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Real-time Data Streaming with Serverless and Caching

Real-time data streaming refers to a process that emphasizes the consumed and produced data, which is stored in the live environment. The scope of the data is derived from a plethora of sources. You can fetch and import the data, after which it remains stored in the system, followed by the execution of different data analysis algorithms.

Real-time data streaming is used on a wide scale to analyze the massive data at a specific point. This process involves the extraction of valuable information for the business enterprise. Real-time data streaming has become the need of the hour as it facilitates massive data volumes faster. In the present day, a wide assortment of data processing platforms is present, which help in data processing from different ingestion platforms. Few of them support Serverless Application processing, whereas few of them support real-time data streaming.

Benefits of real-time data streaming

Real-time data streaming has become the latest buzzword as distributed file systems like S3, Hadoop, and other distributed file systems provide support for data processing in massive volumes. There are a bunch of business enterprises that attempt to collect large amounts of data. Data engineering helps to convey specific data in certain basic formats, following which data analytics turn the data into specific useful results that are useful to the business enterprise in different ways. It offers a helping hand to improve the customer experience, thereby boosting the productivity of the team members.

Reasons to choose real-time data streaming

Now, we will discuss some of the benefits of real-time data streaming:

Data visualization

Here, a set of different historical datasets is placed on the singular screen, which helps to represent the whole point. It is possible to visualize the data streaming so that it is updated to display in real time.

Security

In the case of fraud detection, it is possible to detect the fraud in no time, as soon as it occurs. Hence, it is important to adopt proper safety precautions to restrict the damage.

Business insights

Speaking of business, real-time data analytics makes it beneficial to receive alerts depending on specific, pre-defined parameters. If you experience a drop in sales, it is possible to trigger an alert so that management can know about the serious issues. Real-time data analysis is notable in this aspect as it is useful to business enterprises in beating their competitors, depending on batch processing analysis.

Serverless caching for real-time data streaming

Momento serverless cache is regarded as the elastic service that helps to handle the dynamic, which supports traffic of millions of requests per second. Momento serverless cache integration is effective in highlighting various use cases for the serverless option, such as working as the cache for DynamoDB and different NOSQL databases, such as Cassandra and Mongo.

Momento serverless cache helps to accelerate Amazon Aurora Serverless V2, Google CloudSQL, Amazon DynamoDB, PostgreSQL, Google Cloud Datastore, and different serverless databases. It also helps to enhance the app’s speed, which is created with Google Cloud functions, AWS Lambda, and Google Cloud. It also helps boost performance at a lower cost than Google Cloud Storage and AWS S3.

Data streaming in real time

Real-time refers to the processes and infrastructure that help analyze, process, and capture the data in real time. The specific architecture comprises four primary components, which include data ingestion, data sources, data delivery, and data processing.

Data sources

It refers to different devices and systems that produce the data. It is inclusive of web apps, transactional databases, and social media platforms, to name a few. Such kinds of data sources help to produce massive amounts of data in semi-structured and unstructured formats, owing to which it becomes challenging to analyze and process.

Data ingestion

The specific data ingestion component is responsible for the data collection, data filtration, and data formatting for processing. It is inclusive of different steps, which include data normalization, data validation, and data enrichment. After the data formatting, you can send it to the processing component for future analysis.

Data processing

The data processing component is responsible for data analysis, which helps generate insights in real time. Such components are inclusive of different technologies and tools. In addition, the specific data processing component helps to recognize data patterns, anomalies, and trends, which helps inform different business decisions.

Data Delivery

Data delivery happens to be the final component of data streaming in real time. It helps to generate insights, which are produced by the data processing component, for potential users.

Data streaming for real-time use cases

Predictive maintenance

Data streaming architecture in real time helps to track equipment performance and predict whether maintenance is essential. It helps to detect the problems earlier. Hence, the businesses offer a helping hand in avoiding the expensive downtime, thereby preventing equipment failure.

Financial services

You can also use the data streaming real-time architecture in different financial services applications. Thus, the businesses analyze the market data in real time and recognize different trading opportunities, thereby making different informed investment decisions.

Fraud detection

Data streaming in real time is used on an extensive scale in different fraud detection apps. With transaction data analysis in real time, businesses recognize fraudulent activity, thereby taking the necessary action to prevent losses.

Social media monitoring

Data streaming architecture in real time is used in different social media monitoring apps. Social media data analysis in real time provides a suitable choice for businesses to recognize different sentiments and trends, thereby adjusting different marketing strategies.

Amazon Kinesis happens to be a serverless application, which makes it easier to analyze, process, collect, and stream the data in real time with various capabilities, including Kinesis data analytics, Kinesis data firehose, Kinesis data stream, and Kinesis video streams. Kinesis data streams help to ingest, custom process, and buffer the streaming data. In addition, Kinesis data analytics allow streaming data filtering, processing, and aggregation in real time. Furthermore, Kinesis video streams help stream the video to AWS from different connected devices for machine learning playback, analytics, and other data processing.

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