Sometimes, when companies start to deal with really huge amounts of data, they don’t really know how to handle it. Data streaming is the way – if you need a significant amount of data to be processed in no time to gain insights or to perform some operations, you should find a good data stream solution.
What do you think when you hear “data”? Information collected by companies can be used for analysis to create useful insights and for reporting – all of this is done in order to improve business performance and increase profits. Data streams though allow you to gain business insights in almost real time and to perform some actions that are essential for day-to-day work in the company.
What is data streaming?
Streaming data means a continuous flow of data from various sources – data streams can be processed, stored and analyzed using special technologies as it is generated in real-time. The goal of this streaming is to ensure a constant flow of data to be processed without needing to download it from the source first. Data streams can be generated from various types of sources, using data in various formats and volumes, different applications, devices and transactions.
Examples of data streaming
So what kind of companies require data streaming and why?
- Financial institutions – they have to stay up to date with any changes in the stock market in real time in order to evaluate risk, but data streaming is also important for any company which has to process transactional information.
- Social media – data streaming allows real-time monitoring of posts on social media platforms in order to find “fake news” or “hate speech”. Those kinds of platforms use tools that are able to process huge amounts of data and take action upon chosen posts.
- Manufacturing – almost all modern machines send data to streaming applications for better performance control, to detect potential errors, to eliminate defects in products and increase efficiency.
- E-commerce and retail – when customers visit your website, their activity is tracked. You can learn what they’ve been looking for, what they have bought and what data they have left on the website. All this data about their choices and preferences can be used in real time for creating recommendations.
- Logistics and transportation – thanks to data streaming you can quickly receive information about your trucks and cars in transit. You’ll receive an alert if they’ll be behind schedule. You can also learn if they will arrive earlier than planned.
- Internet Of Things – IoT devices require access to data all the time. It has to keep flowing otherwise they wouldn’t be able to function. But there’s more to it – any information shortage could cause a disaster.
- Game industry – gaming platforms process huge amounts of data every day, every moment. They require reliable data stream processing and real-time monitoring to ensure high quality game play. Successful data streaming is crucial here.
When is real-time streaming necessary?
All businesses that need their data to be analyzed in real time should consider using data streaming technology. It is a fact that in many cases the value of a previously performed analysis decreases after a while – for example when your systems have limited time for recommending a product to a client that is visiting your shopping platform at the moment.
Data streaming is useful when your company requires real-time calculations of costs, evaluating risk or analyzing changes on the market. Real-time data analytics is very important if you have to know what is happening right now. If constantly monitoring some processes or performance is crucial, you also need to work with data streams.
Why is streaming data difficult?
It is hard to even imagine the quantity of data being collected by your systems and applications every day. Actually sensors, IoT devices, social networks, and online transactions all generate data that needs to be monitored constantly and acted upon quickly. Bear in mind that those source devices are often produced by different manufacturers, so they can deliver data in a variety of formats. Sounds complicated already, right? As businesses need more and more data to produce useful insights and make good decisions, streaming data solutions have to be highly scalable.
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Real-time Streaming Platforms for Big Data – examples
The thing is that for many businesses, real-time data analytics is not a must-have feature, although it can quickly provide many useful insights, but there are many companies whose success depends on data streaming. Here is a list of popular tools for big data streaming.
Azure Stream Analytics
Kinesis processes streaming data in the cloud – just like Azure solution. Obviously, it is integrated with other Amazon services for building a complete Big Data architecture and goes with the KCL tool for developing streaming applications. This additional tool enables developers to use data streams for dashboard alerts. It is scalable and highly flexible – it allows companies to benefit from basic reporting and analytics but also enables them to use machine learning algorithms to improve their analysis.
Google Cloud DataFlow
Google uses Python to support data streaming – this is not at all surprising as Python is quickly gaining popularity, and it is now being used by many developers and data scientists all around the world. Google Cloud DataFlow filters and rejects inaccurate data in order to prevent the analytics from slowing down. This tool can be used with others (such as Apache Beam) to define data pipelines to process data from multiple sources.
Apache streaming projects
As demand for powerful data streaming tools grows, Apache moved on from its traditional framework for big data processing – Hadoop – and came up with data streaming projects. There are many Apache open-source streaming platforms:
- Apache Flink – it can process pipelines in almost real-time with high fault tolerance. Flink enables batch and stream processing. This solution is often compared to Apache Spark, although there are some differences in the implementation between those two. Flink uses data from distributed storage systems like HDFs as it doesn’t have its own data storage system. It is scalable, and it supports programs written in Java and Scala.
- Apache Spark – this previously mentioned tool has become really popular. Spark can run on its own or on top of Hadoop YARN (one of the main components of Hadoop). Even though it was written in Scala, it can support multiple programming languages including SQL, Python or R. It is capable of in-memory processing, which makes it highly effective. Developers use Spark streaming to build fault tolerant streaming applications. There are few data streaming tools so appreciated by developers and data scientists as Apache Spark. Structured Streaming is the main model for handling streaming datasets. In Structured Streaming, a data stream is treated as a table that is being continuously appended. This leads to a stream processing model that is very similar to a batch processing model. You express your streaming computation as a standard batch-like query as on a static table, but Spark runs it as an incremental query on the unbounded input table.
- Apache Storm – this runs on top of Hadoop YARN. As the matter of fact, it is often compared to Hadoop, with one difference – it deals with real-time data processing the way that Hadoop deals with batch processing. The nice thing about it is that it can be used with any programming language. Similar to other Apache solutions for data streaming, it ensures scalability and fault-tolerance. It is often used in combination with other Apache tools such as Kafka or Spark.
Those are only 3 of the well-known Apache solutions for data streaming – there are more and they all have their own specific qualities. Many businesses have to ingest large amounts of data and process it in real-time. How can you choose the best data streaming solution for your business? Comparing those above mentioned platforms is not easy. Contact us! We’ll be more than happy to learn your company’s needs and advise you.
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