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What is Data Streaming

Data streaming is the process of continuously and rapidly ingesting, processing, and analyzing large volumes of data as it is generated and received in real-time. The data is processed and analyzed as it flows, known as stream processing, rather than waiting for it to be stored in its entirety and processed as in batch processing. Data streaming technology is designed to handle high-velocity, high-volume, and high-variety data and enables real-time decision making, event detection, and action triggers.

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Definition of a Data Stream

There are two definitions of data stream: a popular (streaming data) and the technical (a data stream of data packets). Data streams refer specifically to a flow of data packets, whereas data streaming refers to the complex process of receiving continuous data from multiple systems to be processed in real-time.

Data streaming can be used in a variety of applications, such as:

  • Real-time analytics: where large data sets are analyzed in real-time to identify patterns, anomalies, and trends.
  • Internet of Things (IoT): where data from sensors and devices is collected and analyzed in real-time to monitor and control physical systems.
  • Fraud detection: where financial transactions are analyzed in real-time to detect fraudulent activity.
  • Social media analytics: where data from social media platforms is analyzed in real-time to track sentiment and opinion.
  • Streaming media: where video or audio is sent and received in real-time over the internet.

Data streaming can be implemented using a variety of technologies, such as Apache Kafka, Apache Storm, Apache Flink, and Apache Samza. These technologies provide a platform for data streaming, including data ingestion, processing, and storage.

What is stream processing?

Combining data streams with stream processing technology enables the real-time processing of continuous data streams as they are generated or received. Stream processing allows for the immediate analysis and action on data, rather than waiting for it to be stored and then analyzed later. Stream processing systems can process large volumes of data in real-time, making it suitable for use cases that require real-time decision making, such as fraud detection, anomaly detection, and real-time analytics.

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Understanding Data Stream Processing

Data stream processing can be contrasted with batch processing. The inherent latency in batch is what differentiates these two processing techniques. Latency is the difference between when data is generated at the source until when it is made available to use in the target system. The two major processing methods are batch processing, highly common, and real-time streams which are essential in time sensitive applications. Batch processing, as the name suggests, is performed at intervals in batches and therefore the latency is the time between these processing intervals. In stream processing, latency is reduced to the efficiency of the data stream and its subsequent processing and storage for use.

Batch processing vs. real-time streams

Most data moves between applications and organizations as files or chunks of data on request or periodically as updates. This process is known as a batch or sometimes ETL. The batch is usually very large, and requires significant time to transfer and resources to process, therefore it is often performed during off-peak hours when compute resources can be wholly dedicated to the job. Batch processing is often used for data conversions, migrations, and archiving, and is particularly useful in processing huge volumes of data in short time frames.

Batches are processed in one go which entails synchronization risks where one system that is periodically updated by batch processes becomes out of sync until the update batch is completed. But, there are many techniques to mitigate this risk, including adjusting batch frequency, using a scheduler, and the use of micro-batches.

Some systems too critical to business operations cannot take latency, for example, ordering and inventory systems that could be processing thousands of transactions an hour. This scenario calls for real-time, synchronous solutions. In streaming data processing, or target accumulation, the target system does not wait for a source-based scheduler, instead, it will accumulate data into a buffer queue and process it in order.

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Data Stream Benefits

The main benefit of data streaming, or low latency, is that data transmission and processing are conducted over extremely fast data integrations at long distances with greater speed than any other data synchronization type. The drawback is a much greater investment in both hardware and software solutions to support the necessary techniques that maintain real-time speeds.

Data streams have several key benefits, including:

  • Low latency: Data streams can be processed in real-time, as soon as they are generated, resulting in low latency.
  • Scalability: Data streams can handle large volumes of data, allowing for easy scalability.
  • Flexibility: Data streams can be processed and analyzed in various ways, such as through batch processing or real-time processing.
  • Cost-effectiveness: Storing and processing data streams can be more cost-effective than storing and processing large datasets.
  • Easy integration with other systems: Data streams can be easily integrated with other systems, such as databases or data lakes, for further analysis and storage.
  • Continuous learning: Data streams can be used for continuous learning, where the model can learn from new data as it arrives.

Data Stream Challenges

Data streams are inherently challenged in multiple ways, however, data stream processing tools and solutions are able to readily overcome these challenges. Some of those challenges include:

  • Massive Quantities of Data — Data stream processing must be able to handle high-velocity, unbounded data streams in real-time.
  • Concept Drift — Solutions must deal with concept drift, where the statistical properties of the data change over time and eventually invalidate the original model.
  • Data Quality Issues — The handling of missing or incomplete data.
  • Stationarity of Data — Handling data with complex, non-stationary distributions indicate that its mean, variance, and covariances will change over time.
  • Latency — Providing low latency for query results and updates.
  • Privacy and Security — Maintaining the privacy and security of the data
  • Scalability — Scalability to handle large volumes of data and many concurrent users.

What are the components of Data Streams?

A streaming data architecture model is typically composed of three components: adapters, a streaming data processing engine, and query groups. Extract, transform, and load (ETL) functions are still present, but have been equated to an event within the data stream, performed continuously.

  • Adapters — Before the data processing engine is the input adapter which converts input data to a format that can be processed by the stream data processing engine and then sends the data to the stream data processing engine. After the data processing engine is the output adapter which converts processed data to a specified format, and then outputs the data.
  • Stream Data Processing Engine — In the stream data processing engine, input data will be processed in accordance with a pre-registered query, or filter, and then be sent to the output adapter.
  • Query Groups — Query groups represent the analysis scenarios that the stream data processing engine adheres to when determining how to process input data. A query group consists of an input stream queue (input stream), query, output stream queue (output stream). The query defines how input data is to be processed.

The simplicity of the model components above belies the reality. Considering that streaming data processing systems may need to handle millions of daily events, components, like aggregators and brokers installed to assist in orchestrating these complex streaming systems.

  • Aggregators — Aggregators are essential components given the numerous data sources that businesses need to ingest in their operations. Aggregators collect event streams and batch files and pass them onto brokers.
  • Brokers — Brokers make data available for consumption or ingestion by a streaming data engine that blends streams together.
  • Streaming Data Storage — Advancements in cloud storage technology, data warehouses, and data lakes have made storing streaming event data economical. Many businesses can easily retain detailed records on all their operations, capable of pulling historic records, and are able to do so without having to own the infrastructure themselves.

Examples of Data Streams

Examples of data streams include:

  • Social media feeds: tweets, posts, and comments on platforms such as Twitter and Facebook
  • IoT sensor data: temperature, humidity, and other sensor readings from connected devices
  • Financial data: stock prices, trading volume, and other financial market data
  • Network data: traffic and logs from network devices such as routers and switches
  • Clickstream data: website visitor interactions and browsing history
  • Video and audio streams: live and recorded video and audio streams from cameras and microphones
  • Environmental data: weather data, air quality readings, and other environmental data.

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