By Anthony Loss, Director of Solution Strategy, ClearScale
GenerativeAI (GenAI) is at the forefront of innovation for many organizations. Companies can now create high-quality content – text, images, videos, software code, and more – without having to build or maintain their own generative models. In other words, GenAI has enabled companies everywhere to access and leverage the power of AI/ML.
However, GenAI success requires more than smart prompt engineering and effective content distribution. GenAI has to fit under a broader, well-defined data science strategy – one that’s founded on robust data architecture and data engineering principles. What’s more, we believe that data architecture and data engineering should both happen on the cloud to maximize performance. In this blog post, we’re going to focus on the data architecture side of things.
Data architecture refers to the resources and tools used to ingest, store, and move data across cloud environments. Data architecture includes things like real-time data ingestion pipelines that pull information from IoT devices in the field or data lakes that store large volumes of structured and unstructured data. In some cases, data architecture also facilitates basic data exploration.
At the end of the day, data architecture provides the foundation upon which organizations build data engineering and complex data science applications, including those that involve GenAI. Without good data architecture, GenAI is much harder to get right.
Fortunately, AWS offers a vast set of tools and services that make it easy to design the ideal data architecture for advanced use cases. In the next section, we’ll highlight some of the most widely used data architecture solutions available on the platform today.
AWS Data Architecture Tools and Services
AWS addresses the full spectrum of data architecture needs, starting with data ingestion. Data ingestion refers to the process of collecting and consolidating data from external sources. It’s how companies get data from the real world into their data stores and warehouses on the cloud in preparation for big data analytics, ML model training, and other purposes.
Data ingestion can happen in real time or in batches. AWS has answers for both. With Amazon Kinesis Data Firehose, organizations can gather, process, and analyze streaming data at any volume without having to worry about provisioning resources. The solution can also load data directly into services like Amazon S3, Amazon Redshift, and OpenSearch, making it a valuable tool for quickly pulling data into an IT ecosystem and preparing it for downstream workloads.
When it comes to moving data, services like the AWS Database Migration Service, AWS Snow Family, and AWS Storage Gateway all come in handy. The AWS Database Migration Service streamlines homogeneous and heterogeneous data migrations for on-premises to cloud transitions and cloud-to-cloud transitions. The AWS Snow Family is ideal when companies want to move massive volumes of data to the cloud but don’t have a reliable or secure way to move it over networking infrastructure. And AWS Storage Gateway is perfect for enterprises that want to create hybrid setups and share data between their on-premises and cloud environments.
Beyond these services, there are many other ways data teams can move data across AWS architecture. The right combination of components depends on the unique needs and capabilities of the business.
On the data storage front, AWS has high-performing options for object, file, and block storage, as well as a myriad of database types. As mentioned previously, Amazon S3 is an object storage service that is ideal for storing large files and hosting static websites. Amazon EFS and Amazon FSx are both file storage services that enable teams to share storage across multiple server instances. For those who want to store their data in databases, AWS has relational, NoSQL, and purpose-built database engines that correspond to highly specific use cases.
To summarize, AWS has everything modern enterprises need to set up scalable, durable, and efficient data architectures. Teams can ingest data at any volume from anywhere in the world into their cloud environments. They can then store it cost-effectively and move it around securely in preparation for data engineering.
How Can ClearScale Help with Data Architecture?
ClearScale is an AWS Premier Consulting Partner with more than a decade of experience on the AWS platform. Our team of cloud engineers and solutions architects has earned 11 AWS competencies, including the Data and Analytics and Machine Learning competencies. We’ve worked with organizations in all industries to set up data architecture for modern applications.
For example, we worked with a provider of natural gas measurement products and services to upgrade its data management infrastructure. Our cloud engineers built a data pipeline MVP that involved using Amazon Kinesis Data Firehose, AWS Lambda functions, Amazon DynamoDB, and Amazon S3. By the end of the project, our client had an automated IoT solution that could ingest data at scale and make that information available to end users after some processing.
In another project, we worked with USA Baseball to set up a data lake on AWS and configure multiple data pipelines to ingest data into the data lake. The goal of the project was to provide USA Baseball with a better way to manage data and extract deeper insights. As a result of our work, the organization now has data architecture that is scalable, secure, and capable of supporting advanced analytics.
So, what data architecture issues are you experiencing today? What data engineering and GenAI use cases do you want to unlock?
We’re here to help you think through every part of your data science strategy to help you lay the foundation of a generative AI program. And we can take care of project execution so that you can focus on delivering great products and services.
To learn more, download our eBook A Quick Start Guide to Data Readiness for Generative AI on AWS.