Streaming data is generated continuously by multiple different sources like the sensors or the server logs. It’s also called an event stream. The streaming data records are often small. Most of them are a few kilobytes, and that’s why the stream goes on without having to stop in most cases.
The continuous data is then fed into the stream processing software to derive valuable insights. Then, the changes in states of the data are analyzed for the appropriate actions to be taken.
Streaming data architecture
- 1 Streaming data architecture
- 2 The benefits of the streaming data architecture
- 3 Components of data streaming architecture
- 4 Streaming data storage
- 5 General characteristic of data streams
Streaming data architecture refers to the framework of the software components built to ingest and process large amounts of streaming data. The traditional data solutions methods focus more on the writing processes of the data and reading the data in batches.
With the advanced streaming data architecture, the data is consumed immediately as it’s generated. It’s then stored for further analysis through real-time processing, data manipulation and analytical tools.
Every data streaming architecture must account for the unique characteristics of the data streams, even with the massive volumes of the data being delivered. Stream processing is a complex process that can rarely be solved with a single database or the ETL tool. That’s why the streaming data architecture consists of multiple building blocks.
Sometimes the building blocks are combined and replaced with the declarative data pipelines built within the system.
The benefits of the streaming data architecture
Deals with never-ending streams
Advanced data streaming architecture can deal with never-ending streams of generated events. With the traditional batch processing approach, the tools require frequent stopping of the stream events, capturing the batches and then combining them with the multiple different streams available at the moment. Even though combining data with the current streaming data architecture is challenging, you can easily derive instant insights from the large volumes of the data being delivered.
The modern streaming data architecture can real-time handle processing of the large volumes of data delivered in the form of kilobytes. That’s why most companies adopt stream processing with near-real-time processing. These data analytics are designed with high-performance database systems that allow the data to lend itself to the stream processing model.
Current streaming data architecture can detect patterns of the time-series data which is impossible with the traditional approach. That’s because the process used to break data into batches-some of the events are split across two or more batches. These patterns include the trends of the website traffic, which are continuously processed and analyzed.
The modern streaming data architecture offers easy data scalability that can deal with gigabytes of data per second with just a single stream processor. Without the enhanced data scalability, the growing volumes of the data can break the batch processing system as it will require the provision of more resources for it to function effectively. This solves the challenges that arise as a result of infrastructure failures.
Components of data streaming architecture
Four major components of the data streaming architecture are a must for every system, regardless of whether it’s modern or traditional. These components include;
The message broker
This component is also known as the stream processor. It takes data from the source producer and then translates it into a standard format before it’s streamed on an ongoing basis. After that, the other components listen and consume the data messages that the broker passes.
Most companies are using hyper-format messaging platforms that support a streaming paradigm. They support high performance with great persistence and a higher capability to handle more message traffic. These platforms are designed to focus more on streaming with little support for the task scheduling processes.
Real-time ETL tools
The shared data streams across different messaging platforms must be aggregated, structured, and transformed before being analyzed with other analytics tools. This is possible with a platform that receives queries from the users, then fetches the corresponding events from the message queues and applies the queries to get the desired results.
Different frameworks work in different ways, with all of them having the capability to listen to the message streams, process the received data and then save it to the storage. The stream processors provide the appropriate syntax for the querying process and data manipulations. Different business cloud services that support different analytics make the data more organized and beneficial for the ingestion process.
Serverless query engine
After the stream processors have prepared the data for consumption, it’s then analyzed for different uses. There are several approaches to streaming data analytics depending on the kind of tool used.
Elasticsearch is one of the best tools that automatically uses text search to map the corresponding data types. This tool allows the user to perform a quick text search within a short time.
Streaming data storage
Several options can be used for storing streaming data. A data warehouse is one of the best options that can be used for the real-time storage of streaming data. In these cases, operational data stores are the areas where the data is overwritten and frequently changed.
It provides a snapshot of the latest data from different transactional systems. A typical data warehouse contains the static data for the archive processes, storage, historical analysis and reporting data. With the data coming from multiple sources, there is a need to clean it, resolve all the redundancy elements, and check the data against the business rules for integrity purposes.
General characteristic of data streams
The stream data from different sources like sensors and web browsers have specific characteristics that make them extraordinary. One of the common characteristics is that they are time-sensitive. Each element carried in the data stream has a timestamp, and that’s why most of them lose their significance after a certain period.
Data streams are also continuous in that they don’t have a beginning or an end. They are continuous and happen in real-time. Due to the disparity in the sources of their origins, the stream of data might be a mix of different formats. That’s why they must undergo processing. As a result of the different data transmission mechanisms, a data stream might have damaged elements that make them imperfect sometimes.