Key Technologies In Streaming Data

Stream Processing Technology

Though working with streaming data is challenging, fortunately more tools and platforms are entering that market that make it easier to handle. This includes infrastructure tooling for managing high volumes of events, stream processing frameworks aimed at developers, and databases and data stores which are optimised for working with this type of data.

Streaming Platforms

The key component at the heart of your streaming solution is likely to be an event streaming platform such as Apache Kafka or Apache Pulsar.

The primary role of these platforms is to enable the transport of large volumes of event data from sources to destinations through a publish/subscribe or broadcast model.

Some of the key features of event streaming platforms include:

• High throughput and low latency: Event streaming platforms are designed to handle high volumes of event data in real-time with low latency, ensuring that data is processed and analyzed in a timely manner.

• Scalability and fault tolerance: Event streaming platforms are designed to be highly scalable and fault-tolerant, allowing them to handle large volumes of data and to continue operating even in the event of system failures.

• Distributed architecture: Event streaming platforms typically have a distributed architecture, with multiple nodes working together to manage event data and ensure that data is processed efficiently and reliably.

• Event-driven architecture: Event streaming platforms are based on an event-driven architecture, where events are the primary means of communication between applications and services.

• Data persistence: Event streaming platforms typically provide data persistence, allowing event data to be stored for future analysis or replay.

• Real-time processing and analysis: Event streaming platforms support real-time processing and analysis of event data, enabling organisations to make timely decisions based on the most up-to-date information.

Event streaming platforms such as these are powerful tools for managing event data, making them a fundamental building block of modern data processing and analytics architectures.

Stream Processing Frameworks

Stream processing frameworks such as Apache Flink and Kafka Streams are software frameworks that enable developers to build and deploy real-time data processing applications.

These frameworks are designed to handle the challenges of processing and analyzing continuous streams of data, such as those generated by IoT devices, sensors, or social media feeds.

Some of the key features of stream processing frameworks include:

• High-throughput processing: Stream processing frameworks are designed to handle large volumes of data with high throughput, enabling organisations to process and analyze real-time data at scale.

• Low-latency processing: Stream processing frameworks are optimized for low-latency processing, allowing organisations to make real-time decisions based on incoming data.

• Fault tolerance and high availability: Stream processing frameworks are designed to be highly resilient, with features like automatic failover and replication to ensure continuous processing even in the event of system failures.

• Support for complex data transformations: Stream processing frameworks provide powerful APIs for transforming and aggregating data, allowing developers to implement complex data processing pipelines with ease.

• Integration with other data systems: Stream processing frameworks can be integrated with other data systems like databases, message queues, and data lakes, enabling organisations to build end-to-end data processing pipelines.

• Scalability: Stream processing frameworks are designed to be highly scalable, with support for distributed processing across multiple nodes and clusters.

Stream processing frameworks are powerful tools for building real-time data processing applications, enabling organisations to extract insights and make real-time decisions based on the most up-to-date information.

Real Time Databases

Real-time data stores such as ClickHouse and Firebase are databases that are optimized for handling high volumes of real-time data. These databases are designed to enable fast, efficient, and scalable storage and retrieval of data, making them ideal for applications that require fast access to the most up-to-date information.