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Streaming Analytics: An Introduction
Also referred to as real-time analytics or data stream analytics, streaming analytics captures, processes, and analyzes data in real-time, as it is generated, for the purposes of extracting immediate business insights.
Confluent’s streaming data platform enables real-time processing and integration, allowing analytics platforms to maximize efficiency, reduce costs, and uncover powerful insights across both historical and real-time data.
What Is Streaming Analytics?
Streaming analytics is an approach to business analytics and business intelligence where data is captured, processed, and analyzed in real-time, or near real-time, as it is generated. By enabling immediate business-level insights, it enables timely and proactive decisions and activates new use cases and scenarios.
This is in contrast to regular (or traditional or batch) analytics, where data is typically considered for static analysis only after it’s “at rest,” typically in a data warehouse, long after the business event that created it.
Instead, streaming analytics strives to enable analysis of data when it’s still “in motion”, at the time of its creation or update. This means that dynamic trends, patterns, and anomalies can be detected on a more dynamic or real-time basis, driving new kinds of important decisions, automation, efficiencies, and real-time use cases.
For instance, financial institutions can detect and react to fraudulent transactions as they’re happening, and take immediate action (such as blocking a credit card exploit before it completes). A retail chain can watch changes in inventory in real-time and trigger supply chain operations to compensate, balancing just-in-time parameters such as expected demand, inventory, supply chain, and transport, or to generate unique up-sell offers to its customers. Or an airline’s operations division can analyze the real-time data stream from its fleet of aircraft to predict potential faults (anomaly detection), trigger maintenance or regulatory events, and proactively schedule and reposition equipment and crews in response.
How It Works
With streaming analytics, large volumes of data are continuously processed in real time. To facilitate meaningful business-level analysis, data infrastructure such as a data stream processing platform is used, which allows the ingestion and analysis of data from multiple sources in real time (such as financial transactions, IoT sensors, social media feeds, logs, clickstreams, etc).
The analysis functions may range from simpler comparisons, correlations, and joins, to more sophisticated techniques such as complex event processing (CEP) and machine learning. These functions, which effectively generate new data “products” of value, may be implemented in application code or using standard SQL with stream processing extensions.
The stream processing platform enables this analysis to drive real-time decisions or visualizations, to be routed to traditional data warehouses for further or legacy business intelligence, or to other operational data sources to drive other functions in the organization.
In this way, a stream processing platform can be seen as a centralized way to connect an organization’s data sources and sinks, with real-time value-added computation and analysis along the way.
How Stream Analytics Differs from Batch Analytics
Streaming analytics and batch (regular) analytics represent two related approaches to data analytics, differing in when data is available for analysis. With batch analytics, data is typically considered for what is effectively static analysis only after it’s “at rest,” typically in a data warehouse, long after the business event that created it. streaming analytics, on the other hand, enables analysis of data when it’s still “in motion,” at the time of its creation or update.
In this regard, streaming analytics represents the evolution of analytics, from batch to streaming. An organization can introduce a stream processing platform to connect data sources and sinks, thus adding new capabilities, without disturbing existing/legacy batch analytics.
| Feature | Streaming Analytics | Regular Analytics |
|---|---|---|
| When data is analyzed | As it is being generated | After it has been stored in a database |
| Typical use cases | Real-time applications | Non-real-time applications |
| Benefits | Ability to react to events in real-time | Ability to analyze large amounts of data |
| Challenges | Complex to implement | Can be slow for real-time applications |
| Reaction time | Real-time/immediate | Delayed |
| Decision-making | Forward-looking contemporaneous, and retrospective | Retrospective only |
| Analysis and decision latency | Low | Medium to high |
| Intelligence/Analytics paradigm | Both push-based, continuous intelligence systems or pull-based, on-demand analytics systems | Pull-based, on-demand analytics only |
| Storage cost | Low | High |
| Data processing | Real-time | Request-based/periodic |
| Dashboard refresh | Every second or minute | Hourly or weekly |
| Ideal for | Decision automation, process automation | Non-time sensitive use cases like payroll management, weekly/monthly billing, or low-frequency reports based on historical data |
Some industries where streaming analytics is used:
- Finance: streaming analytics is used to monitor financial markets for signs of fraud or other suspicious activity.
- Retail: streaming analytics is used to track customer behavior and optimize inventory levels.
- Manufacturing: streaming analytics is used to monitor production lines and identify potential problems before they cause a disruption.
- Logistics: streaming analytics is used to track the movement of goods and ensure that they arrive on time.
- Healthcare: streaming analytics is used to monitor patients' health and identify potential problems early on.
Use Cases
Here are the top use cases for real-time analytics:
Fraud detection
Real-time analytics can be used to detect fraud in real-time, such as credit card fraud or insurance fraud. →
Customer service
Real-time analytics can be used to improve customer service by providing customer support agents with the information they need to resolve issues quickly and efficiently. →
Marketing
Real-time analytics can be used to personalize marketing campaigns and target customers with the most relevant offers. →
Supply chain management
Real-time analytics can be used to optimize supply chain management by tracking the movement of goods and ensuring that they arrive on time. →
Manufacturing
Real-time analytics can be used to improve manufacturing processes by identifying potential problems early on and taking corrective action. →
Financial services
Real-time analytics can be used to monitor financial markets for signs of fraud or other suspicious activity. →
Healthcare
Real-time analytics can be used to monitor patients' health and identify potential problems early on. →
Media and entertainment
Real-time analytics can be used to personalize content and recommendations for users. →
Internet of Things (IoT)
Real-time analytics can be used to collect and analyze data from IoT devices to gain insights into how people are using products and services. →
Self-driving cars
Real-time analytics is essential for self-driving cars to make decisions in real time about how to navigate the road safely. →
Advantages of Streaming Analytics
Real-time insights, decision-making, and prediction
Streaming analytics allows organizations to gain insights into data as it is being generated, which can help them make faster and better decisions, and anticipate future outcomes. For example, a streaming analytics solution could be used to track customer behavior in real-time and identify potential fraud or security threats.
Improved efficiency
Streaming analytics can help organizations automate tasks and processes, which can save time and money. For example, a streaming analytics solution could be used to automate the process of generating reports or sending alerts.
Reduced risk
Streaming analytics can help organizations identify and respond to potential risks more quickly, which can help them avoid costly disruptions. For example, a streaming analytics solution could be used to monitor the performance of critical infrastructure and identify potential problems before they cause an outage.
Enhanced customer experience
Streaming analytics can be used to personalize the customer experience by providing real-time insights into customer behavior. For example, a streaming analytics solution could be used to recommend products or services to customers based on their past purchases.
Increased innovation
Streaming analytics can help organizations innovate faster by providing them with access to real-time data that can be used to develop new products and services. For example, a streaming analytics solution could be used to track customer sentiment in real-time and identify new opportunities for product development.
Challenges
Here are the top 5 challenges of streaming analytics:
- Data volume: The amount of data that businesses generate is growing exponentially, and this makes it difficult to analyze all of the data in real-time.
- Data velocity: Real-time analytics requires businesses to analyze data as it is being generated, which can be difficult to do if the data is coming in at a high velocity.
- Data variety: Businesses generate data from a variety of sources, and this can make it difficult to integrate all of the data and analyze it in real-time.
- Data quality: Real-time analytics requires businesses to have high-quality data, and this can be difficult to achieve, especially if the data is coming from a variety of sources.
- Cost: Real-time analytics can be expensive to implement and maintain, and this can be a barrier for some businesses.
Despite these challenges, streaming analytics can be a valuable tool for businesses that need to make decisions in real-time. By overcoming these challenges, businesses can gain a competitive advantage by making better decisions faster.
Common Technologies and Solutions
The most common technologies used for streaming analytics:
- Apache Kafka: Apache Kafka is an open-source stream processing platform that can be used to collect, store, and process real-time data.
- Apache Flink: Apache Flink is an open-source processing platform that can unify real-time data streams and batch processing
- Apache Spark: Apache Spark is an open-source cluster computing framework that can be used to process large amounts of data in real-time.
- Apache Druid: open-source real-time analytics database
- Apache Beam: open source framework for defining batch and streaming data-parallel processing pipelines, with execution on a supported distributed processing back-ends (such as Apache Flink, Apache Spark, and Google Cloud Dataflow).
- Imply: data platform built on Apache Druid
- Apache Pinot: An open-source distributed columnar storage engine that provides low-latency querying capabilities for real-time analytics with high scalability and fault tolerance
- Confluent Platform: managed streaming platform built on Apache Kafka and Apache Flink that provides additional features, management tools, and integrations to support large-scale streaming data pipelines and stream processing.
- Google Cloud Dataflow: managed Apache Beam service
- Amazon Kinesis: managed service for real-time streaming data
- Microsoft Azure Stream Analytics: managed service for real-time streaming data processing
- StarTree: support for analysis & visualization of large-scale, time-series data
- Rockset: indexing and query service supporting real-time analytics on structured and semi-structured data
Why Streaming Analytics with Confluent
Confluent offers Apache Kafka, a full-featured streaming data pipeline platform, with on-premise and fully-managed cloud service options. As a managed service, Confluent extends Kafka with additional features, such as a centralized control plane for managing and monitoring Kafka clusters and connectors and integrations to connect Kafka with other applications. These features enable businesses to access, store, and manage data more easily as continuous, real-time streams.
To facilitate data connectivity within an organization, Confluent offers a wide range of data connectors that seamlessly ingest or export data between Kafka and other data sources or sinks. These include Kafka Connect (an open-source framework for building and running Kafka connectors), Confluent Connectors (Confluent-supported connectors for JDBC, Elasticsearch, Amazon S3 Connector, HDFS, Salesforce, MQTT, and other popular data sources), Community Connectors (contributed and maintained by the community members), and Custom Connectors (built by an organization’s own developers).
Confluent also offers a range of features to protect and audit sensitive data and prevent unauthorized access.
Building on Kafka’s capabilities, Confluent also offers a range of fully-managed options for real-time analysis of data streams “in motion”, as data is created or updated:
- Kafka Streams: a lightweight Java library that is tightly integrated with Apache Kafka. With it, developers can build real-time applications and microservices by processing data directly from Kafka topics and producing results back to Kafka. Because it's part of Kafka, Kafka Streams leverages the benefits of Kafka natively.
- ksqlDB: built on top of Apache Kafka, ksqlDB provides a higher-level abstraction for processing streaming data using SQL queries, simplifying the development of stream processing applications.
- Apache Flink: open-source stream processing framework that supports event time processing, stateful computations, fault tolerance, and batch processing. Flink provides a distributed processing engine that can handle large-scale data streams and offers advanced features like event-time processing, exactly-once semantics, and support for various data sources and sinks, along with support for fault tolerance and exactly once delivery.
These options — Kafka Streams, ksqlDB, and Flink — enable a wide range of processing and analytics requirements, scalability needs, and real-time analytics architectures; an organization can build on a single platform as its needs change or their complexity increases. For example, for more complex scenarios, Kafka and Flink are often used together when analytics processing generates large intermediate data sets or require a full range of SQL capabilities.