How to Solve Modern Data Challenges on AWS

We create a massive amount of data every day, and according to IBM, the vast majority of it is unstructured. Fortunately, modern data and analytics tools enable us to create value from all of the information we now have at our fingertips.

Businesses are becoming aware of how modern data tools can deliver higher-quality offerings, cut costs, and identify new growth opportunities. Netflix, a leading proponent of data analytics, claims its personalized recommendation engine is worth $1B per year!

Success stories of this magnitude are promoting the rapid growth of Big Data solutions, which is a way to manage data that is so large or complex that it’s difficult to process using traditional analytical methods. Statista reports the Big Data market will reach $103B by 2027, spurred on by the widespread need for sophisticated data management practices.

At ClearScale, we understand how to help organizations leverage Big Data using cloud-native technologies. In this blog, we discuss the main challenges of conventional data analytics methods and how Amazon Web Services (AWS) is the ideal long-term partner for those seeking a powerful cloud solution for their data and analytics needs.

The Four Primary Modern Data Challenges

Today, there are four key data challenges that modern enterprises face:

• Dissimilar data sources
• Lack of scalability
• Inefficient data warehousing
• Overwhelming security needs

Many organizations use a combination of dissimilar relational databases (e.g., MSSQL, MySQL, and PostgreSQL), NoSQL databases (e.g., DynamoDB and MongoDB), or data streams. The problem with this is that it is difficult to query or relate similar data. Companies can’t efficiently process information across dissimilar data sources to make informed business decisions.

Legacy data management practices are also difficult to scale cost-effectively. Businesses have to accurately forecast data growth to purchase the appropriate amount of storage. They also have to ensure pipelines and analytical tools can easily handle the volume of information they collect.

With on-premises data warehouses, organizations often find themselves paying for licensing, support, and hardware that they don’t need. Smaller companies can struggle to justify investing in such data infrastructure when they have to spend so much money upfront.

Costs can also escalate quickly, depending on the storage classes used, ancillary technologies, duplicative records, and other variables. Leaders can easily get trapped in vendor relationships that force companies to invest in less-than-ideal data solutions.

Finally, robust data security is becoming increasingly complex to implement. Between setting up access controls, logging audits, and enforcing other security policies, SecOps teams have to manage a lot, especially as data volumes increase.

Fortunately, cloud-native tools, like those offered by AWS, alleviate these modern data challenges. That makes Big Data capabilities attainable for businesses of all sizes.

Solving Data & Analytics Problems with AWS

With cloud-native Big Data tools, organizations can create significant value. They can implement real-time monitoring, create tailored customer experiences, deploy predictive analytics, and much more. AWS offers tools that are specifically designed to address the challenges described above.

AWS offers a suite of artificial intelligence, machine learning, and business intelligence (BI) applications so that businesses can unleash the full potential of their data.

AWS Tools

Using Amazon SageMaker, companies can host their own machine learning tools in the cloud. Through AWS’s pre-built AI programs, such as Amazon Personalize, organizations can deploy tailored recommendation engines, forecast sales, build chatbots, identity fraudulent online activity, and more.

AWS also offers BI tools, like Amazon QuickSight, which make it easy for users to create interactive dashboards and analyze their data in compelling visualizations. With a tool like Amazon Athena, companies can create ad-hoc dashboards without establishing relations between different datasets.

Amazon EMR is a distributed computing framework that enables users to quickly process and store very large volumes of data. Amazon Redshift is a petabyte-scale data warehouse service that can be used to run queries on structured and semi-structured data. Query results can be used to populate reports and dashboards in cloud-based business intelligence tools.

Using Amazon S3, AWS’s robust object storage service, companies can consolidate their unstructured data in a highly available and durable platform. Amazon S3 solves the dissimilar data sources problem by eliminating the need to maintain different types of databases for various use cases. It is also highly scalable, enabling users to store any volume of data.

AWS Glue is a fully managed ETL service that makes it easy to prepare and load unprocessed data for analytics. At ClearScale, we use AWS Glue to streamline analytical processes for clients.

On the security front, AWS users have access to AWS Lake Formation, a cloud-native service that automates many of the tasks needed to set up secure data lakes. Organizations can use AWS Lake Formation as a centralized, safe repository for data in any form. SecOps teams can also define data sources and enforce security policies directly within the services.

In the AWS cloud, users control costs by only paying for what they use. AWS makes selecting storage classes, removing duplicate records, and tiering easy to execute. With services such as AWS Cost Explorer and AWS Budgets, finance leaders can monitor how much they are spending on data management.

These are just some examples of the many software applications, tools, and services that come with AWS.

Unlocking the Value of Your Data

At ClearScale, we help companies achieve their Big Data goals through end-to-end cloud projects. As an AWS Premier Consulting Partner, we also have access to funding opportunities to relieve some of the cost burdens of migrating legacy data practices and infrastructure to the cloud.

We recently worked with a biotech company, microTERRA, to automate its existing manual data procedures across an IoT footprint. To learn how we implemented automated device management capabilities, sophisticated BI dashboards, and real-time analysis, read the case study here.

We also built a powerful recommendation engine for a leading Canadian news publication, The Globe and Mail. Now, the organization has a cloud infrastructure that auto-scales with demand. And it delivers tailored content to readers based on their past behaviors. Read the case study here.

If you are interested in learning more about how you can overcome modern data challenges in the cloud, we have a webinar – Solving Modern Data Challenges on AWS and Best Practices – that goes into further detail. We take a deep dive into how you can make the most of Big Data using the power of cloud technologies.