AWS Real-Time Analytics 101: Unleashing the Power of Data

on AWS, AWS Kinesis, Data Analytics, Data Warehouse, ETL, ETL Tutorials, Real-Time Data Analysis • May 12th, 2022 • Write for Hevo

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AWS Real-Time Analytics can do things that were previously impossible — such as detecting and preventing financial fraud and detecting cybercrimes in action. Moreover, the product can deliver personalized offers in real-time to improve campaign performance and identify failures before they occur.

However, until recently, there was no way to ingest and analyze streaming data in real-time for immediate action. Data can provide insights, but only after hours or even days of collection, at which point the data might become irrelevant.

Real-time data generation is increasing exponentially year on year, with data stream types ranging from weather reports to stock quotes and tweets, all of which are constantly changing and updating. In recent months, we’ve seen an increasing number of organizations start on a cloud journey, with many of them beginning with Amazon Web Services. AWS Real-Time Analytics has handled a wide variety of real-time data use cases with varying business requirements.

In this article, we’ll give you some pointers on how to deal with AWS Real-Time Analytics by introducing a real-world scenario. We will walk you through each necessary step to provide you with the necessary understanding of the subject.

Table of Contents

What is Amazon AWS?

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Amazon Web Services, Inc. (AWS) is an Amazon company that provides pay-as-you-go cloud computing platforms and APIs to consumers, organizations, and governments. Through AWS server farms, these cloud computing web services provide distributed computing processing capability and software tools. Amazon Elastic Compute Cloud (EC2) is one of these services, which offers customers a virtual cluster of computers that is always accessible through the Internet.

AWS’s virtual computers emulate the majority of the characteristics of a real computer, including hardware central processing units (CPUs) and graphics processing units (GPUs) for processing; local/RAM; hard-disk/SSD storage; a selection of operating systems; networking; and pre-installed application applications such web servers, databases, and customer relationship management (CRM).

Customers receive AWS services via a global network of AWS server farms. Fees are computed using a mix of consumption (a “Pay-as-you-go” model), hardware, operating system, software, or networking characteristics chosen by the subscriber, as well as availability, redundancy, security, and service choices.

Subscribers can buy a single virtual AWS machine, a dedicated physical computer, or clusters of both. Amazon offers some parts of security to subscribers, while others are the responsibility of the subscriber (e.g. account management, vulnerability scanning, patching). AWS operates in several geographical zones across the world, including six in North America.

Key Features of Amazon AWS

Here are some prominent Amazon AWS features:

  • Mobile Hub via Amazon Web Services:  AWS Mobile Hub guides you to the most relevant and compatible functionality for your app. It comes with a console that gives you access to AWS services including mobile app creation, testing, and monitoring.
  • Cloud Services that do not require a server:  Amazon API and Amazon Gateway help users operate and scale their code. Because AWS administers the entire process, users are not accountable for the servers.
  • Databases: Amazon supplies databases as required, and they entirely oversee the databases they give.
  • Storage: Storage is one of the AWS capabilities provided by Amazon. It is affordable, adaptable, and simple to use. To fulfill your needs, AWS storage may be utilized separately or in combination with other services.
  • Security and compliance: Many organizations rely on AWS because Amazon protects the security of the data they give. Customers may use AWS services to scale and innovate. Customers only pay for the services they utilize at this location.

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Introduction to AWS Real-Time Analytics

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Real-time data is now seen and used everywhere, from social networks to mobile and web applications, IoT devices, data center instrumentation, and a variety of other sources.

As the speed and amount of this type of data increases, so does the demand for real-time data analysis using Machine Learning algorithms to derive a deeper understanding of the data. For example, you might want a continuous monitoring system to detect changes in sentiment in a social media feed so that you can respond in near real-time.

In this post, we will gather and store real-time data using Amazon Kinesis. We then use Amazon Kinesis Data Analytics to process and analyze the real-time data continually.

This change to AWS Real-Time Analytics necessitates the development of a completely new set of tools capable of handling the demands of doing instant analysis on continuous inflows of data. Amazon Kinesis makes it simple to gather, process, and analyze real-time streaming data, allowing you to gain timely insights and respond rapidly to new data.

Amazon Kinesis

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Amazon Kinesis is a real-time large data processing Amazon Web Service (AWS).

AWS Real-Time Analytics can process hundreds of terabytes per hour of streaming data from sources like operations logs, financial transactions, and social media feeds. According to Amazon, Kinesis fills a void left by Hadoop and other technologies that analyze data in batches but do not allow for real-time operational choices based on continually flowing data. This capacity, in turn, simplifies the process of developing apps that rely on real-time data processing.

Amazon Kinesis is compatible with Amazon Redshift, Amazon Dynamo Database, and Amazon Simple Storage Service (Amazon S3), as well as a variety of third-party products. Customers are billed using the normal AWS pay-as-you-go plan, with charges based on the amount of data handled and how the data is bundled. Amazon Kinesis also provides the below functionalities:

1) Amazon Kinesis Data Firehose

Amazon Kinesis Data Firehose is the most reliable means to feed streaming data into data warehouses and analytics tools. It can gather, transform, and load streaming data into Amazon S3, Amazon Redshift, Amazon OpenSearch Service, and Splunk, allowing for near real-time analytics with your existing business intelligence tools and dashboards. It is a fully managed solution that scales automatically to fit your data throughput and requires no ongoing administration. It may also batch, compress, convert, and encrypt data before loading it, reducing storage requirements and boosting security.

From the AWS Management Console, you can quickly create a Firehose delivery stream, configure it with a few clicks, and begin feeding data to the stream from hundreds of thousands of data sources to be loaded continuously to AWS—all in a matter of minutes. You may also set your delivery stream to convert incoming data to columnar formats such as Apache Parquet and Apache ORC before delivering it to Amazon S3 for cost-effective storage and analytics.

What makes Hevo’s ETL Process Best-In-Class

The ideology behind creating a manual Data Pipeline is one that requires a lot of time, effort, and understanding. Automated tools like Hevo can automate this process without writing a single piece of code. Its integration with a wide range of data sources such as SQL Server, MongoDB, DynamoDB along with Business Intelligence Tools like Tableau, Power BI, and much more help to map your data accurately and generate valuable insights from them.

Check out what makes Hevo amazing:

  • Integrations: Hevo’s fault-tolerant Data Pipeline offers you a secure option to unify data from 100+ data sources (including 40+ free sources) and store it in the SQL Server or any other Data Warehouse of your choice. This way you can also focus more on your key business activities and let Hevo take full charge of the Data Transfer process.
  • High-Speed Data Loading: Loading compressed data into SQL Server is slower than loading uncompressed data. Hevo can decompress your data before feeding it to Database. 
  • Quick Setup: Hevo with its automated features, it can be set up in minimal time. Moreover, with its simple and interactive UI feature, it is extremely easy for new customers to work on and perform operations with ease.
  • Scalability: As the number of sources and the volume of your data grows, Hevo scales horizontally, handling millions of records per minute with very little latency.
  • Live Support: The Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.

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2) Amazon Kinesis Data Analytics

Amazon Kinesis Data Analytics is the simplest method to analyze streaming data, gain actionable insights, and respond in real-time to your company and customer demands. Amazon Kinesis Data Analytics simplifies the development, management, and integration of streaming applications with other AWS services.

Using templates and an interactive SQL editor, SQL users may simply query streaming data or construct a full streaming application. Java developers may quickly create sophisticated streaming apps that can modify and analyze data in real-time by leveraging open-source Java libraries and AWS connectors.

Amazon Kinesis Data Analytics handles everything that is needed to constantly perform your queries and scales automatically to meet your incoming data’s volume and the throughput rate.

3) Amazon Kinesis Data Streams

Amazon Kinesis Data Streams is a hugely scalable and long-lasting real-time data streaming solution from Amazon. KDS is capable of continually collecting gigabytes of data per second from hundreds of thousands of sources, including website clickstreams, database event streams, financial transactions, social media feeds, IT logs, and location-tracking events. The collected data will be available in milliseconds, allowing for real-time analytics use cases like real-time dashboards, real-time anomaly detection, dynamic pricing, and more.

4) Amazon Kinesis Video Streams

Amazon Kinesis Video Streams enables the safe transmission of video from connected devices to AWS for analytics, Machine Learning (ML), playback, and other processing. Kinesis Video Streams provisioned and elastically scaled all infrastructure required to ingest streaming video data from millions of devices automatically.

It also securely saves, encrypts, and indexes video data in your streams and provides easy access to your data via APIs. Through integration with Amazon Rekognition Video and libraries for ML frameworks such as Apache MxNet, TensorFlow, and OpenCV, Kinesis Video Streams allows you to playback video for live and on-demand viewing, as well as quickly build applications that take advantage of computer vision and video analytics.

Benefits of AWS Real-Time Analytics

Here are the benefits of implementing AWS Real-Time Analytics:

  • Create a real-time vision and video-enabled apps: AWS Real-Time Analytics helps you to create apps with real-time computer vision capabilities via integration with Amazon Rekognition Video, as well as real-time video analytics capabilities via popular open-source machine learning frameworks.
  • Secure: AWS Real-Time Analytics lets you manage access to your streams with AWS Identity and Access Management (IAM). It protects your data by encrypting it at rest using AWS Key Management Service (KMS) and in transit with the industry-standard Transport Layer Security (TLS) protocol.
  • Long-lasting, Searchable storage: AWS Real-Time Analytics uses Amazon S3 as the underlying data store, so your data is safe and secure. You can rapidly search based on timestamps generated by the device and the service.
  • There is no Infrastructure to manage: AWS Real-Time Analytics handles all of the infrastructures. You don’t have to worry about infrastructure scaling, configuration, software updates, or failures as the number of streams and consuming applications develops.
  • Create ML Streaming Applications: Machine learning (ML) models use AWS Real-Time Analytics to examine data and forecast inference endpoints as streams progress to their destination.

Conclusion

AWS Real-Time Analytics with Amazon Kinesis assists businesses to exploit Data Analytics more. This solution automatically provisioned the services required to gather, process, analyze, and visualize website clickstream data in real time. This solution is intended to provide a framework for analyzing and visualizing metrics, freeing you up to concentrate on adding new indicators rather than managing the underlying infrastructure.

To become more and more efficient in handling your Databases, it is preferable for you to integrate them with a solution that you can carry out Data Integration and Management procedures for you without much ado and that is where Hevo Data, a Cloud-based ETL Tool, comes in. Hevo Data supports 100+ Data Sources and helps you transfer your data from these sources to your Data Warehouses in a matter of minutes, all this, without writing any code!

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Share your experience of learning AWS Real-Time Analytics in the comments section below!

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