What is streaming data?

Streaming data is data that is generated continuously by thousands of data sources, which typically send in the data records simultaneously, and in small sizes (order of Kilobytes). Streaming data includes a wide variety of data such as log files generated by customers using your mobile or web applications, ecommerce 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.

This data needs to be processed sequentially and incrementally on a record-by-record basis or over sliding time windows, and used for a wide variety of analytics including correlations, aggregations, filtering, and sampling. Information derived from such analysis gives companies visibility into many aspects of their business and customer activity such as –service usage (for metering/billing), server activity, website clicks, and geo-location of devices, people, and physical goods –and enables them to respond promptly to emerging situations. For example, businesses can track changes in public sentiment on their brands and products by continuously analyzing social media streams, and respond in a timely fashion as the necessity arises.

Streaming data processing is beneficial in most scenarios where new, dynamic data is generated on a continual basis. It applies to most of the industry segments and big data use cases. Companies generally begin with simple applications such as collecting system logs and rudimentary processing like rolling min-max computations. Then, these applications evolve to more sophisticated near-real-time processing. Initially, applications may process data streams to produce simple reports, and perform simple actions in response, such as emitting alarms when key measures exceed certain thresholds. Eventually, those applications perform more sophisticated forms of data analysis, like applying machine learning algorithms, and extract deeper insights from the data. Over time, complex, stream and event processing algorithms, like decaying time windows to find the most recent popular movies, are applied, further enriching the insights.

• Sensors in transportation vehicles, industrial equipment, and farm machinery send data to a streaming application. The application monitors performance, detects any potential defects in advance, and places a spare part order automatically preventing equipment down time.
• A financial institution tracks changes in the stock market in real time, computes value-at-risk, and automatically rebalances portfolios based on stock price movements.
• A real-estate website tracks a subset of data from consumers’ mobile devices and makes real-time property recommendations of properties to visit based on their geo-location.
• A solar power company has to maintain power throughput for its customers, or pay penalties. It implemented a streaming data application that monitors of all of panels in the field, and schedules service in real time, thereby minimizing the periods of low throughput from each panel and the associated penalty payouts.
• A media publisher streams billions of clickstream records from its online properties, aggregates and enriches the data with demographic information about users, and optimizes content placement on its site, delivering relevancy and better experience to its audience.
• An online gaming company collects streaming data about player-game interactions, and feeds the data into its gaming platform. It then analyzes the data in real-time, offers incentives and dynamic experiences to engage its players.
  

Before dealing with streaming data, it is worth comparing and contrasting stream processing and batch processing. Batch processing can be used to compute arbitrary queries over different sets of data. It usually computes results that are derived from all the data it encompasses, and enables deep analysis of big data sets. MapReduce-based systems, like Amazon EMR, are examples of platforms that support batch jobs. In contrast, stream processing requires ingesting a sequence of data, and incrementally updating metrics, reports, and summary statistics in response to each arriving data record. It is better suited for real-time monitoring and response functions.

Many organizations are building a hybrid model by combining the two approaches, and maintain a real-time layer and a batch layer. Data is first processed by a streaming data platform such as Amazon Kinesis to extract real-time insights, and then persisted into a store like S3, where it can be transformed and loaded for a variety of batch processing use cases.

Amazon Web Services (AWS) provides a number options to work with streaming data. You can take advantage of the managed streaming data services offered by Amazon Kinesis, or deploy and manage your own streaming data solution in the cloud on Amazon EC2.

Amazon Kinesis is a platform for streaming data on AWS, offering powerful services to make it easy to load and analyze streaming data, and also enables you to build custom streaming data applications for specialized needs. It offers three services: Amazon Kinesis Data Firehose, Amazon Kinesis Data Streams, and Amazon Managed Streaming for Apache Kafka (Amazon MSK).

In addition, you can run other streaming data platforms such as – Apache Flume, Apache Spark Streaming, and Apache Storm – on Amazon EC2 and Amazon EMR.

• Find out more about Amazon MSK
• Find out more about Amazon Kinesis
• Learn about AWS big data services
• Check out the Apache Spark on Amazon EMR page
• Convert your streaming data into insights with just a few clicks using Amazon Kinesis Data Firehose and Amazon Redshift