Data Stream Management System Architecture
Last Updated :
11 Sep, 2024
A Database Management System (DBMS) is a software system that is designed to manage and organize data in a structured manner. It allows users to create, modify, and query a database, as well as manage the security and access controls for that database.
The Data Stream Management System manages continuous streams of data with very fast changes in real-time. Unlike other databases which may be static data, its source might include sensors or social media. It thus offers real-time insight and rapid decision-making in those applications that need immediate data analysis and reporting.
What is DSMS Architecture?
DSMS stands for data stream management system. It is nothing but a software application just like DBMS (database management system) but it involves processing and management of a continuously flowing data stream rather than static data like Excel PDF or other files. It is generally used to deal data streams from with various sources which include sensor data, social media fields, financial reports, etc.
Just like DBMS, DSMS also provides a wide range of operations like storage, processing, analyzing, integration also helps to generate the visualization and report only used for data streams.
There are wide range of DSMS applications available in the market among them Apache Flint, Apache Kafka, Apache Storm, Amazon kinesis, etc. DSMS processes 2 types of queries standard queries and ad hoc queries.
Data stream Management system architecture
DSMS consists of various layer which are dedicated to perform particular operation which are as follows:
1. Data source Layer
The first layer of DSMS is data source layer as it name suggest it is comprises of all the data sources which includes sensors, social media feeds, financial market, stock markets etc. In the layer capturing and parsing of data stream happens. Basically it is the collection layer which collects the data.
2. Data Ingestion Layer
You can consider this layer as bridge between data source layer and processing layer. The main purpose of this layer is to handle the flow of data i.e., data flow control, data buffering and data routing.
3. Processing Layer
This layer consider as heart of DSMS architecture it is functional layer of DSMS applications. It process the data streams in real time. To perform processing it is uses processing engines like Apache flink or Apache storm etc., The main function of this layer is to filter, transform, aggregate and enriching the data stream. This can be done by derive insights and detect patterns.
4. Storage Layer
Once data is process we need to store the processed data in any storage unit. Storage layer consist of various storage like NoSQL database, distributed database etc., It helps to ensure data durability and availability of data in case of system failure.
5. Querying Layer
As mentioned above it support 2 types of query ad hoc query and standard query. This layer provides the tools which can be used for querying and analyzing the stored data stream. It also have SQL like query languages or programming API. This queries can be question like how many entries are done? which type of data is inserted? etc.,
6. Visualization and Reporting Layer
This layer provides tools for perform visualization like charts, pie chart, histogram etc., On the basis of this visual representation it also helps to generate the report for analysis.
7. Integration Layer
This layer responsible for integrating DSMS application with traditional system, business intelligence tools, data warehouses, ML application, NLP applications. It helps to improve already present running applications.
The layers are responsible for working of DSMS applications. It provides scalable and fault tolerance application which can handle huge volume of streaming data. These layer can change according to the business requirements some may include all layer some may exclude layers.
Conclusion
A DSMS architecture would, therefore, open the way for real-time processing of continuous data streams, components to include data ingestion, stream processing, and query facilities to allow analyses with low latency and provide instant insights. Such an architecture, in effect, can swiftly monitor and effectively handle decision-making, and is apt for those applications that have updated information emanating from a variety of source types.
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