What is a streaming database?
A streaming database is a real-time data repository specifically designed to store, accumulate, process and enhance a data stream. A data stream is the stream/flow of data which is generated continuously from multiple resources. Data streams are incrementally processed through stream processing techniques without initially needing to access the complete data.
Streaming databases allows you to analyze your data in real-time. This means when some data flows into such databases, it is instantly processed, and then it immediately updates the outcome of associated registered queries. Hence, you can analyze your data in real-time and check how it has evolved, unlike in a traditional relational database.
The term “streaming database” is not just confined to an individual discrete class database management system. Instead, it is spread to numerous stream data handling databases in real-time. The databases handling this streaming data can lie in any database category, i.e., NoSQL databases, NewSQL databases, time-series databases, in-memory databases, or in-memory data grids.
How do streaming databases work?
When a data stream reaches a real-time streaming database, it is immediately processed. That data can directly be used in your application after analysis.
The data inputs to a streaming database are called data streams. These data streams are events of append-only sequences and are immutable. The input data to a database is categorized into two tiers. The first tier is streaming, while the second tier is user-built based on the behavior of these streams, which may be referred to as statistics of events. This input stream analysis is stored in columns and tables in the same way, it is stored in a traditional relational database. The following image presents the flowchart for the working of a streaming database:
The data reserved in a streaming database can be a web-generated log file, user information from social media platforms, e-commerce user trends, in-game user activity reports, or telemetry from various gadgets in data centers. This data is processed sequentially and incrementally and then used in making analyses like regression, filtering, sampling, and correlation.
This real-time data analysis opens various use cases for different industries. Companies can make use of this analysis and make relevant decisions based on the analysis results. Consider an example for an organization, where social media analysis is done through the resources from a streaming database. The organization can easily analyze the user behavior and activity. This observation can help the company to take new steps from the analyzed data to improve efficiency.
What is the difference between a Streaming database vs traditional relational database?
One of the major differences between streaming and a traditional relational database is the real-time application of streaming databases.
Traditional databases are simply a data repository, but these can work alongside streaming databases as a use case for large organizations. In such databases, when you input the data in columns and tables, the data will simply be integrated into the system and nothing is going to happen in front of your eyes. When a query is issued, it is simply scanned by the traditional database, and results are declared. You are blind to any process happening between the invocation of two queries, into the database.
On the other hand, streaming databases are a contrast to traditional databases. When a query is invoked, the data is immediately processed, and this process updates the results of all registered queries. You can read all the query results by viewing changes the data has suffered over time. This can be referred to as a continuous learning experience.
Companies use simple applications for data collection, and for processing statistical operations like minimum-maximum computation. With streaming databases, these applications can evolve and use real-time complex processing algorithms for computations and query results. These real-time processing algorithms are capable of conducting sophisticated analysis through several machine learning models.
A relational database also has some advantages that allow it to work alongside a streaming database. Relational databases help to maintain data accuracy and integrity. It is also capable of reducing the data redundancy to near zero. As relational databases do not carry real-time implementation features, it is relatively easier to implement processes along with the flexibility of data. But in this case, you cannot analyze the evolutions data has undergone before declaring the query result.
In short, it is not wrong to say that relational databases are useful, and many diverse organizations prefer using them alongside streaming databases for enhanced results.
What are the use cases for streaming databases?
A lot of IT companies are adapting to streaming databases. Here are some of both technical and business use-cases of streaming databases:
Allows the interface of various machine learning models
Offers an advantage of real-time data analysis alongside data generation
Assists you to enable alert and security systems for its real-time applications
Provides a better work efficiency than its competitor databases
Stipulates the benefit of supporting the maintenance use cases
Conducts better streaming data analysis from IoT resources providing minimalistic data
Allows you to interact with databases through SQL languages
Provides faster results through the real-time execution of queries in contrast to processing the idle batches of queries
Allows real-time data transfer from one built-in app to another build-in app for clients
Serves as a communication backbone for microservice architectures due to its real-time application
What are the top 5 streaming databases?
Have a look at some of the best streaming databases:
Materialize
Materialize is an SQL streaming database that is built over an open-source timely dataflow project. Its streaming database provides below listed features:
Direct connection to event streaming infrastructures
Interaction through Postgre SQL interface
Integrated plug-and-play tool
Enable users to ask questions related to living data streaming
Materialize facilitates you with a free trial period of 30 days. You can also purchase hourly plans depending on the size of your organization. Head towards Materialize for more information.
Memgraph
Memgraph is a graph application platform that provides its users with a fully-featured streaming database. The streaming database provided by Memgraph has the following features:
Carries high-availability replication
Better optimized for performance
Allows ACID transactions
Equipped with a hybrid storage engine
Provides On-disk persistency
Real-time data analyzation
Provides full Cypher support
Has high-availability
Is optimized for low latency
As for the pricing scheme, its streaming repository for the community edition is completely free.
Rockset
Rockset is a real-time analysis application that allows its user to build a real-time analysis through fast queries. Its database carries the following features:
Provides a low-latent search
Less operational burden
Connects with massive data streams
Allows aggregations
Rockset provides a free model to its users who are considered best for prototyping. It provides a $0.7989 per hour plan for better production rates. You can purchase your custom stream as well.
Vectorized
Vectorized is a recent project built with some newer streaming tools in January 2021. It provides the following features:
Provides an alternative to Apache Kafka engine
Allows open-source streaming processes
Newer streaming tools
Better performance
Head over to Vectorized for its enterprise pricing schemes.
Kafka
Apache Kafka provides a streaming database from multiple source systems. Kafka proposes to have the below-mentioned features for its streaming databases:
Real-time analysis of big data streams
Carries fault-tolerant features
Durable, scalable, and fast system
Allows tracking of IoT data
You can download the Kafka software from its website.
