What is streaming data?

The primary distinction between streaming data and static data lies in their temporal characteristics:

• Static Data: Is relatively stable and unchanging over time. It is typically stored in databases and files, with updates occurring at discrete intervals. For instance, customer information in a relational database, product catalogs, and historical sales data are all forms of static data.
• Streaming Data: Is continuously generated and updated in real-time. It does not have a fixed structure or predefined endpoints, and it is often transient. Data streaming may be generated by a wide range of sources and is typically used to monitor and respond to events as they happen.

Streaming data finds applications in various domains and industries. Its real-time nature makes it invaluable for making informed decisions, responding to events, and gaining deeper insights. Here are some prominent use cases:

• Real-time Analytics: One of the most common applications of streaming data is real-time analytics. Organizations can leverage this data to gain immediate insights into their operations, customer behavior, and market trends. For example:
  • E-commerce platforms use streaming data to monitor user activity and recommend products in real-time.
  • Financial institutions analyze stock market data to make real-time trading decisions.
  • Transportation companies optimize routes and schedules based on real-time traffic and weather data.
  • Gaming companies adjust game scenarios in real-time based on a player’s activities within the game.

• Fraud Detection: Financial and e-commerce industries rely heavily on streaming data to detect fraudulent activities. By monitoring transaction data in real-time, they can identify anomalies and potentially fraudulent transactions before they cause significant damage.
  • Credit card companies use it to flag suspicious transactions for further review or prevent them from going through
  • E-commerce platforms track user behavior to identify fraudulent activities, like account takeovers and payment fraud.

• Internet of Things (IoT): The IoT is a prolific source of streaming data, with countless sensors and devices continuously generating information. Streaming data from IoT devices enables:
  • Predictive maintenance of industrial machinery by monitoring equipment sensors for anomalies which saves money on costly repairs.
  • Smart home devices to provide real-time insights into energy consumption and security.
  • Environmental monitoring through sensors that track air quality, temperature, and more.

• Social Media Monitoring: Social media platforms are treasure troves of streaming data. Businesses and individuals use this data to gauge public sentiment, track trends, and make marketing decisions. For example:
  • Companies analyze social media feeds to understand customer sentiment and receive immediate feedback on their products or services.
  • Political campaigns use social media data to monitor public opinion and adapt their messaging strategies.

Managing streaming data requires a robust IT infrastructure capable of handling data ingestion, storage, processing, and scalability. Here are the key components of this infrastructure:

• Data Ingestion: Data ingestion is the process of collecting streaming data from various sources and making it available for processing. This often involves data connectors, APIs, and middleware solutions that gather data from different sources and deliver it to a central processing system. Popular tools for data ingestion include Apache Kafka, Amazon Kinesis, and RabbitMQ.
• Data Storage: Storage has been a major priority for companies doing anything with streaming data. That is because most traditional streaming solutions cannot do anything with the data until it is stored. Most traditional relational databases are typically ill-suited for streaming data due to their static nature. Instead, organizations have turned to specialized data stores that can handle high volumes of real-time data. These include:
• NoSQL databases: These databases, like Apache Cassandra and MongoDB, are designed for high write and read throughput, making them suitable for streaming data storage.
• Time-series databases: These databases, such as InfluxDB and Prometheus, are optimized for storing time-ordered data points, which are common in streaming data.
• Streaming databases: This is a relatively new classification, which actually comprises several different types of data management devices, with the key qualification being that they can process data in real-time, regardless of how they store it. They typically use the same terminology as other types of databases, such as tables, rows, columns, and indexes, and also rely on SQL as the main query language. 
  But even these new storage technologies can introduce unnecessary latency into the real-time data process.

• Data Processing: Streaming data needs to be processed in order to extract insights and value from it. In older systems, that requires the data to be stored first and, only then, could it be analyzed or processed. A new form of data processing tools called stream processors or stream processing platforms play a critical role in this stage. Apache Flink, Apache Storm, Apache Spark and Hazelcast Platform are examples of popular data processing frameworks used to analyze streaming data in real-time or near real-time.Warning: Some companies seem to confuse or conflate  the two concepts of data streaming and stream processing in their marketing material, perhaps intentionally. Data streaming is the conveyance or movement of the data from one place to another. Stream processing is doing something useful on or with the data itself. Kafka, by itself, is not a stream processor but rather a data streaming platform. Confluent sometimes calls it a streaming engine, but even that is misleading since engine implies action. It might be more accurate if you think of Kafka more as a pump than as an engine.
• Scalability: Scalability is a key consideration in streaming data infrastructure. The volume of streaming data can fluctuate significantly, and organizations need to be able to dynamically scale their systems to handle the load in a cost-effective and efficient way. Cloud-based solutions, such as Amazon Web Services (AWS), Google Cloud Platform, and Microsoft Azure, offer elasticity and scalability features that allow organizations to adapt to changing data volumes seamlessly.

Analyzing streaming data requires specialized tools and techniques to extract meaningful insights from the continuous flow of information. Here are some essential elements of streaming data analysis:

• Data Visualization: Data visualization tools are crucial for making sense of streaming data. Real-time dashboards and charts enable organizations to monitor key metrics and events as they happen. Tools like Tableau, Grafana, and Kibana are commonly used for visualizing streaming data.
• Machine Learning: Machine learning models can be applied to streaming data to make predictions, detect anomalies, and automate decision-making processes. For instance:
  • Anomaly detection models can identify unusual patterns in streaming data that may indicate a problem.
  • Predictive maintenance models can anticipate equipment failures by analyzing sensor data from machinery.

• Anomaly Detection: Anomaly detection is a vital aspect of streaming data analysis. Anomalies in streaming data could be signs of issues, threats, or opportunities. Machine learning algorithms, statistical techniques, and rule-based systems are used to detect anomalies in real-time. Common approaches include:
• Threshold-based Detection: Setting thresholds for specific metrics and flagging data points that exceed or fall below these thresholds.
• Machine learning-based Detection: Training models to recognize patterns in the data and identify deviations from these patterns.
• Time-series Analysis: Analyzing historical data to identify cyclic patterns, seasonality, and deviations from expected trends.

While streaming data offers numerous advantages, it also presents challenges that organizations need to address:

• Latency: Processing and analyzing data in real-time introduces latency concerns. Ensuring that data is processed quickly enough to support real-time decision-making is a critical challenge. To overcome this, organizations must optimize their data processing pipelines and employ efficient technologies.
• Data Quality: Streaming data can be noisy and prone to errors. Inconsistent or inaccurate data can lead to incorrect conclusions and decisions. Implementing data quality checks, data validation, and cleansing processes is essential to maintain data accuracy.
• Scalability: As data volumes grow, organizations must ensure their infrastructure can scale horizontally to accommodate the increased load. This may require the adoption of cloud-based solutions or the development of a flexible and scalable architecture.

Herrell concludes by stating that the logical next step is using stream processing to power ML-driven applications for inference based on what’s happening in the moment. That will enable companies to meet customer needs instantly, even before the customer is aware that they will be having that need shortly- based on previous patterns.