Blog Author
Swetha
  • Sep 22 2022
  • Technology
Choosing the AWS analytics service for your business

Big data solutions allow for more efficient data analysis. These insights can assist organizations in gaining a competitive advantage by leveraging data. Data management tools have progressed beyond traditional data warehouses to complex architectures capable of handling sophisticated requirements such as batch and real-time processing, as well as unstructured data and high-velocity transactions
AWS offers the most comprehensive set of analytics services to meet all of your data analytics requirements, allowing organizations of all sizes and industries to reinvent their businesses with data. AWS offers purpose-built services that provide the best price-performance, scalability, and lowest cost for data movement, data storage, data lakes, big data analytics, log analytics, streaming analytics, business intelligence, and machine learning (ML) to anything in between

Amazon Kinesis

Amazon Kinesis simplifies the collection, processing, and analysis of real-time, streaming data, allowing you to gain timely insights and respond quickly to new information. Amazon Kinesis provides critical capabilities for cost-effectively processing streaming data at any scale, as well as the flexibility to select the tools that best meet the needs of your application. Real-time data such as video, audio, application logs, website clickstreams, and IoT telemetry data can be ingested into Amazon Kinesis for machine learning, analytics, and other applications. Instead of having to wait until all of your data is collected before processing can begin, Amazon Kinesis allows you to process and analyze data as it arrives and respond instantly

AWS Data Lake

A data lake is a new and growing method of storing and analyzing data because it enables businesses to manage multiple data types from various sources and store this data, structured and unstructured, in a centralized repository.
Many of the building blocks needed to help customers implement a secure, flexible, and cost-effective data lake are available on the AWS Cloud. These include AWS managed services for ingesting, storing, finding, processing, and analyzing structured and unstructured data. AWS provides Data Lake on AWS, which deploys a highly available, cost-effective data lake architecture on the AWS Cloud along with a user-friendly console for searching and requesting datasets, to help our customers build data lakes.

Amazon Redshift

Create a secure, flexible data strategy and architecture to enable users across the organization to tap into diverse data and power business insights using real-time analytics and machine learning (ML). Utilize data to its full potential, from data ingesting, storage, and access to data analysis, visualization, and prediction in all forms. All of this can be accomplished without the need for specialized data warehouse management experience.

AWS Glue Data brew

AWS Glue DataBrew is a new visual data preparation tool that enables data analysts and data scientists to easily clean and normalize data in order to prepare it for analytics and machine learning. To automate data preparation tasks, you can choose from over 250 pre-built transformations, all without having to write any code. Filtering anomalies, converting data to standard formats, correcting invalid values, and other tasks can be automated. When your data is ready, you can use it right away for analytics and machine learning projects. There is no upfront commitment and you only pay for what you use.

AWS Glue Data brew

AWS Glue DataBrew is a new visual data preparation tool that enables data analysts and data scientists to easily clean and normalize data in order to prepare it for analytics and machine learning. To automate data preparation tasks, you can choose from over 250 pre-built transformations, all without having to write any code. Filtering anomalies, converting data to standard formats, correcting invalid values, and other tasks can be automated. When your data is ready, you can use it right away for analytics and machine learning projects. There is no upfront commitment and you only pay for what you use.

AWS Quick Sight

Quick Sight is a powerful and cost-effective BI service with a robust feature set that includes self-service BI capabilities and interactive dashboards. Quick Sight have the ability to embed analytics deep within Healthcare Data Platform. Quick Sight helps to integrate dashboards directly into your business platform using the embedded analytics software in this solution.
Quick Sight is the fastest BI solution that can create a new dataset, assign permissions to it, create an analysis, and publish and share it in a report so quickly. The solution also improves developer's overall experience. Quick Sight performs extremely well for rapid development and deployment at scale at a very competitive price. Quick Sight is a serverless solution which helps in avoiding a lot of infrastructure, maintenance, and licensing costs

AWS Athena

Amazon Athena is a query service that allows you to easily analyze data in Amazon S3 using standard SQL. Because Athena is serverless, there is no infrastructure to manage, and you only pay for the queries you run. Athena is simple to use: point to your data in Amazon S3, define the schema, and begin querying using standard SQL. Amazon Athena is best suited for quick, ad-hoc querying and integrates with Amazon QuickSight for easy visualization, it can also handle complex analysis, such as large joins, window functions, and arrays.

AWS EMR

Amazon EMR is a web service that allows businesses, researchers, data analysts, and developers to process massive amounts of data in a simple and cost-effective manner. EMR makes use of a hosted Hadoop framework that runs on Amazon EC2 and Amazon S3. Hadoop framework for managing massive amounts of data. Apache Spark, HBase, Presto, and Flink are also supported. Typically used for log analysis, financial analysis, or extract, translate, and load (ETL) tasks.

How to Select the best service that suits your business?

Each use case usually has its own set of characteristics. To ensure that you correctly assess the situation, ask the following questions:
• Do you require analysis results in seconds, minutes, or hours?
• What are you hoping to gain from the analysis?
• What is your financial situation? What is the data's scope growth rate?
• How is the data's schema and structure?
• What data consumption and production integration capabilities are available?
• How much latency is required between data producers and data consumers?
• What level of durability and availability is required, and how much does downtime cost?
• Do you need consistent or flexible analysis workloads?

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