In this fast-paced digital era, multiple sources like IoT devices, social media platforms, and financial systems generate the data continuously and in real-time.  Every business wants to analyze these data in real-time to be ahead in the competitive game. 

Streaming Data Pipeline is becoming a game changer in this area. It has the ability to ingest and process real-time data and gather insights quickly to share with businesses.

To process the data in real-time where every second counts, the organizations rely on streaming data pipelines for seamless processing of the data and generate the insights in real-time.

Traditional data pipeline which processes the data in batches, streaming pipelines is different as it process the data instantly and enable the businesses to react to changes as they happen

In this blog, we will dive deep into what streaming data pipelines are, how they work, their use cases, and how they compare to batch data pipelines.

What are Streaming Data Pipelines?

Streaming data pipelines are data pipelines that are designed to ingest, process, and transport data continuously from one or multiple sources in real-time. Streaming data pipelines run continuously as compared to batch pipelines, which process the data at fixed intervals.

Streaming data pipelines handle the data as it arrives thereby enabling quick analysis and decision-making for businesses.

Streaming data pipelines is crucial where timing is critical, such as monitoring work, traffic data, transaction data to avoid fraudulent activity, sensor data, or personalized experience in real-time. 

The various streaming tools are Apache Kafka, Apache Flink, AWS Kinesis, GCP Pub-Sub, etc.

Key Features of Streaming Data Pipelines:

1. Real-Time Processing: Streaming data pipelines run continuously to ingest the data as they arrive, analyze it using a set of logic, and generate results for consumption. 
2. Scalability: The Streaming Data Pipelines should have the ability to handle high data volumes without compromising performance. Tools like Apache Kafka, Apache Flink, AWS Kinesis, and GCP Pub-Sub are a few examples that handle scalability without impacting performance. 
3. Low Latency: Streaming Data Pipeline offers low latency so that Insights and decisions can be made almost instantly. This is a very crucial feature of any streaming data pipeline as delay in the analysis can cost heavily.
4. Flexibility: Streaming Data Pipeline supports diverse use cases, from IoT applications to fraud detection.

For example, a taxi service like Uber uses streaming pipelines to match the riders and drivers position, estimate the arrival and departure times, and provide live traffic updates. These services uses the Streaming Data Pipeline in the background to ensure seamless, real-time experiences. Read the advantages of data pipeline and leverage its benefits to the fullest.

How do Streaming Data Pipelines Work?

Although the Streaming Data Pipeline has different stages as per the requirement. However, the Streaming Data Pipelines has four major stages as described below –

1. Data Ingestion: It is the initial step in the Streaming Data Pipeline. This step involves collecting raw data from multiple sources in real time and ingesting them for further analysis.
   • The various sources like IoT devices, application logs, user interaction, financial transactions, social media posts etc. generate a high volume real-time data.
   • Tools like Apache Kafka, Amazon Kinesis, Google Pub Sub, and RabbitMQ are commonly used for high-throughput real-time data ingestion.

Example: Social media platforms like Twitter, Facebook, Instagram, etc. uses Ingestion pipelines to process millions of tweets/events per second.

2. Data Processing: When the data is ingested to the system, it is then processed to derive meaningful insights from the data in the real-time. Streaming data pipeline involves the following steps for Data Processing –
   • At first, the data is filtered to remove duplicates, junk records, and other irrelevant information.
   • Then the aggregation is performed on the data to gather the insights by applying business rules.
   • The data is then enriched with additional data sources to increase the quality of the data.
   • Incoming data streams are processed in real-time using frameworks like Apache Flink, Spark Streaming, or Google DataFlow.

3. Data Storage: Once the data is processed, it is stored in the optimized format for quick retrieval, thereby reducing latency. The popular databases used for storage are ElasticSearch, Redis, Snowflake, etc.
   • Example: A stock trading application or real-time sports streaming uses a high-performance database to store real-time market data for instant access.

4. Data Delivery: Data Delivery is the last step of the Pipeline. This step makes the analyzed and processed data available for the end user or any downstream application. The data is available via API, dashboards, or real-time reports.

Popular visualization tools such as Tableau or Power BI consume this data and make it available for the end users.

Use Cases of Streaming Data Pipelines?

1. Fraud Detection

Real-time fraud detection is critical for any financial service to safeguard customer’s assets. The Streaming data pipeline along with Machine Learning analyses the transaction patterns and flags anomalies. Streaming data pipelines analyze streams from multiple sources (e.g. transaction logs) to detect fraud activity.

Example:

• Streaming Data Pipeline can monitor the users transactions for unusual activity, such as sudden purchases, gambling purchases, frequent transactions at a location, or any other activity that may be treated as fraudulent.
• Streaming Data Pipeline triggers the alerts that can block the transactions and alert the users to take preventive actions.

2. IoT and Smart Device Monitoring

IoT devices generate huge amounts of data within a short period, and they need real-time processing to ensure the smooth and reliable running of the devices. The popular IoT applications are – 

• Predictive maintenance application analyses sensor data to predict equipment failures in manufacturing based on the data generated by the sensors.
• In Smart Cities, traffic lights are managed based on real-time traffic information and patterns. 

3. User Personalization in E-Commerce

E-commerce is a growing business in today’s era. It processes the user activity in real time to send the personalized experiences to the users which increases the engagement with the customers.
They use the Streaming data to provide personalized product recommendations to the customers and feature dynamic pricing when there is high demand.

Benefits:

• The streaming data pipeline enables product recommendations for users based on their real-time browsing behavior.
• Streaming Data pipeline enables the e-commerce to dynamically update the pricing based on supply, demand, and competitor activity.

Example:

Amazon uses streaming pipelines to update its “Customers who bought this also bought” recommendations as users interact with the platform.

4. Operational Monitoring in IT Systems

IT systems are often monitored via streaming pipelines. The various IT logs such as CPU health, CPU usage, activity monitor, memory consumption, etc. are streamed through the streaming data pipeline and enable the team to detect any issues.

Streaming data pipelines are used to trigger the alerts when anomalies are detected, such as a spike in CPU usage or memory consumption. IT teams use streaming pipelines to monitor logs and system metrics continuously. This enables early detection of performance issues and faster response times.

Differences between Streaming & Batch Data Pipelines

Streaming and Batch pipelines are the two essential pipelines in the data processing world. However, they significantly contradict each other based on their use cases, features, and performance differ significantly. 

The below table shows a side-by-side comparison against a feature for the Streaming Data Pipeline and Batch data Pipeline.

Feature	Streaming Data Pipeline	Batch Data Pipeline
Data Processing	Continuous processing of incoming data in real-time.	Processes data in scheduled and fixed intervals.
Latency	Data is processed with Low latency i.e. results available almost instantly.	Data is processed with Higher latency i.e. results are delayed by intervals.
Scalability	Auto scales dynamically to handle variable data loads.	It may require significant reconfiguration for scaling.
Error Handling	Errors are identified and corrected in real-time.	Errors are detected post-processing.
Use Cases	Ideal for real-time analytics, fraud detection, and IoT applications.	Suitable for historical data analysis and reporting.

Conclusion

In today’s digital era, businesses should adapt the changes quickly to be competitive in the market. The streaming data pipeline empowers the organization to process and analyze the data as soon as it is ingested to the streams. Streaming Data Pipeline helps organizations to prevent frauds, optimize IoT operations, real-time analysis, and many more. 

As technology advances, streaming data pipelines is becoming essential for organizations to build a safe and reliable system that prevents their users from frauds as well as generating the real time insights to grow their business.

Hevo is an all-in-one cloud-based ETL pipeline that helps you transfer data and transform it into an analysis-ready form. Its native integration with 150+ sources (including 60+ free sources) ensures you can move your data without writing complex ETL scripts. 

Sign up for Hevo’s 14-day free trial and experience seamless data migration. Check out the pricing details to understand which plan fulfills all your business needs.

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