Modernizing Data with AWS Tools

Modernizing Data with AWS Tools

If you want to establish a successful business, you must keep up with the latest online technology. We’re here to help with that. To start, we’re going to look at some of the most popular services from AWS (Amazon Web Services). We’ve seen this platform grow massively in popularity and functionality in the past two years, which isn’t surprising given its affordability and many enterprise-level capabilities.

AWS offers incredibly sophisticated services across many industries like computing, networking, database management, storage, etc. If you haven’t heard of it before, AWS is so vast in scope that it creates confusion among most companies entering the marketplace for the first time.

Amazon QuickSight

Amazon QuickSight is a business intelligence and visualization tool that allows you to generate graphics, do ad-hoc analysis, and effectively get business insights from your data. QuickSight enables you to connect to your data sources, visualize the data in various ways, create interactive dashboards, share findings with others across an organization, and embed them securely into applications.

You may use the built-in connections for major AWS services like Amazon Athena, Amazon Aurora, Amazon Redshift, and Amazon S3 to import your own data sets.

Amazon Aurora

Amazon Aurora is a relational database engine that is fully managed and compatible with MySQL and Postgres. It’s designed to integrate seamlessly with other AWS services like RDS, S3, and DynamoDB, allowing users to store data in their preferred database format while also allowing them to switch between storage methods depending on their needs. From a performance and reliability standpoint, Amazon Aurora boasts speed capabilities of up to three times faster than traditional databases and continuous backups and automated failovers.

Furthermore, Amazon asserts that this technology is highly durable since it replicates six copies of your data over three availability zones—to put that in layman’s terms, there are a lot of safeguards in place to ensure your information won’t get lost! One of the best things about using Amazon Aurora is its cost-effectiveness: instead of charging you for each database instance you create, they’ll bill you based on the total amount of storage.

In general, this technology is suitable for any application that would benefit from increased transaction throughput or fault tolerance—a few common use cases include IoT analytics and mobile gaming apps. However, this technology may not be a good fit for applications like real-time video processing or something requiring a high amount of disk I/O operations per second (IOPS).

Amazon Redshift

Amazon Redshift is a speedy, fully managed data warehouse that makes it easy and affordable to analyze all of your data using normal SQL and existing Business Intelligence (BI) tools. It enables you to conduct complicated analytic queries across petabytes of structured data by leveraging advanced query optimization, columnar storage on high-performance local disks, and massively parallel query execution.

Use cases for Amazon Redshift include:

  • Online Transaction Processing (OLTP)
  • Online Analytical Processing (OLAP)
  • Data warehousing
  • Data marts and reporting

EMR

You can use EMR to set up, manage, and scale your Hadoop Cluster on the cloud. It works with a variety of big data engines, including Apache Spark and Hive, and custom applications using Hadoop MapReduce. It can process petabytes of data stored in S3 and serverless, so you don’t have to worry about provisioning or maintaining your infrastructure. You can set up an EMR cluster within minutes from the AWS console and use it for analytics or machine learning.

Amazon SageMaker

If you’re interested in using automated machine learning to modernize your data, Amazon SageMaker is a great place to start. This fully-managed service lets you build, train, and deploy machine learning models easily, quickly, and at scale. You can take advantage of the built-in algorithms and tools that SageMaker offers or bring your scripts.

With native support for getting your Jupyter Notebooks into the AWS console and easy access to popular frameworks like TensorFlow and Apache MXNet, it’s never been easier to prototype proof of concept or operationalize an ML model. The best part? It integrates with other AWS services like the S3 simple storage service, so you can get started right away without worrying about managing yet another data pipeline or infrastructure component. Modernizing your data has never been easier!

It’s not too late to modernize your data.

Don’t let the cost of modernization scare you, especially if you’re on a tight budget. Modernization doesn’t have to happen all at once. Instead, it can be done in phases. If you already use AWS services, get started with the ones covered in this post: QuickSight, Athena, and Glue.

The sooner your organization starts to modernize its data, the better off it will be. Modernizing your data helps your organization access more types of data with ease. Additionally, it allows departments across your organization to analyze that modernized data in a way that’s pertinent to their needs and goals. If you’ve been thinking about how best to modernize your company’s data but haven’t made any changes yet, consider giving QuickSight, Athena, and Glue a try today!

Our Final Thoughts in Modernizing Data with AWS Tools

The AWS platform is ideal for helping companies modernize their data processing without having to worry about managing any infrastructure. It offers a broad range of powerful data-oriented services to suit the needs of companies at every stage of maturity in their business. Should you need help deciding on how much or what type of AWS service you should be using for your project, AWS Consulting experts are available to help you assess your requirements and get started on the right track.

Contact us for additional solutions in modernizing data with AWS tools. Further blogs within this Modernizing Data with AWS Tools category.