What is Streaming Analytics? | The Complete Guide for 2022

on Apache Flink, Apache Hive, Apache Kafka, Apache Spark, AWS, Data Integration, Data Streaming, Event Stream Processing, Event Streams • April 29th, 2022 • Write for Hevo

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Have you ever wondered how Netflix, Spotify, and Prime have made it possible for the world to consume so much content without actually uploading the whole file? The answer lies within the Data Streaming technology of a company. Streaming services are capable of bringing content or data right into your device in a split second. Data Streaming refers to the continuous flow of data as is generated from a source and delivered to different devices via the internet and accessed in real-time. This article will help you understand the importance of Streaming Analytics in this data-driven world.

There are more and more people online than ever before. In today’s data-driven world, a massive amount of data is generated from IoT devices, social networks, applications, online transactions, sensors, and more. Streaming Analytics is nothing but the processing and analysis of this fast-moving real-time data from a variety of sources to extract immediate insights. Let’s get started and delve a little deeper into Streaming Analytics.

Table of Contents

What is Streaming Analytics?

Streaming Analytics | Hevo Data
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Streaming Analytics is the process and analysis of data in real-time to generate automated, real-time alerts or actions. Any organization wanting to extract real-time and actionable insights from fast and ever-growing volumes of data must leverage Streaming Analytics. With Streaming Analytics, it becomes easy to query continuous data patterns, perform simple calculations, and predict future trends. This type of analytics focuses mainly on data flows without complex analytical tasks. Its primary function is to provide users with up-to-date information.

Streaming Analytics is also known as Event Stream Processing and it refers to the analysis of high volumes of real-time “in-motion” data via continuous queries, called Event Streams. Streaming Analytics allows organizations to analyze and extract real-time information from multiple sources as the number of data streams expands. In simple words, it allows you to analyze continuous data patterns while also responding to key events within a short span of time.

How does Streaming Analytics Work?

Streaming Analytics deals with huge pools of moving data with the help of Event Streams. Event Streams are triggered by specific events that occur as a direct result of an action or series of actions. These actions can be anything from a financial transaction to a website click.

As discussed already, data is generated from the Internet of Things (IoT), web interactions, transactions, Cloud applications, and machine sensors. Streaming Analytics platforms help in extracting business value from vague sets of fast-moving data. This helps organizations in detecting risks and opportunities.

Streaming Analytics Process: Hevo Data
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Advantages of Streaming Analytics?

  • Data Visualization: Keeping a check on the company’s most important information regularly is the key to maintaining good Key Performance Indicators (KPIs). And what better way is to monitor the data than Data Visualization. Streaming Analytics allows users to visualize and monitor the data in real-time.
  • Business Insights: This is something that’s not hidden from anyone now, users always have access to actionable and valuable business insights with Streaming Analytics. Abnormal activity can be detected easily and flagged for investigation. In fact, this can also be used in cybersecurity to detect and respond to threats.
  • Increased Competitiveness: Today, Businesses look to have a competitive edge, and Streaming Analytics is one of the best ways to discern trends and set benchmarks faster. This helps businesses outperform their competitors who’re still stuck with Batch Analysis.
  • Finding Missed Opportunities: They say, data can unlock the secrets of any individual, their likes, dislikes, behavior, etc. Well, it’s true. Streaming Analytics can help companies discover hidden trends, patterns, correlations, and other valuable insights. Big Data has answers to all your questions. Such valuable information can then be used to upsell, and cross-sell clients.
  • Create New Opportunities: Streaming Analytics makes it easy to predict future trends, which can be used to cut costs, solves problems, and grows sales. Streaming Analytics has been growing tremendously and it has led to the invention of new revenue streams, business models, and product innovations.

Why Use Streaming Analytics?

It’s a healthy practice to detect problems at an early stage before they become a mess. Streaming Analytics helps you detect early signs of failure by analyzing raw information. It enables you to predict and detect significant business events at the exact moment they occur allowing you to minimize risk and maximize gain. This is just one of the use-cases of Streaming Analytics. You can do the following with Streaming Analytics.

  • You can easily design and implement sophisticated analytics capable of monitoring multiple event streams on the go.
  • Detecting and analyzing trends and patterns from multiple sources at the same time has never been so easy.
  • Streaming Analytics lets you respond to events within a span of a few seconds. You can further detect anomalies in advance and respond to events even before they occur with predictive models. This way you can easily prevent pending equipment failures.
  • You can automate responses to take smarter and more intelligent decisions instantly without any human intervention.

 Let’s just take a look at the different use-cases of Streaming Analytics.

  • Healthcare: Streaming Analytics can be used for real-time monitoring of health conditions and clinical risk assessment. It can further generate alerts in advance.
  • Finance: It aids in transaction processing and helps you monitor the state of the market/currency.
  • Retail/customer service: This is the most common use case of Streaming Analytics or just Data Analytics in general. Analytics can help you understand customer behavior and adjust your marketing strategy accordingly.
  • Home security: IoT Data Stream Analysis can help you with smart protection and alert systems improvement.
  • IT: Streaming Analytics can be used to detect any kind of fraud and also help in system maintenance with real-time data analysis.

Tools for Analytics Streaming

Let’s look at the major technologies working toward making Stream Processors capable of fast computation and concurrent processing of multiple streams.

Apache Tools: Kafka, Spark, Storm, and Flink

Streaming Analytics: Apache | Hevo Data
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Apache Kafka is a distributed event store and Stream Processing platform widely used by companies all over the world to create and manage seamless streaming Kafka Data Pipelines, Data Integration, and Analytics. Originally developed by LinkedIn as a messaging queue application, Kafka was open-sourced and donated to Apache in 2011 as a Data Streaming platform.

Kafka is a Stream Processor which integrates applications and data streams via an API. It is fully capable of running concurrent processing and swiftly moving high volumes of data.

You can further integrate Kafka with other Apache tools such as Hive, a Data Warehousing solution, and Hadoop for Batch Processing of the stored data. It can also be integrated with Apache Spark, a Big Data processing engine. 

Apart from Kafka, Apache also has other Stream Processing tools such as Storm and Flink for distributed Stream Processing. Storm and Flink can also be used as ETL tools or batch processors integrated into Hadoop.

Amazon Tools: Kinesis Streams, Kinesis, and Firehose

Streaming Analytics: AWS | Hevo Data
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Amazon Kinesis Streams is a scalable and highly customizable data stream processing solution. Kinesis Streams comprises a Stream Processor and allows you to create your own applications using client libraries, connectors, and APIs.

Kinesis is a managed stream processing solution from Amazon. Apart from automated processing and fine-tuning, Amazon Kinesis supports integration with various Apache services such as Spark and Kafka.

You can also utilize Amazon Firehose to link data streams into existing BI tools and analytical interfaces, as well as into a Data Warehouse. It may also assist you in retrieving data and integrating it into Amazon’s existing Warehousing solutions like S3 and Redshift Cloud Warehouse.

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Limitations of Streaming Analytics

By now you’re probably familiar with the importance and benefits of Streaming Analytics. However, it comes with certain drawbacks. It has always been a challenge to store real-time data in the cloud. Hence, it’s important for businesses to come up with new strategies to archive data carefully and securely.

As data is coming from various sources and moving through a distributed system, data streams come across the challenge of arranging their data in a sorted way and delivering it to the customers. Data Streams are regularly facing the issue of CAP, which stands for Consistency, Availability, and Partition Tolerance. Businesses also have to deal with real-time data processing issues, late-arriving information, and system disruptions.

Conclusion

People are generating tons of data every day. And, speed is certainly a word that characterizes the flow of data in today’s world. Hence, every organization is reliant on data and Data Analytics technologies. Streaming Analytics solves the same purpose of processing and analyzing this fast-moving real-time data from a variety of sources to extract immediate insights.

This blog introduced you to Streaming Data Analytics and took you through various aspects of it. However, it’s easy to become lost in a blend of data from multiple sources. Imagine trying to make heads or tails of such data. This is where Hevo comes in.

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Share your experience of understanding Streaming Data Analytics in the comments section below.

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