Real-time analytics has become the most crucial term in Big data analytics for enterprises. This enables enterprises to use all available data as real-time analytics big data. With real-time analytics, enterprises can generate analytics reports as and when the data is received. It ideally takes a minute. Furthermore, using real-time analytics, enterprises can receive fresh and contextual analytics reports. This gives close relevance to market trends. Real-time analysis happens through continuous querying. Streaming analytics or Real-time analytics enables applications to integrate with external data sources to application flow. Otherwise, it updates an external database with already processed information. This, in other terms, is known as stream processing.
In Real-time analytics, while the stream of data moves continuously, it calculates statistical analytics on the live streaming data. Thus, it allows the monitoring and management of live streaming data. So, the business can look at events happening at any given moment before the data loses its value.
So, the most critical elements about real-time analytics are:
It is helpful for the enterprises and industries that generate and deal with massive data daily or weekly.
– The architecture of real-time analytics is designed so that it fetches data the moment it is generated. Then the system utilizes big data analytics algorithms, which provides insight into the fresh data.
– In case of a large amount of data, the data batches are sent and mapped to different compute engines. After processing the data, the results are compiled for analysis.
– Using a analytics dashboard which is a software the data insights are delivered in real time.
Why is Real-time Analytics Important?
Real-time analytics allows organizations to analyze data as soon as it becomes available. Hence, it allows for analyzing risks before they occur. So, the business can easily find new opportunities, which may result in an increase in profits, improved customer service, and new customer ventures. A Streaming Analytics platform can process millions of events per second. “Because data in a Streaming Analytics environment is processed before it lands in a database, the technology supports much faster decision making than possible with traditional data analytics technologies,” Philip Howard of Bloor Research said in a recent Datamation interview. Since using real-time analytics, companies can detect different security threat patterns and risks. It helps in security protection and physical monitoring as well as network.
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Types of Real-Time Data Analytics
There are two types of real-time analytics:
On-demand real-time analytics — This is a reactive analysis approach where the user processes a request through query and delivers the result as analytics. For example, web site analytics is a kind of on-demand real-time analytics where an analyst monitors site traffic to resist the website's potential crash.
Continuous real-time analytics — This is a proactive analysis approach where users are continuously updated with alerts in real-time. For example, stock market tracking with various visualization representations is this type of analytics.
How does Real Time Analytics Work?
Real-time data is either pushed or pulled by real-time analytics tools. For data streaming, the real-time analytics tools must have the ability to push massive amounts of data, which is fast-moving. Streaming can happen from many resources, and in that scenario, data pulling needs to happen in intervals that may range from seconds to hours. Also, this data pulling must align with the business needs to not disrupt the operations.
The response times in case of real-time data streaming vary based on the product or business. For example, if it is a self driving car then the response time might be milliseconds, whereas, for a windmill application it could be minute. Similarly, for a credit scoring system it can take several minutes.
A real-time analytics system must meet the below requirements:
- Low Latency
- High availability
- Horizontal Scalability
Following components work in real-time analytics include:
Aggregator — From different data sources, real-time data analytics gets compiled into this. Spark is often used for this purpose.
Broker — This makes real-time data available for use.
Analytics Engine — Streaming data is analyzed here. It correlates data values and blends data streams.
Stream Processor — It sends and receives data streams from executing real-time app analytics.
To get the best result, you can deploy the real-time analytics at the edge, which means near to the data source. Here data analysis happens at the closest point of its arrival.
The Architecture Layer
There are four working layers in real-time analytics:
- decision layer
- integration layer
- analytics layer
- data layer.
Data Layer:
This is the foundation layer. Data, which means it is going to be analyzed. It involves DBMS, which can be Hbase, NoSQL, or Impala. Unstructured data can also be used at this layer, including tools like Apache Spark, Hive, or Apache Storm.
The analytics layer
This is a production environment for real-time analytics. In this layer, analytics models are developed. Hence, it is a development layer too.
Integration layer
Enterprise DevOps teams utilize this layer. They use the necessary APIs to hold the analytics engines, end-user dashboard, and data layers.
Decision layer
It is the layer where analyzed data is produced on a real-time basis. This is typically business intelligence software through which end-user can review the data analytics.
What’s so real about Real-Time Analytics?
Real-time means at the very moment. Hence, real-time analytics is capable of processing data at the moment it arrives in the system. So, there is no possibility of batch processing or future processing of data. Not to mention, it enhances the ability to make better decision making and performing meaningful action on a timely basis. So, real-time analytics combines and analyzes data at the right place and at the right time. Thus, it generates value from disparate data.
Advantages of Streaming Analytics
Data visualization on a real-time basis provides Deeper Insight:
To make a key performance daily, KPI, or key performance indicator, plays a vital role for companies. And Visualization is a key ingredient for KPIs. As the companies can view KPI data on a real-time basis, they can get a granular view of business data at any given point in time. This data can improve sales, identify errors, reduce costs, and provide information to react faster to risks to mitigate them. Real-time Analytics accelerates decision-making, along with providing access to business metrics and reporting.
Customer Behaviour insights:
As real-time analytics provide real-time insights on customer data like what they are buying, their preferences, likes, and dislikes, it gives companies to retain customers and generate extra profits. Additionally, companies can rapidly respond to customer needs, increasing revenues through cross-selling and up-selling of services and goods.
Remain Competitive:
Real-time analytics helps companies become more innovative and remain competitive by strengthening the band. With real-time visualization reports, businesses can identify trends and benchmarks, develop use cases, white papers, and generate forecasts of their company and industry. This not only reduces internal and external threats but also provides awareness of industry changes.
Provide customizable user experiences:
Utilizing real-time analytics you can provide a responsive and unique customer experience for your consumers and users. Depending on users' interaction with the web content, real-time analytics can allow you to recommend, up-sell and cross-sell.
Disadvantages of Streaming Analytics
Lack of Experts: Though streaming analytics is a happening field, there is a lack of availability of experts in the field. The main reason behind it is the small number of Data Scientists. Since real-time analytics is still a recent technology, and it shows a slow adoption by most developers due to their lack of expertise. “The streaming application programming model is unfamiliar to most application developers,” wrote Forrester analysts Mike Gualtieri and Rowan Curran in a Q3 2014 Forrester report on Big Data and Streaming Analytics.
Perform Risk Analysis: One of Streaming analytics's main features is it shows the analyzed result of the latest industry and media news. This helps companies to keep updated on the latest development amidst high competition. Along with that, since real-time analytics data on vendors and customers are now in hand, it helps to take action against specific risks or events.
Securing Data by threat analysis: WithStreaming Analytics, companies can now identify internal and external threats that may affect the company or industry. Identifying sensitive information that is not protected is at the fingertips now with streaming data analytics. Whether it is federal, state, or regulatory information, protecting them is easy with streaming data analytics.
Conclusion
This is a real-time society, and real-time analytics is a powerful tool to tap into the power of data. Today data is considered not as valuable but also as a commodity. Nowadays, the need of the companies is to expect immediate access to the information they are seeking. While experimented with applications, this information brings new insights that allow them to make decisions on the next action items with the data.