Streaming databases are designed to process, analyze, and store streaming data. In this article, we delve into 8 leading streaming databases, each of which boasts a distinct array of features and capabilities, making them highly adaptable to a diverse range of use cases.
In today's fast-paced world, organizations generate data at an unprecedented rate. To extract valuable insights from this data, it's essential to process it in real-time. Streaming databases, designed to process, analyze, and store streaming data, have become crucial for businesses to make real-time decisions. This article discusses 8 top streaming databases well-equipped for real-time stream processing.
RisingWave is a distributed SQL streaming database fully compatible with PostgreSQL. It supports a variety of data sources and sinks, including but not limited to messaging systems, OLAP databases, data warehouses, data lakes, and OLTP databases.
RisingWave can automatically refresh materialized views in a consistent manner, allowing users to query data in materialized views concurrently. It adopts a decoupled compute-storage architecture and leverages tiered storage to optimize performance in the cloud. RisingWave also supports many advanced stream processing functionalities, including exactly-once semantics, watermarks, and window functions.
Started in 2020, RisingWave was built in Rust from scratch, without relying on any existing products like PostgreSQL, Apache Flink, or ClickHouse. It has been deployed in production in tens of enterprises and fast-growing companies.
In April 2022, RisingWave was open-sourced under the Apache 2.0 license. RisingWave Cloud is the cloud service that hosts RisingWave.
Yingjun Wu, the founder of RisingWave, previously worked at AWS Redshift and IBM Almaden Research Center. He holds a PhD in database systems and stream processing. In its early stages, RisingWave borrowed some design ideas from DuckDB, a popular PostgreSQL-compatible OLAP database system. DuckDB, in its early stages, borrowed some design ideas from Peloton, a research project developed by the database group at Carnegie Mellon University, where Yingjun was one of the leading developers.
Arroyo is a distributed stream processing engine developed using the Rust programming language. Its primary purpose is to efficiently perform complex computations on continuous streams of data while maintaining stateful operations. This enables users to analyze high-volume real-time data and obtain results within a very short timeframe.
The project's main objective is to make real-time data analysis more accessible to a wider audience. By open-sourcing its core engine, Arroyo aims to foster a community around real-time data infrastructure. It is built as a Rust data project, leveraging Rust data libraries like DataFusion.
Arroyo's architecture has been optimized to provide strong support for SQL queries, starting from the SQL planner and extending to the storage layer. This ensures efficient handling of SQL queries and consistent performance. The engine is designed to enable users to construct reliable and efficient streaming pipelines, even without specialized knowledge of streaming technologies.
One of Arroyo's notable features is its compatibility with modern cloud environments. It supports serverless operations, allowing pipelines to dynamically scale, recover from failures, and adaptively reschedule tasks. This aligns well with the demands of cloud-native applications.
The project is open-source and released under the Apache 2.0 license.
HStreamDB is a streaming database platform designed to enable real-time data integration and synchronization. The platform is built upon a cloud-native architecture, separating compute and storage layers for scalable and independent horizontal scaling. It draws inspiration from frameworks like Kafka Connect, Pulsar IO, and Airbyte to create HStream IO, which facilitates data integration with external systems.
HStreamDB's optimized storage engine ensures low-latency persistent storage for streaming data and replicates data across multiple storage nodes for enhanced reliability. It supports hierarchical data storage and automated historical data migration to cost-effective storage services. The platform employs a publish-subscribe model for low-latency data subscription delivery, even during cluster failures.
With a focus on flexibility, scalability, and efficient scaling, HStreamDB offers online cluster scaling, allowing dynamic expansion and contraction without the need for data repartitioning or extensive data copying. Overall, HStreamDB aims to provide a comprehensive solution for managing real-time streaming data through its versatile architecture and integration capabilities.
HStreamDB is released under BSD license.
KsqlDB is a specialized database optimized for handling streaming data and assisting developers in constructing applications that process data streams using Apache Kafka. It is a fully managed service available on the Confluent Cloud.
A significant advantage of KsqlDB is its support for SQL interactions, allowing users to directly create tables. Additionally, it enables the creation of materialized views, tables that continuously and incrementally update aggregate calculations as new data streams in. This ensures quick query responses and guarantees that rows associated with a particular key are located in the same partition.
KsqlDB supports two types of queries, both capable of accessing data from a materialized view in a table. Pull queries conclude in a typical relational manner, whereas push queries remain active to capture changes in the data stream.
KsqlDB is deeply integrated with Apache Kafka and is built on top of Kafka streams. Internally, it utilizes Kafka for buffering data exchanges between different operators and employs RocksDB for storing the state required for computing aggregates and joins.
Finally, KsqlDB is available under the Confluent Community License, which provides open access to its source code.
Materialize is a streaming database that leverages SQL for processing. It is compatible with PostgreSQL, allowing it to integrate with numerous systems that already have PostgreSQL integration. One of its key features is its ability to automatically refresh materialized views in a consistent manner, enabling users to query data in these views concurrently.
Materialize is built upon the foundation of Timely Dataflow, a Microsoft research project developed to support incremental and iterative processing. To achieve fault tolerance, Materialize employs a hot-standby model. While the source-available version of Materialize operates as a single-node in-memory database, Materialize Cloud is designed to be distributed and cloud-native.
Materialize was officially released on February 20, 2020, under a Business Source License (BSL). Materialize Cloud is the cloud-hosted variant of Materialize. The company was founded by Arjun Narayan, a former early engineer at Cockroach Labs.
EventStoreDB is a specialized operational database designed to store important data using streams of unalterable events. The platform is specifically tailored to accommodate Event Sourcing practices, offering a robust solution for constructing systems based on event-driven principles.
Key features of EventStoreDB include guaranteed write operations, a well-structured concurrency model, and precise stream and stream APIs. These characteristics position EventStoreDB as a preferred choice for systems that rely on event-driven approaches, especially when compared to databases originally intended for different use cases.
EventStoreDB follows a source-available approach, provided under the EventStore license. In addition to its core offering, Event Store Cloud is a fully managed cloud service that simplifies the deployment and management of applications utilizing EventStoreDB.
Timeplus is a data analytics platform designed with a focus on streaming-first analytics. It provides a range of capabilities that enable organizations to process both streaming and historical data quickly and intuitively. The platform empowers data and platform engineers to unlock the value of streaming data using SQL.
The platform features a high-performance streaming SQL engine that leverages vectorized data computing and modern parallel processing technology, allowing for super high efficiency in processing streaming data.
Beyond being just a streaming SQL database, Timeplus offers a complete suite of analytic functionalities. These include various data source connections, an interactive web client for real-time data analysis, real-time visualizations and dashboards, and an API for data interaction and sending analytic results to downstream data systems. It also enables setting up alerts for real-time actions based on anomalies detected in the streaming analytic results.
Timeplus has developed its own engine for stream processing and uses ClickHouse for Online Analytical Processing (OLAP) processing.
DeltaStream is a stream processing platform designed to facilitate the development and deployment of streaming applications. It is built on Apache Flink, an open-source stream processing framework. This platform offers a unified SQL interface, which allows users to query and process streaming data using standard SQL syntax. This feature is particularly useful for those familiar with SQL, as it eliminates the need to learn a new language or syntax for stream processing.
Additionally, DeltaStream incorporates a serverless architecture, which simplifies the deployment and scaling of streaming applications. This is crucial for applications that need to be scaled up quickly to handle surges in data volume. Furthermore, DeltaStream is designed with a secure and scalable infrastructure, capable of managing large volumes of streaming data. This ensures that the platform can handle the demands of large-scale applications while maintaining data security.
DeltaStream was founded by Hojjat Jafarpour, who was a co-creator of the KsqlDB project.
Conclusion
In conclusion, streaming databases play a vital role in the real-time processing and analysis of data. Each offers a unique set of features and capabilities, making them well-suited for different use cases. When choosing a streaming database, it is essential to consider your specific needs and requirements, such as the type of data you will be processing, the volume of data, and the level of performance and reliability you need.
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