Real-time analytics. Clickhouse and Kafka example

Imagine a world where businesses can make intelligent decisions instantly, based on real-time data analysis. The power of real-time analytics can revolutionize the way organizations operate, react to market changes, and engage with customers. With the ability to process and analyze data as it is generated, the potential impact on businesses is immense.

What is Real-Time Analytics?

Real-time analytics refers to the process of collecting, processing, and analyzing data in near-instantaneous time, typically within seconds or milliseconds of its generation. This approach enables organizations to make immediate and data-driven decisions based on up-to-the-minute information. Real-time analytics is crucial in various industries, including finance, e-commerce, healthcare, and manufacturing.

Key components of real-time analytics include data streaming, where data is ingested continuously as it is generated, and real-time processing, which involves analyzing and deriving insights from the data in real-time. Technologies like stream processing engines and in-memory databases play a vital role in supporting the speed and responsiveness required for real-time analytics.

Exploring the Basics of Real-Time Analytics

Real-time analytics is the practice of preparing and analyzing data as soon as it is input into a database, allowing users to gain insights or draw conclusions rapidly after the data is entered into their system. This approach is a significant departure from historical data analytics, which relies on past occurrences and surveys for decision-making. Real-time analytics allows businesses to process data instantly, leading to immediate decisions that offer a competitive advantage. This is achieved through the utilization of specific hardware and software systems that expedite data processing and analysis.

The main components of real-time analytics software are a system which takes in and sends out data streams along with executing the application and implementing real-time analytics. This system is generally known as the stream processor. Industries like security software and logistics are already employing real-time analytics to detect vulnerabilities, track shipments, and optimize decision-making. This highlights the superior capabilities of advanced analytics compared to traditional batch analytics, which processes data in groups after its collection.

How Does Real-Time Analytics Work?

Real-time analytics involves processing and analyzing data immediately as it is generated, providing timely insights for decision-making. It typically follows these steps:

• Data Collection: Data is collected from various sources, such as sensors, devices, or applications, and streamed to a central system.
• Ingestion: The collected data is ingested into a real-time analytics platform. This can involve data preprocessing and formatting to ensure compatibility.
• Processing: Data is processed in real-time using complex algorithms and analytics engines. This step involves extracting valuable information and patterns from the incoming data.
• Analysis: Analytical tools are applied to the processed data to derive meaningful insights. This can include statistical analysis, machine learning models, or other algorithms based on the specific use case.
• Visualization: The results of the analysis are often visualized through dashboards or reports, making it easy for users to understand and interpret the information quickly.
• Actionable Insights: Decision-makers can use the real-time insights to make informed decisions, respond to events as they happen, or trigger automated actions based on predefined rules.

Key challenges in building real-time analytics

• Massive data volumes: Processing and transferring terabytes of data for real-time analytics is extremely costly.
• Low-latency processing: Achieving sub-second response times for data processing and analysis while handling high throughput rates of millions of events per second.
• Data integration complexity: Seamlessly integrating diverse data sources like databases, streaming platforms, and APIs.
• Latency control: Minimizing processing latency down to milliseconds or microseconds to enable near-instant insights and rapid response times.
• Anomaly detection and forecasting: Using advanced algorithms to detect anomalies in real-time and provide accurate forecasting models with low error rates using historical and real-time data.
• Data security and compliance: Implementing robust security measures including end-to-end encryption, access controls, and compliance with industry standards.

The Mechanics Behind Real-Time Data Processing

Real-time data analytics functions by pushing or pulling data into the system and typically includes components like in-database analytics, processing in memory (PIM), in-memory analytics, and massively parallel programming (MPP). Streaming data in real-time analytics is a continuous generation and collection of data in a live environment, which involves processing data as it is generated and analyzing it without any considerable latency.

Event processing in real-time data analytics serves to analyze data streams in real-time, detect patterns, and enforce rules to trigger suitable actions. Additionally, it involves storing the processed events for further analysis, reporting, or archival purposes. Tools such as Apache Kafka, Amazon Kinesis, Google Cloud Dataflow, Apache Spark Streaming, and Splunk are commonly used to collect and manage streaming data.

Streaming Data and Event Processing

Streaming data and event processing are key components of real-time analytics, allowing for uninterrupted flow and immediate analysis of data as it is produced. This allows for real-time processing and instantaneous action on the data, empowering organizations to optimize the power of real-time analytics for more informed decision-making.

Some examples of technology that provide effective solutions for real-time event processing in data analytics include:

• Apache Flink
• Apache Kafka
• Apache Storm
• Spark Streaming

These tools enable organizations to process and analyze streaming big data in real-time, enabling them to make faster and more accurate decisions based on up-to-date information.

Taking advantage of streaming data and event processing, organizations can identify patterns, trends, and anomalies as they emerge. This empowers them to react quickly and make prompt decisions based on the freshest information available. Examples of technologies/tools employed for streaming data and event processing in real-time analytics include:

• Apache Kafka
• ClickHouse
• Amazon Kinesis
• Apache Spark
• Apache Flink
• Apache Storm
• Google Cloud DataFlow

Harnessing Machine Learning for Predictive Insights

Machine learning plays a pivotal role in providing predictive insights in real-time analytics, as it is capable of continuously analyzing real-time data and updating models to make accurate predictions. This enables the identification of patterns and trends in the data, allowing businesses to make informed decisions and take prompt action based on the insights generated. Moreover, real-time machine learning models can independently make ongoing decisions that directly affect the business, leading to more intelligent decision-making and better results.

Organizations can leverage machine learning algorithms to automate decision-making processes by training them on historical data and using them to analyze new data in real-time. These algorithms can identify patterns, diagnose issues, anticipate outcomes, and make predictions or decisions based on the available data, thus enabling decision-makers to make efficient and informed decisions in a timely manner.

The Advantages of Immediate Data Analysis

Real-time analytics provides immediate insights and enables quick actions, leading to improved customer engagement, more effective financial trading, and instant credit scoring. The timely identification of issues enabled by real-time analytics allows businesses to address them before they become more widespread or have a greater impact on the organization.

Finance teams can also benefit from real-time data analytics, as it allows them to evaluate daily operations, apply process improvement, and monitor a business’ financial standing. By leveraging real-time analytics capabilities, businesses can make more informed decisions and take action based on the latest available data.

Enhancing Customer Engagement with Real-Time Insights

Real-time analytics can offer understanding into time-sensitive customer data, leading to enhanced customer satisfaction. By analyzing customer interactions data in real-time, businesses can:

• Deliver personalized and relevant experiences
• Create hyper-personalized interactions that resonate with customers
• Build trust
• Enhance customer engagement

Customer engagement tools such as customer onboarding tools, surveys, live chats, chatbots, and email marketing tools can be employed to gain real-time insights and interact with customers effectively. By leveraging real-time data and event processing, organizations can strengthen customer retention and loyalty by identifying patterns and trends in customer behavior, keeping track of key metrics, and providing immediate insights. These insights can be used to optimize advertising and marketing campaigns for better results.

Financial Trading and Real-Time Credit Scoring

Real-time analytics plays a significant role in financial trading. It enables traders to:

• Keep track of financial markets
• Identify trends
• Make informed trades by using immediate information from financial databases, news outlets, social media, and other relevant data sources

This results in more astute and informed trading decisions.

Real-time credit scoring is a process utilized by lenders to make timely and precise credit risk decisions. It involves the utilization of real-time data and analytics to evaluate an individual’s creditworthiness at the time of the credit application, allowing lenders to make faster and more informed decisions regarding whether to approve or deny a credit application.

Real-time credit scoring typically involves analyzing various factors such as:

• Credit history
• Income
• Debt-to-income ratio
• Other pertinent data points

This analysis helps evaluate an individual’s creditworthiness.

Real-time streaming data architecture utilizing Kafka

A data producer like a web server can publish data to Kafka. Kafka organizes data into topics, allowing the producer to disseminate data on a specific topic. The real-time streaming architecture involves several key steps:

• Data publication: Producers like web hosts or servers publish data to Kafka, which organizes it into topics.
• Data consumption: Consumers or Spark Streaming components listen to specific Kafka topics to reliably consume the data in real time.
• Processing with Spark Streaming: Spark Streaming receives the consumed data and performs real-time processing and transformations using its capabilities.
• Storage: The processed data can be stored in systems like MySQL or Cassandra based on needs.
• Real-time data pipeline: Kafka acts as a backbone for efficiently processing and transmitting high-volume, high-velocity data through a real-time pipeline.
• Subscription and dashboard: Subscribed Kafka data is pushed to a dashboard via APIs, enabling users to visualize and interact with real-time data.

In summary, this architecture leverages Kafka’s strengths in data publication, consumption, and processing along with Spark Streaming’s capabilities to create a robust pipeline that can manage high-velocity data and deliver real-time insights through an interactive dashboard.

Real-World Analytics With ClickHouse

Yango Tech, a global IT solutions provider for retailers and e-commerce platforms, needed to develop a minimum viable analytics solution to visualize real-time store data.

Requirements included speed, scalability, easy integration of new data sources, dashboards, reports, and embeddable analytics.

To avoid vendor lock-in, they chose the open-source ClickHouse database and DoubleCloud visualization.

By deploying ClickHouse on AWS S3 and configuring data pipelines, Yango Deli enabled real-time data transfer from their WMS and ERP systems. The intuitive DoubleCloud interface facilitated building reports and dashboards accessible to partners and internal teams.

This solution empowered monitoring new partner integration, effortless additions of data sources, and data-driven business improvements. Valuable operational insights enhanced efficiency, profitability, and optimization.

Notably, compared to prior solutions, ClickHouse and DoubleCloud cost 20% less. Key selection criteria were simplicity, speed, and flexibility.

How can DoubleCloud assist in maximizing the benefits of ClickHouse?

DoubleCloud offers a fully-managed database service for ClickHouse, an open-source analytical database known for blazing fast query speeds. Our managed solution provides the following key benefits:

Simplified Setup and Management

We handle all ClickHouse installation, configuration, maintenance and updates, freeing you to focus on building your analytics applications.

Reduced Operational Overhead

As a managed service provider, we take care of the heavy lifting of deploying, monitoring and securing your ClickHouse infrastructure.

Reliability and Availability

We provide a resilient infrastructure designed for high uptime and quick disaster recovery. You get peace of mind knowing your ClickHouse cluster is in good hands.

Enhanced Data Security

Your data is safeguarded through encryption, access controls and other security best practices. We enable analytics agility without compromising on protections.

Flexibility and Scalability

Our solution allows seamless scaling of your ClickHouse resources up and down to match your analytical needs, giving you flexibility as requirements change.

Expert Guidance and Support

Our ClickHouse team provides reliable support, assistance and guidance throughout your use of our managed service. Consider us your trusted analytics advisors.

In summary, DoubleCloud simplifies ClickHouse adoption by managing the complexity behind-the-scenes. We handle the heavy lifting so you can unlock greater analytics velocity and time to insight.

Summary

In conclusion, real-time analytics offers businesses the power to make intelligent decisions based on up-to-the-minute data analysis. With the ability to process and analyze data as it is generated, businesses can improve customer engagement, optimize financial trading, and make more informed decisions. By overcoming the challenges of implementing real-time analytics, integrating it with existing business intelligence systems, and empowering business users through accessible analytics solutions, the potential impact of real-time analytics on businesses can be truly transformative.

Frequently Asked Questions

What is real-time usage analytics?

Real-time usage analytics enables organizations to use data as soon as it is produced to answer questions and make predictions quickly, enabling better decisions. Gartner defines real-time analytics as “the discipline that applies logic and mathematics to data to provide insights for making better decisions quickly.”

What is the difference between real-time analysts and data analysts?

Real-time analysts work with data as soon as it is captured, providing up-to-date insights for faster decision-making, whereas data analysts use stored data to push out business insights.

What is the main difference between real-time analytics and batch analytics?

Real-time analytics processes data in the moment it is generated, while batch analytics evaluates data that has already been collected.

How does real-time analytics help improve customer engagement?

Real-time analytics provide insight into customer data, allowing for optimization of customer satisfaction and improved engagement, retention and loyalty.