Handling Real-time events can be really hard for the developers, they need to juggle with a lot of tools and configurations just to handle the large amount of data in the App.
Mordern Tech stack can help with this problem easily.
Event Driven Apps are apps that depend on triggers based on events rather than just working with regular calls or methods.
Events could be messages, timers, API calls, and clicking of buttons, and it could be the data event such as insertion, updation or deletion of data.
We already have technologies to handle the real-time data, then why do we need to upgrade and look into the modern stack to build real-time event-driven applications? These modern technology stacks can handle huge amount of data and highly responsive to events and give us feedback and update as per change of information.
Apps and tech Stack that Can be used :
Reactjs for Fronted
Spring boot in the backend
A data streaming Software, Apache Kafka
Rising Wave to improve the performance of data Streaming software and Database capabilities.
What Else can be used?
Node js for backend, Apache Flink , Spring Cloud Stream, Aws Lambda and many more...
Need of Data Streaming Layer in our Event Driven application
The high volume of data and real-time update of data require multiple stream of data to be handled properly to match the rapid pace of Data, that traditional or relational database are not designed to handle these kind of operations, tradinational databases are handle discrete, immutable data transactions that needs to be committed inside the database in a consistent, serialized manner; this approach worked well for predictable and consistent data flows.
We can handle the stream of data with traditional database such as postgreSQL or SQL Server-
Materialized View: Materialized view is predefined cached data view that help us to obtain real time result, by keeping an eye on changes we can create the real time materilized database view.
CDC (Change Data Capture)- it is a software capabilty used to compare a source database(old ) with destination (database with change), it can also find the database change in real time
Triggers- Triggers are one good way to handle the change in data, triggers automatically updates or execute when there is change of the data .
Streaming Databases are the upgarde to SQL databases for real time queries.
https://www.risingwave.com/blog/Rethinking_stream_processing_and_streaming_databases/
Apache Kafka and Role in Data Streaming Services
Think of Apache Kafka as Central hub for taking all the data streams, it process the data and stores the data streams, if necessary. it works on the Publisher and Subscription Model. All of this functionality is provided in a distributed, highly scalable, elastic, fault-tolerant, and secure manner. Kafka can be deployed on bare-metal hardware, virtual machines, containers, and on-premises as well as in the cloud, you can choose your favourite platform and vendor.
Apache Kafka is fully distributed communication system having Server and client, it is fault tolerent and scalable , It works on TCP network protocol. Kafka provides built-in support for stream processing, and integration with tools, connectors, systems and plugins.
Kafka allows us to analyze, transform, and aggregate data streams in real-time. We can use Kafka Streams or other stream processing tools to perform real-time processing of event data as it flows through the system.
Can we do Even better for the Real-time Data?
Yes, we can. Because...
Data is CLEAR. Data is Streaming. Data is BEAUTIFUL.
with #RisingWave -
Rising Wave, SQL-Based stream processing platform, delivering fresh and consistent results. RisingWave is built on cloud-native architecture to integrate well with the tools and technologies of the modern data stack.
RisingWave consumes streaming data, performs incremental computations when new data comes in, and updates results dynamically. As a database system, RisingWave maintains results in its own storage so that users can access data efficiently.
RisingWave accepts data from sources like Apache Kafka, Apache Pulsar, Amazon Kinesis, Redpanda, and materialized CDC sources.
Data in RisingWave can be output to external targets such as message brokers, data warehouses, and data lakes for storage or additional processing.
Why and How Rising wave can help?
you can use SQL as the interface
Realtime and Materialized view
it is cost efficient, offers users on-demand deployment with elastic scaling of resources.
As a cloud Native its highly Elastic and store data on offers users on-demand deployment with elastic scaling of resources
works along with Kafka and other streaming connectors
Now you know what is the modern Stacks that can help you to scale your Event driven application.
Read How we can implement it practically :
Modern stack to build a real-time event-driven app - Bobur Umurzokov
What is RisingWave? | RisingWave
https://kafka.apache.org/intro
join Rising Wave youtube- https://bit.ly/risingwaveyt
