Understanding Amazon Web Services’ Cloud Architecture

Transitioning data to cloud platforms is becoming a necessity for organizations of all sizes and industries. But for those just beginning their modern analytics journey, navigating the cloud platform marketplace can be difficult. Having a working knowledge of one of the leading cloud platform providers can streamline these efforts and get you to where you want to be.

In this demo, we’ll dive into Amazon Web Services (AWS) to discover the powerful tools that can be used to address your data analytics needs. We’ll walk you through a sample cloud data pipeline, beginning with the extraction of data from an on-premises SQL server database and loading it into a pair of cloud data warehouse solutions, Amazon Redshift and Snowflake, where it can be leveraged by data visualization tools.

By applying lessons learned from this demo, you’ll be able to:

  • Solidify a reference architecture that can be used across all cloud engagements
  • Take traditional data and analytics skills and translating it to the cloud
  • Raise awareness and engagement in the cloud analytics space
  • Train and equip your team as you move to the cloud

 

Exploring AWS Cloud Architecture

Google Cloud Platform Cloud Architecture

Amazon Web Services is one of the three largest cloud platforms, along with Microsoft Azure and Google Cloud Platform. Want to see how AWS cloud architecture compares to Google Cloud Platform?

 

Amazon Web Services Cloud Data Pipeline

The graphic above represents a sample AWS cloud data pipeline through which data is processed and moved between different storage and analytics services at specified intervals. Let’s take a closer look at the steps through which the data flows through this pipeline:

  1. Source Data – The data begins its journey from a relational database management system (RDBMS), or in this case, the on-premises SQL server database.
  2. Data Orchestration – We then leverage a cloud-native ETL/ELT tool called Matillion to move the data from the RDBMS into a data lake.
  3. Data Staging (in a Data Lake) – The data is then staged in a data lake (or landing zone). In this case, the data is staged in Amazon S3, an object-based storage solution similar in concept to a traditional file system where data can be stored as flat files.
  4. Data Orchestration – Once loaded into the data lake, Matillion moves the data into a cloud data warehouse (CDW).
  5. Cloud Data Warehouse (Option 1) – The data is stored in Amazon Redshift, which is Amazon’s native cloud source for all reporting and analytics activities.
  6. Cloud Data Warehouse (Option 2) – The data is stored in Snowflake, an alternative to RedShift born in the cloud to serve as a singular source for data reporting and analytics.
  7. Data Visualization – Now that your data is transformed, it can then be consumed by any number of visualization tools, including LookerPower BIQuickSight, and Tableau.

 

Advantages of Amazon Web Services

AWS is one of the leading cloud platforms in the market for next-gen data and analytics that provides access to several tools to help you on your modern analytics journey, including:

  • Amazon S3 – a market leader in data lake technology and data archival strategy
  • Amazon QuickSight – a fully managed BI service offering, provides a fast, cloud-powered, and cost-effective alternative to other industry leaders, such as Tableau or Power BI
  • Amazon Redshift – a data warehousing product that continues to capture market share given its easy setup and configuration, as well as its rapidly improving speed and service features

In addition, AWS is considered a leader in developer tools, including robust SDKs, APIs, and security models. It also supports industry-leading, cloud-native ETL/ELT tools, such as Matillion, that allow seamless integration into all aspects of your data while introducing new levels of simplicity, speed, scale, and savings to your business solutions.

 

How to Select a Cloud Platform

Having your data in the cloud allows your organization to move toward more modern, self-service analytics and make data-driven decisions. However, the modern analytics landscape can be confusing, and many people are still trying to figure out where to start. For those in the beginning stages, it’s essential to understand the differences between legacy and modern analytics, the advantages of cloud architectures and other available platforms, and the next steps you should pursue. To learn more about preparing for a cloud analytics transformation, download our eBook, Modern Analytics: Reaching Beyond the Clouds.

When exploring different cloud platforms, it’s important to perform a thorough side-by-side comparison of other providers that allows you to measure against organizational criteria to better understand what options are available, what they offer, and how they can best suit your company’s needs.

Even with a thorough comparison and review of your organization’s guidelines, it can be difficult to determine which cloud analytics platform will serve you best. If you need guidance or recommendations, our team of experts can help you develop and deploy a modern cloud analytics plan that will work best for your company. When you’re ready, contact us, and we’ll get you on your way.

 

 

 

 

 

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