Real-time Data Streaming: Why it’s much more than just a buzzword
The real-time data streaming process involves ingesting data from a variety of sources and processing it in real-time so that meaningful and insightful information can be extracted. The term “streaming” is used to describe continuous, never-ending data streams with no beginning or end, that provide a constant feed of data that can be utilized/acted upon without requiring it to be downloaded first. With real-time data streaming, organizations can analyze and process data immediately, instead of waiting hours, days, or even weeks.
Streaming data include log files generated by customers using your mobile or web applications, e-commerce purchases, in-game player activity, information from social networks, financial trading floors, or geospatial services, and telemetry from connected devices or instrumentation in data centers.
Understanding the Real-time Streaming Architecture
The streaming data architecture consists of software components that are designed to manage real-time streaming data.
As opposed to traditional batch processing, real-time data streaming allows for immediate and accurate responses to incoming data.
Real-time streaming data infrastructure usually consists of the following components:
1. Stream Source
Real-time data streaming can be derived from a number of sources, including IoT sensors, mobile apps, and databases.
2. Stream Ingestion
These tools act as an intermediary between the source of streaming data and the system that is receiving it. Stream ingestion transforms incoming raw streams into a consumable format, such as JSON or CSV.
3. Stream Storage
The streamed data needs to be stored somewhere which can be used later. Stream storage solutions like AWS Kinesis Data Streams, or Estuary Flow on Amazon S3 or Google Cloud Storage, can provide scalable and cost-effective solutions for storing streaming data in either a data lake or data warehouse.
4. Stream Processing
Tools such as these transform incoming data into a structured format by performing tasks such as validation, normalization, and enrichment so that it can be analyzed further. Some of the popular tools to process streaming data include Apache Spark Streaming, Apache Flink, and Azure Stream Analytics.
5. Stream Destination
After completing the four stages mentioned above, the analysis generated needs to be sent somewhere to derive value from it. This is typically done by sending it downstream to:
- Third-party integrations
- Data warehouses
- Event-driven applications
Streaming real-time data has many advantages
Real-time data streaming offers various advantages by empowering organizations to make informed business decisions. They can gain valuable insights into their customers’ behaviors, preferences, and trends by using real-time data streaming insights which can help in making timely and informed decisions to drive the business growth.
Let’s look at some of the most common benefits that real-time data streaming can offer:
1. Real-time Insight
Gain access to and take advantage of up-to-date information and insights for strategic planning that will keep you ahead of the competition.
2. Faster Decision-Making
Act quickly on current data, make faster decisions, and capitalize better on emerging opportunities. You can reduce latency significantly by responding immediately when an event happens, therefore increasing ROI in the process.
3. Addressing Real-Time Business Needs
Address real-time business requirements such as improved omnichannel customer experience or better customer service response times. This leads to more informed decisions overall.
4. Increased Accuracy
Real-time stream processing offers improved accuracy because it can analyze high-velocity streams constantly at very small intervals. This ensures that no data is left out and you can identify and resolve problems quickly.
5. Improved Scalability
Stream processing can support hundreds or thousands of streams at once without degrading performance, making it ideal for scalability.
6. Reduced Latency
Real-time stream processing can respond immediately when an event occurs, empowering organizations to make faster, more accurate decisions and improving customer satisfaction levels significantly.
7. Cost Savings
Real-time stream processing reduces server costs due to its distributed nature. It also helps in saving money on setting up data infrastructure since it requires fewer resources than traditional approaches.
8. Enhanced Security
Real-time stream processing comes with native encryption capabilities which ensures the privacy and security of customers’ personal or confidential information. It protects sensitive data from malicious actors or any other unforeseen incidents that may occur during the transmission or storage of data streams.
9. Improved User Experience
With real-time stream processing, accessing information across verticals and departments becomes hassle-free. Organizations can create intuitive and interactive user interfaces, eliminating the need for complex menus or search functions, unlike traditional approaches.
A real-time data streaming solution also allows you to take immediate action on insights generated by analytics tools instead of waiting for a response from another tool or system.
Real-Time Data Streaming in the Real-World
Real-time streaming can be applied in real-world situations. Some of the most commonly seen use cases of real-time data streaming include:
1. Media Streaming
The ability to access and watch on-demand content at any time, from anywhere in the world, is a key benefit of real-time streams in media delivery and broadcasting. It also enables broadcasters to provide high-quality audio/video streams with very low latency.
2. Real-Time Analytics
The use of real-time analytics is becoming increasingly common across organizations and industries in order to gain actionable insights into customer behavior and operational performance. It also helps organizations to track, monitor, and respond quickly to evolving customer needs.
3. Financial Trading Floors
Financial trading floors heavily rely on the speed and responsiveness of real-time data streaming technology which enables traders to swiftly react to market conditions and seize opportunities the moment they emerge. With the help of real-time data streams, traders can analyze trends and discern patterns, making them better informed when trading stock or other investments.
4. Geospatial Services
Real-time stream processing is often used in geospatial services such as navigation systems or mapping applications by allowing these services to update location information in real-time. Applications such as Google Maps can help drivers in accessing real-time traffic information while traveling.
5. eCommerce Purchases
To increase sales, many eCommerce sites have integrated real-time streaming technology into their platforms, allowing customers to swiftly and effortlessly complete purchases thereby reducing a lengthy checkout process.
eCommerce websites such as Myntra and Amazon are increasing sales by providing customers with recommendations based on their current shopping cart contents.
6. Credit Card Fraud Detection
Credit card fraud detection has improved significantly through real-time stream processing solutions. It allows credit card companies and banks to continuously monitor transactions to detect suspicious activities immediately rather than waiting until after transactions have already been processed.
7. Efficiency in Logistics
Real-time data streams have enabled logistics companies to optimize delivery routes by analyzing traffic conditions and weather forecasts to obtain real-time updates about traffic jams, closed roads, etc. It allows drivers to make faster deliveries with fewer delays, saving both money and resources.
8. Personalized Customer Experiences
Through its ability to leverage customer data, real-time stream processing has allowed businesses to customize web experiences for customers according to their preferences by recommending products and displaying relevant offers. It provides customers with a highly personalized experience on websites, resulting in overall better engagement levels and increased sales conversions.
By continuously monitoring data stream anomalies, real-time stream processing detects security threats in the early stages, helping companies to take appropriate measures immediately upon detection. It also allows for storing and analyzing log files generated during customer interactions and helps security personnel to detect suspicious activities.
Qubole Pipeline Services: Your one-stop solution for real-time streaming
Real-time data streaming is emerging as one of the most crucial technologies of this decade. It has become indispensable in many business domains as it allows for the rapid collection and analysis of important information and helps in making well-informed business decisions. This adoption has resulted in improved process efficiency and cost savings, highlighting the significant impact of this technology.
As data-driven innovations such as AI/ML and the IoT increasingly drive competitive advantage for organizations, troves of streaming data are continuously generated from multiple internal and external sources. A rapidly growing number of organizations need a solution that makes it faster and easier for developers to unlock the value of streaming data, and Qubole Pipelines Service fully addresses this need.
Qubole Pipeline Services makes it much easier and faster for customers to build robust, scalable streaming data pipelines and capitalize on the rapid growth of real-time data in their businesses. With Qubole Pipeline Services, data teams can now build, test, deploy, monitor, and manage hundreds of streaming data pipelines from a single platform, resulting in increased productivity, greater innovation, and reduced operating costs.
With Qubole Pipeline Services, businesses can complement their existing data lake with advanced features that help them instantly capture streaming data from various sources, accelerate the development of streaming applications, and run highly reliable and observable production applications at the lowest cost. This can all take place in a managed environment via the public cloud of their choice.
Salient new features include:
- Accelerated Development Cycle: Numerous built-in connectors, code generation wizard, dry run framework, and quick-start options that help accelerate development lifecycles. A pipeline can be developed within minutes without writing even a single line of code.
- Robust and Cost-Efficient Stream Processing Engine: Leveraging Apache Spark Structured Streaming, Qubole added several enhancements including Rocksdb state storage, direct writes, and memory pressure scheduling, among others for reliably building and deploying long-running streaming applications.
- Comprehensive Operational Management: Qubole Pipelines Service includes a broad set of APIs and user interfaces for engineers to holistically manage the lifecycle of streaming applications and get continuous operational insights.
- Data Management and Consistency: The new pipelines service uses Qubole’s ACID framework to efficiently compact small files in the background while allowing concurrent read/write operations, without impacting performance.