What is streaming data AWS?
Writer : Mr. George Miguel
Streaming data is a type of data that is constantly being updated. When a large number of sources are generating data at the same time in a small amount of time, it's known as streaming data (order of Kilobytes). There are many types of streaming data, including log files generated by customers using your mobile or web applications, ecommerce purchases and in-game player activity as well as information from social networks, financial trading floors and geospatial services.
In order to perform a wide range of analytics, such as correlations, aggregations, filtering, and sampling, this data must be processed record by record and incrementally over sliding time windows. Using this type of analysis, companies can see a wide range of information about their business and customers, including service usage (for billing/metering), server activity, website clicks, and the geographic location of devices, people, and physical goods. By constantly monitoring social media streams, businesses can keep tabs on public opinion of their brands and products, and respond quickly when necessary.
Streaming data has numerous advantages.
In most situations where new, dynamic data is generated on a regular basis, streaming data processing is advantageous. Most industries and big data use cases can benefit from this approach.. The majority of companies begin with simple applications like collecting system logs and rudimentary processing, such as rolling min-max computations. Advanced near-real-time processing is the next step in the evolution of these applications First, simple reports and simple actions like emitting alarms when key measures exceed certain thresholds may be generated by applications. Machine learning algorithms will eventually be used to perform more complex data analysis, such as extracting deeper insights from the data. More complex algorithms like decaying time windows to find the most recent popular movies are applied over time, which further enriches the insights.
Streaming data examples
- The data from sensors in vehicles, machinery, and farm equipment is streamed to a streaming application. In order to avoid equipment downtime, the application keeps an eye on performance, looks for any potential problems ahead of time, and automatically orders replacement parts.
- Real-time stock market monitoring, risk assessment, and portfolio rebalancing are all functions of a financial institution.
- It is possible for real-time property recommendations to be generated by tracking a subset of mobile device data collected by a real-estate website.
- If it doesn't, a solar power company will have to pay penalties for failing to keep up with demand. All panels in the field are monitored and service is scheduled in real time via a streaming data application, which reduces the amount of time each panel experiences low throughput and consequent penalty payouts.
- Data from millions of clickstream records is streamed by a media publisher and enriched and aggregated to deliver a better user experience by incorporating demographic information about the audience.
- Data about player-game interactions is streamed into a gaming platform by an online gaming company. Real-time analysis and dynamic experiences are offered to keep players engaged.
Stream processing vs. batch processing comparison
We should first compare and contrast the stream processing and batch processing methods before dealing with streaming data. An arbitrary query can be computed using batch processing, which can be applied to any number of datasets. It is capable of performing in-depth analyses on large datasets by calculating results based on the input data it contains. Batch jobs can be executed on systems based on MapReduce, such as Amazon EMR. A stream processor on the other hand updates metrics, reports, and summary statistics incrementally as new data records arrive. Monitoring and response functions can be performed immediately.
Use of the dataset's entire contents in a set of queries or other processing.
Querying or analyzing data over a period of time, or only on the most recent data record.
Large batches of data.
A single record or a few records in a small batch.
Latencies in minutes to hours.
Latency of seconds or milliseconds is required.
Rolling metrics and simple response functions.
As a result, many companies are implementing a hybrid model that incorporates both approaches. For batch processing, the data is first processed by a streaming platform like Amazon Kinesis, which extracts real-time insights, and then stored in a store like S3.
Streaming data has its own set of challenges.
There are two layers required for streaming data processing: a storage layer, and a processing layer. For large data streams to be read and written quickly, cheaply, and repeatedly, the storage layer must support record ordering and strong consistency. Processed data from storage is consumed by this layer before it is processed and sent back to the storage layer to be deleted when it is no longer needed. Scalability, data durability, and fault tolerance must also be taken into consideration in both storage and processing layers. Amazon Kinesis Data Firehose, Amazon Kinesis Data Streams, and Amazon Managed Streaming for APAC are some of the platforms that have emerged to provide the infrastructure needed to build streaming data applications.
Streaming data on Amazon Web Services
AWS offers a variety of ways to work with live streaming data. Streaming data services provided by Amazon Kinesis can be taken advantage of, or you can build and manage your own streaming data solution on Amazon EC2.
As a platform for streaming data on AWS, Amazon Kinesis allows you to load and analyze streaming data, as well as build custom streaming data applications for specific needs. AWS Kinesis Data Firehose, AWS Kinesis Data Streams, and AWS Managed Streaming for Apache Kafka are the three services it provides (Amazon MSK).
Apache Flume, Spark Streaming, and Storm can all be run on EC2 and EMR in addition to other streaming data platforms like Apache Kafka.
Data Streams from Amazon Kinesis
Custom applications that process or analyze streaming data can be built using Amazon Kinesis Data Streams. In a single hour, it's capable of storing terabytes of data from an infinite number of different sources. There are a variety of ways to use Amazon Kinesis Data Streams data, including real-time dashboards, alerting systems and dynamic pricing and advertising systems. Kinesis Client Library (KCL), Apache Storm, and Apache Spark Streaming are all supported by Amazon Kinesis Data Streams.
Kinesis Data Firehose for Amazon
To load streaming data into AWS, Amazon Kinesis Data Firehose is the quickest and most straightforward method currently available. Near real-time analytics can be achieved with the existing business intelligence tools that you already use today, thanks to Amazon S3 and Amazon Redshift streaming data capture capabilities. You can quickly implement an ELT approach, and reap the benefits of streaming data.
Apache Kafka Streaming with Amazon Managed Streaming (Amazon MSK)
It's easy to build and run Apache Kafka-based streaming applications using Amazon MSK, a fully managed service. Streaming data pipelines and applications can be built with Apache Kafka, an open-source platform. Amazon MSK allows you to use Apache Kafka APIs to populate data lakes, stream changes to and from databases, and power machine learning and analytics applications.
Other Amazon EC2 streaming solutions
In order to build your own stream storage and processing layers, you can install streaming data platforms on Amazon EC2 and EMR. By using Amazon EC2 and Amazon EMR to build your streaming data solution, you can avoid the hassle of provisioning infrastructure and gain access to a wide range of streaming storage and processing frameworks. Amazon MSK and Apache Flume are two options for the streaming data storage layer. Apache Spark Streaming and Apache Storm are options for stream processing layers.
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