Streaming Data Architecture: All about it

Streaming data means data continuously generated at high velocity. To put it more clearly, a data stream is formed when certain events continuously generate data. Example of it is when a person clicks a link on a website or mobile application, or pressure measured by an IoT device sensor. There are two forms for the streaming data, unstructured or semi-structured. Usually there will be key-value pairs in JSON or XML. 

Streaming technologies are gaining more popularity over the last years, although they are not new. Those who are eager to know how to handle data streaming, should consider certain questions about what it is, the difference between stream processing and real-time one, the designing of it, its components, its benefits etc. 

We can help you with these as Streaming Data is a part of our Data Science Services. 

Streaming Data Architecture: What it is

In Streaming Data Architecture, software components are built and connected together which, from various sources, ingest and process streaming data. It processes data immediately from its collection. It also allocates it to the right storage and may also triggers further procedures like analytics, data manipulation or further processing. 

The difference between stream processing and real-time operations is that the former are about actions taken on the data, and the latter are about reactions to the data. 

Real time solutions executes data shortly after it is arrived, and the reaction will be immediate. 

On the opposite stream processing is a continuous computation that happens when data flows through the system. There is no deadline to process the data. The two things that define the success of stream data architecture are the equality of input and output rates of data, and availability of memory to store the inputs required. If the frequent events are to be tracked, or if they are needed to be detected immediately, stream processing will be a better option.  

To purchase streaming processing technology, one will need a solution with many components. The characteristics of data streams should considered while designing the architecture. To make it more useful, they require further pre processing, extraction and transforming. 

Streaming Data Architecture: its components and the design

There are certain key components for streaming data architecture. They are:  

Streaming data means data continuously generated at high velocity. To put it more clearly, a data stream is formed when certain events continuously generate data. An example of it is when a person clicks a link on a website, or mobile application or pressure is measured by an IoT device sensor. There are two forms for the streaming data, unstructured or semi-structured. Usually, there will be key-value pairs in JSON or XML. 

Streaming technologies have been gaining more popularity over the last years, although they are not new. Those who are eager to know how to handle data streaming should consider certain questions about what it is, the difference between stream processing and real-time one, the designing of it, its components, its benefits etc. 

We can help you with these as Streaming Data is a part of our Data Science Services. 

There are certain key components for streaming data architecture. They are:  

Message Brokers:

The duties of this component include taking data from a source, transforming it into a standard message format, and streaming it on an ongoing basis, and hence making it available for use. Open-source software such as Apache Kafka, PaaS, and components like Azure Event Hub, Azure IoT Hub, GCP Cloud Pub/Sub or GCP Confluent Cloud are some of the popular stream processing tools. 

Processing Tools:

When a message broker streams the output data, it needs to be transformed and structured and get analyzed. This results in certain actions, dynamic dashboards, alerts or more data streams. 

The most popular open-source framework that processes streamed data are Apache Storm, Apache Flink, and Apache Spark Streaming. Dataproc is the main platform in GCP for streamed data processing. 

Data Analytics Tools:

The data, ready for consumption after streaming, needs to be analyzed. Elasticsearch, Apache Cassandra are some of the most popular approaches to analyze streamed data. 

Streaming Data Storage:

Organizations store the streaming data as the cost of storage is low. A data lake, the most flexible and cheap method for the storage of data, is challenging to set up and maintain. It includes steps such as data processing, partitioning and backfilling with historical data that, in the end, it becomes a challenge to create the operational data lake. The majority of cloud ventures provide important components that serve as data lakes. 

A data warehouse is another method to store data which is of certain tools like Kafka, Bigquery, Databricks or Spark.  

Right component selection depends on the available stack. 

Conclusion

Upon designing certain solutions, one should consider the benefits of a modern streaming data architecture. It avoids the need for vast data engineering; it is high in performance and can be quickly deployed, and has built-in fault tolerance and high availability. It is cost-effective and supports multiple use cases. 

Companies choose either full-stack solutions or develop significant architecture blueprints, to make fast and easy delivery of solutions, which is essential to business needs.