Moving Data Analytics to AWS – Challenges and Benefits

 In Accelerite Blog

Organizations make the decision to move their analytics workloads to AWS for multiple reasons, such as gaining access to scalable storage, fully-managed analytics capabilities, better security, and other services that make more financial sense on the cloud. Irrespective of the goal, migrating to AWS is a long process with multiple steps. Organizations first need to move data to AWS in a cost-efficient manner, select the right storage, database and data lake offering from a vast array of options, and they must do so cautiously. Let’s take a look at some of the challenges that companies may face in this journey and the benefits they can reap.


  • Skill Set

The current wave of cloud computing is fuelled by microservices using serverless computing paradigms and containers. The number of professionals available with the necessary DevOps skills to understand these technologies are very few. Not only is it hard to find professionals with these skills, even if organizations find them, meeting their salary demands can be a struggle. The lack of cloud skills is one of the primary reasons why organizations have not made as much headway as they would like on their AWS journey.

  • Complexity

AWS offerings tend to be complicated and are aimed for use by developers. But even for highly-skilled developers, AWS services are hard to navigate in terms of selecting the right tool for the right workload. It is difficult to keep up with the blinding pace at which AWS evolves its services landscape – AWS currently offers over 120 distinct services. A lot of developers also find its web interface to be inscrutable with a lot of trial and error needed to figure out how things work.

  • AWS Cost Management

A lot of AWS users are entangled in a chaotic web of AWS instances and byzantine invoices that constantly cause budget overruns. No one can trace or understand the meaning of every charge on the invoice and everyone fears terminating the service because it might break something within key systems.  A common nightmare is users spinning up instances for demo systems and forgetting to terminate them, bleeding through precious budget dollars.

  • Maintaining Alignment with Business Objectives

When moving to AWS, a key hurdle an organization has to overcome is ensuring that the new AWS applications are in line with business objectives. Executives need to invest time and effort in defining how operations will shape up in AWS. They need to thoroughly assess existing processes that might need modification, explore operational tools that will aid them in AWS, and initiate any level of operational training to reinforce alignment with the overall business strategy.


  • Scalability

Analyzing massive data sets needs substantial compute capacity that can fluctuate in size based on the volume of data and the type of analysis being performed. This means that the AWS pay-as-you-go model is perfectly suited to big data workloads, where capacity can be effortlessly scaled up or down based on needs – without waiting to procure additional hardware. On AWS, the minute you feel the need for increased capacity, you can provision it in a few clicks. If you realize that you do not require so much capacity, you can instantly scale down to the amount you need.

  • Exchange capital expenditure for variable expenditure

Instead of spending heavily on data centers, servers, ETL software, and visualization tools before you understand how and to what extent you will use them, with AWS you only pay when you utilize resources and only pay for the amount you use.

  • Stop struggling with forecasting capacity needs

With AWS, you can put an end to guesstimating your capacity needs, an effort that often proves to be futile. When you make a capacity investment before deploying and using an application, you either wind up with costly idle capacity at your hands or deal with a capacity shortage. With AWS, you can consume as many or as few resources as you need, with provisioning taking only a few minutes. You only pay for what you consume.

  • Improve Agility and Speed

On AWS, new IT resources can be provisioned by teams in a few minutes instead of the waiting period of weeks that is typical of an on-premises setup. This leads to significantly increased agility, as the time and money it takes to experiment and carry out analysis is much lower.

  • Focus on running your business, not infrastructure

With AWS, organizations can spend time and money on initiatives related to their core business instead of exerting their energy on purchasing, powering and maintaining servers, software, and storage.

  • High levels of availability

AWS hosts its resources across numerous Availability Zones globally. Organizations can place their workloads and data in multiple locations, thereby delivering very low latency, high availability and an improved experience to customers.

Traditional analytics applications that run on sophisticated infrastructure offer substantial cost savings when migrated to AWS. However, organizations need to take certain measures to ensure that they see value. For instance, they need visibility into AWS usage to make sure that they don’t hemorrhage their budget by overusing instances or overlooking provisioned instances that are idle. Organizations can achieve this by using a tool like ShareInsights that helps forecast costs for running a workload on AWS. ShareInsights also simplifies the use of AWS analytics offerings by automatically selecting the right AWS service that perfectly suits the workload to be run on AWS. It also provides a drag-and-drop designer that lets business users carry out all analytics operations from data preparation to visualization to machine learning without any programming.

If you are considering moving to AWS, power full speed ahead until you start seeing returns both in terms of increased agility and higher savings.

To learn how ShareInsights can make your move to AWS easier, get in touch with us today.

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