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The Most Vital Aspects of Streaming Data Architecture



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The Most Vital Aspects of Streaming Data Architecture

If you are not familiar with the term “streaming data architecture,” it refers to an information technology framework. This particular kind focuses on processing data in motion.

It’s critical if you want to talk about ETL or extract transform load, processing. It treats it as just one more event in an ongoing stream.

Read on as we examine four of the more critical aspects of streaming data architecture.

The Message Broker

If you’re talking about streaming data architecture, the first thing you’ll probably discuss is the message broker, which some people call the stream processor. The message broker:

  • Is an element that takes data from a source, that people call a producer
  • Then translate that data into a standardized message format
  • Then continues streaming it

This matters because other components can then listen in and consume whatever messages the broker provides. There were first-generation message brokers that relied on something called MOM, or message-oriented middleware. Later, stream processors emerged.

Stream processors are more suitable for streaming paradigms. It shouldn’t surprise anyone that Amazon offers one of these services. They call theirs Amazon Kinesis Data Streams. There are plenty of other popular ones on the market.

If you would like to learn more about how to enhance your streaming experience, you can find plenty of helpful resources that explain stream processing in more detail by doing some research online on websites like Ververicas Landing Page for example.

Real-Time and Batch ETL Tools

Message broker data streams:

  • Need you to aggregate them
  • Need you to transform and structure them

It is only once you do those things that you can analyze the data with SQL-based analytics tools.

Say that you have an ETL tool or platform. People query it, and then it fetches events from message queues. It then applies the query to generate a result.

When it does so, it often performs some additional data aggregations or transformations. There might be many different results from this. You might have a new data stream, an alert, a visualization, or some other action.

Some open-source ETL tools include Spark Streaming and Apache Storm, but there are others.

Data Analytics

The next vital feature is the data analytics area, also called the serverless query engine. After your stream processor prepares the streaming data so that other parts of the system can consume it, you must analyze it. Otherwise, it provides no data.

You can stream data analytics in lots of different ways. Generally, you need to purchase one of the analytics tools that’s on the market if you want to get the most use from that data.

Cassandra is a popular one. It lets you do low-latency serving of streaming events to various apps. When you serve the streams to the apps, they can decide what to do with the data in real-time.

Another good one is Amazon Redshift. This is a type of data warehouse. You use it along with the Amazon Kinesis Streaming Data Firehose.

Using the two together, you save streaming data to Redshift. You can then apply analytic features in close to real-time. You get a dashboard with lots of different elements that might come in handy, depending on what you intend to do with the data.

Streaming Data Storage

The next logical part of any streaming data architecture setup is a reliable data storage method. Technologies exist today where you can store vast data amounts at a minimal cost.

You might wonder about why you’d want to store streaming event data. The answer is that sometimes, you may want to go back and analyze it if you find that a hacker tried to infiltrate your network. You can often identify how they did that if you have detailed streaming event data.

You can store your data in what’s called a data warehouse or database. The real positive when you do this is that you can do easy SQL-based data analysis. The one real drawback is that it’s hard to manage and scale. Also, if you use a cloud-based one, it can cost a lot.

You might also decide to use a “data lake,” like Amazon S3. It’s agile, and there’s no need to structure your data into tables when you do it. It doesn’t cost as much as a database.

However, real-time analysis is difficult with it. With this method, it’s challenging to perform SQL-based analysis.

Now, you know a bit about the four most fundamental elements of streaming data architecture. It pays to know somewhat about it since more companies these days like to keep a close eye on their streaming data.

If you want a streaming data solution, you might choose to look at some of the options we mentioned in this article.


Praneet is the CEO and Editor of the website He is a blogger and have varoius blog on various topic and he is from India who loves to read and write about Technology, Gadgets and Gaming. If you share the similar interests then you can follow him on Facebook | Google+ | Twitter

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