and the same abstractions (tables, columns, rows, views, indexes) as a traditional database. Unlike in traditional databases, data is stored in tables matching the structure of the writes (inserts, updates), and all the computation work happens on read queries (selects); streaming databases operate on a continuous basis, processing data as it arrives and saving it to persistent storage in the form of a materialized view. This allows for immediate analysis and response to real-time events, enabling businesses to make decisions and take actions based on the most up-to-date information.
Streaming databases typically use specialized data structures and algorithms that are optimized for fast and efficient data processing. They also support complex event processing (CEP) and other real-time analytics tools to help businesses gain insights and extract value from the data in real-time.
One of the features unique to streaming databases is the ability to update incrementally materialized views.
What Can You Do With the Stream Database?
Here are some of the things you can do with a streaming database:
- Collect and transform data from different streams/data sources, such as Apache Kafka.
- Create materialized views for the data that needs to be incrementally aggregated.
- Query complex stream data with simple SQL syntaxes.
- After aggregating and analyzing real-time data streams, you can use real-time analytics to trigger downstream applications.
5 Top Streaming Databases
As there are various types of streaming databases available, and numerous features are provided by each.
Below, I have shared the 5 top streaming databases (both open-source and SaaS) and note that they are not in the specific order of popularity or use.
- Amazon Kinesis.
- Apache Flink.
How To Select Your Streaming Databases
Choosing the right streaming data platform can be a challenging task, as there are several factors to consider. Here are some key considerations to keep in mind when selecting a streaming data platform:
Data Sources: Consider the types of data sources that the platform can ingest and process. Make sure the platform can handle the data sources you need. Kafka, Redpanda, Apache Pulsar, AWS Kinesis, Google Pub/Sub are mostly used as stream source services/message brokers. Or databases such as PostgreSQL or MySQL.
Scalability: Consider the ability of the platform to scale as your data needs grow. Some platforms may be limited in their ability to scale, while others can handle large volumes of data and multiple concurrent users. Make sure that the scaling process can be completed almost instantaneously without interrupting data processing. For example, the open-source project RisingWave dynamically partitions data into each compute node using a consistent hashing algorithm. These compute nodes collaborate by computing their unique portion of the data and then exchanging output with each other. In the case of streaming data platforms in cloud providers, they support auto-scaling features out-of-the-box, so it is not an issue.
Integration: Consider the ability of the platform to integrate with other systems and tools, such as BI and data analytics platforms you are currently using or plan to use in the future. Make sure the platform supports the protocols and APIs that you need to connect with your other systems. RisingWave has integration with many BI services that, include Grafana, Metabase, Apache Superset, and so on.
Performance: Consider the speed and efficiency of the platform. Some platforms may perform better than others in terms of query speed, data processing, and analysis. Therefore, you need to select a streaming database that can extract, transform and load millions of records in seconds. The key performance indicators (KPIs) for streaming data platforms are event rate, throughput (event rate times event size), latency, reliability, and the number of topics (for pub-sub architectures). Sometimes compared to JVM-based systems, a platform designed with a low-level programming language such as Rust can be super fast.
Security: Consider the security features of the platform, such as access controls, data encryption, and compliance certifications, to ensure your data is protected.
Ease of Use: Consider the ease of use of the platform, including its user interface, documentation, and support resources. Make sure the platform is easy to use and provides adequate support for your team.
Cost: Consider the cost of the platform, including licensing fees, maintenance costs, and any additional hardware or software requirements. Make sure the platform fits within your budget and provides a good return on investment.
In summary, streaming databases offer several unique features, including real-time data processing, event-driven architecture, continuous processing, low latency, scalability, support for various data formats, and flexibility. These features enable faster insights, better decision-making, and more efficient use of data in real-time applications.
The best streaming database for your use case will depend on your specific requirements, such as supported data sources, volume and velocity, data structure, scalability, performance, integration, and cost. It's important to carefully evaluate each option based on these factors to determine the best fit for your organization.
Published at DZone with permission of Bobur Umurzokov. See the original article here.
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