Top 8 Streaming Analytics
DatabricksAmazon KinesisApache FlinkConfluentAzure Stream AnalyticsPubSub+ Event BrokerSpring Cloud Data FlowAmazon MSK
Easy to use and requires minimal coding and customizations.
The load distribution capabilities are good, and you can perform data processing tasks very quickly.
The scalability is pretty good.
The solution works well in rather sizable environments.
Allows us to process batch data, stream to real-time and build pipelines.
It is user-friendly and the reporting is good.
The most valuable feature of Confluent is the wide range of features provided. They're leading the market in this category.
It is also good for knowledge base management.
The integrations for this solution are easy to use and there is flexibility in integrating the tool with Azure Stream Analytics.
We find the query editor feature of this solution extremely valuable for our business.
As of now, the most valuable aspects are the topic-based subscription and the fanout exchange that we are using.
The way we can replicate information and send it to several subscribers is most valuable. It can be used for any kind of business where you've got multiple users who need information. Any company, such as LinkedIn, with a huge number of subscribers and any business, such as publishing, supermarket, airline, or shipping can use it.
There are a lot of options in Spring Cloud. It's flexible in terms of how we can use it. It's a full infrastructure.
The most valuable feature is real-time streaming.
The most valuable feature of Amazon MSK is the integration.
MSK has a private network that's an out-of-box feature.
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Streaming Analytics Articles
Streaming Analytics Topics
Why do we need streaming analytics?How do real-time analytics work?How do you analyze real-time data?Benefits of Real-Time AnalyticsWho uses real-time analytics?
Why do we need streaming analytics?
Streaming analytics gives companies the ability to analyze data as soon as it becomes available. With immediate real-time data at their fingertips, businesses can identify new revenue streams, resulting in an increase in profits. Other advantages include new customers, improved customer service, and also a way to analyze risks before they even occur. Because data is processed before it even lands in a database, streaming analytics supports much faster decision-making than using traditional data analytics technologies. Streaming analytics also guarantees security protection by detecting threat patterns and their risks, since it provides security monitoring of network and physical assets. By using real-time analytics, organizations can both predict and detect significant business events the moment they occur, making it possible to minimize risk while simultaneously maximizing gains.
How do real-time analytics work?
Using real-time analytics, data can either be pushed or pulled into the system. However, streaming must be in place to push that data into the system which can sometimes be impractical and can also require a lot of resources. While it is not a complicated process, real-time data analytics involves different key components.
- Aggregator - Combines real-time streaming data analytics from multiple different sources
- Broker - Data is immediately available for use
- Analytics Engine - Simultaneously analyzes data and blends data streams
- Stream processor - By obtaining and sending data streams, real-time app analytics and logic are executed
Other factors that make real-time analytics possible include:
- Processing In Memory (PIM) - Reduces latency by integrating the processor in a memory chip
- In-Database Analytics - Processing of data is performed within the database; analytic logic is also integrated into the database
- In-Memory Analytics - Querying data in RAM rather than querying data that is stored on physical disks
- Massively Parallel Programming (MPP) - Coordinated processing of different parts of a program by two or more processors
How do you analyze real-time data?
As big and fast data proliferates and more data streams are generated, companies are relying on real-time analytics more and more. Real-time data monitors any number of event streams and event data of any kind, allowing for a quick response to events at the very moment they happen - or even sooner when using predictive models.
Benefits of Real-Time Analytics
Real-time analytics makes data more organized, useful, and accessible from the instant it’s generated. Here is a look at some of the incredible benefits it provides:
- Tracking customer behavior: Real-time analytics exposes when and why customers behave as they do by revealing the latest time-sensitive customer data and allowing companies to craft an immediate response to optimize their satisfaction.
- Profitability: Streaming data technology creates new opportunities for companies, allowing for the predictability that helps cut costs, forecasts problems, and increases sales.
- Remain Competitive: Businesses can gain a competitive advantage by using streaming data to set benchmarks and discern trends. By doing this, they can outpace other competitors who are still using batch analysis. Moreso, real-time analytics can help businesses forecast big opportunities, encouraging them to become more innovative and strengthen their brand.
- Data visualization: A deeper insight into vital company information can help companies monitor Key Performance Indicators (KPIs). Because KPI data is viewed in real-time, it can react faster to risks and mitigate them. In addition to identifying errors, the data can reduce costs, and can also increase sales.
- Reduce losses: Streaming analytics can help companies prevent security breaches, or other risks like manufacturing issues, media crises’ or stock exchange meltdowns.
- Discover hidden patterns: Companies can identify missed opportunities by detecting routine business operations and other insights; Data streaming allows companies to analyze real-time data immediately, and thus enables them to upsell and cross-sell clients based on the information presented.
- Retain existing customers: Being able to track customer preferences, their likes, dislikes, and what they are buying or not buying helps companies rapidly respond to customer needs.
- Cybersecurity: The use of real-time data can automate detection of abnormal behavior that should be flagged and addressed right away. By alerting companies of suspicious activity, insider threats and malicious attacks can be reduced, or even fully thwarted.
- Testing: Split testing or A/B testing can help make big decisions easier, allowing businesses to forecast with confidence since data is immediately available.
- Quicker response time: Being aware of market fluctuations can open up major opportunities for companies, helping them spot big money-maker opportunities or conversely, could prevent them from situations that may cause huge losses.
- Cost-efficient: Real-time analytics can help companies save money by reducing the workload of IT departments, and providing cost-saving benefits related to employee engagement, as well as hiring and retention efforts.
Who uses real-time analytics?
For companies, real-time analytics can help businesses comprehend what’s happening across their business units, improve the relationship between sales and marketing, enhance workflows, understand customer behavior, and much more. Real-time data can be used for nearly every type of business and industry ranging from financial services, healthcare, information technology, advertising, and transportation.