Data Streaming Explained: Pros, Cons & How It Works

Data streaming refers to the process by which data constantly flows from one source to to be processed and analysed in near-real-time.

How does data streaming work?

Businesses can have many data sources which are sent to various locations. The data is processed by stream processing methods and typically consists of smaller fragments.

Streaming data allows bits of information to be processed real-time or in near-real-time. The two most commonly used scenarios for streaming data are:

  • Video streaming, in particular
  • Real-time analytics

Data streaming was once reserved for only a few businesses like media streaming or trading on stock exchanges for financial value. Nowadays, it’s becoming a standard across all companies. Data streams allow organizations to analyze data in real time which allows companies to track every aspect of their business.

The fact that it is real-time monitoring allows managers to respond and respond to events of crisis more quickly than other methods of data processing. Data streams provide a continuous communications channel between all moving parts of a business and those who take decisions.

Media that stream

Media streaming is an illustration. It lets a user start watching a video without downloading the entire video before watching.

This allows users to start watching the information (video) sooner and in the case of streaming media, stops the device of the user from having to keep large files at the same time. Data can be transferred to and from the device while the data is processed, and viewed.

Real-time analytics

Data streams let companies make use of real-time analytics in order to monitor their operations. The data streams generated can be processed using techniques for data analytics using time-series to show what’s taking place.

The Internet of Things (IoT) has fueled the explosion in the range and quantity of the data streams that are available. The increasing speed of networks contributes to the speed at which data is transmitted.

We receive the widely-accepted three V’s in the data analysis as well as data stream:

  1. Variety
  2. Volume
  3. Velocity

When paired with IoT A company will be able to collect data from different monitors and sensors which allows it to manage a variety of dynamic variables real-time.

From an perspective of chaos engineering viewpoint, real-time analysis is beneficial because it enhances the ability of a company to track the activities of the business. In the event that equipment failed or the readings deliver information that needed urgent action, the company is equipped to take action.

Data streams directly improve the resilience of a business.

Data Architecture for streaming data

Data streams require a particular type of structure, and it is simpler to implement in the event that you are already familiar with cloud-based architectures. The majority of learning curve is completed, the rest is simply adding bits of information in a few places.

Cloud service companies provide the ability to build an information stream. This is the standard Amazon, Azure, Google.

You can also create an own stream of data. It starts by to build an application that can process streams, something that can capture the stream data of the device or application.

To create an efficient stream processor, you could employ tools like:

  • MSK of Amazon
  • Amazon Kinesis
  • Apache Kafka
  • Google Pub/Sub

The next step is to incorporate a method to study or analyze the streaming data employing tools like:

  • Amazon Kinesis Data Analytics
  • Google BigQuery, Dataflow

Thirdly, the analysis has to be output through an app or a dashboard so that users will be alerted and react to the data.

In the end, the data streamed has to be saved somewhere. The price of storage is affordable, so the general rule is to store all data. When it comes to storage for data the general belief is to say that “If it’s not useful now, maybe it will be later.”

For the storage of streaming data these are the best options:

  • Amazon Redshift
  • Kafka
  • Amazon S3
  • Google Storage

(Learn more about how to work with data in Redshift as well as S3. )

Problems with streaming data

Data streams provide constant streams of data that can be analyzed to find information.

The data generally should have a proper order. That is often the purpose of having streams. (After all the messaging application has to have all messages properly arranged.)

Since data could be derived from multiple sources, or even from the same source, however, as it is transferred through the system as a distributed one which means it faces the problem of arranging its data and the delivery to its user.

Data streams are directly confronted with the theorem of CAP issue in the process of building. If deciding to use the database or streaming solution the data architect has to establish the relationship between:

  • With consistent data in which all reads are the most recent written and, if not, give an error.
  • Highly accessible data and all reads contain the data however they might not be the most current.

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