By Anthony Loss, Director of Solution Strategy, ClearScale
Organizations everywhere are jumping headfirst into GenerativeAI (GenAI). It’s understandable why – being able to create high-quality text, images, software code, and more with natural text prompts is a game-changer. Teams can boost productivity and cut costs overnight with GenAI. That’s why the adoption of tools like ChatGPT and DALL-E has exploded.
What many don’t realize, however, is that it’s hard to be successful with GenAI in isolation. Leaders can’t simply tell employees to pick a tool and run with it. At least not if they want to measure progress and understand what role GenAI should play in the business over the long run.
It’s important to remember that GenAI is a form of artificial intelligence/machine learning (AI/ML) technology, which falls under the realm of data science. In other words, the same capabilities that are important for data science are important for GenAI. But we tend to overlook this fact because GenAI technology is so user-friendly on the surface.
To leverage GenAI successfully, organizations need two things:
2) Sophisticated data engineering
Ideally, both of these happen in a cloud computing environment. The cloud empowers organizations to manage massive volumes of data at scale without having to deal with the underlying infrastructure. When you take infrastructure out of the equation, data teams can experiment, innovate, and automate much more easily. This has incredible implications for data engineering.
Data engineering refers to the work that happens on data to prepare it for complex data science after it’s been ingested and consolidated on the cloud. Data engineering often involves heavy data processing, ETL, analytics, and visualization. An effective data engineering practice sets the stage for data scientists and AI/ML engineers to be able to apply statistics, linear algebra, and other disciplines that are important for ML model development.
For those with limited data engineering or cloud experience, figuring out the right combination of resources can be hard. Fortunately, AWS has an impressive suite of plug-and-play data engineering solutions. We’ll highlight several of the most popular in the next section.
AWS Data Engineering Tools and Services
One of the biggest parts of data engineering is data processing. Data processing is how we turn raw, unintelligible, or incomplete information into usable data. It’s common for data to have to go through some sort of processing after it’s ingested and stored.
Services like AWS Lambda and AWS Step Functions make it easy to process data in a serverless, sequential, and automated fashion. AWS Lambda is especially useful for processing real-time, streaming data. For batch data, AWS Glue is a serverless ETL solution that enables users to create custom ETL workflows that can process data from multiple sources.
When it comes to data analysis, services like Amazon Redshift enable AWS users to analyze big data volumes in a petabyte-scale warehousing environment. Amazon Athena is perfect for using SQL to analyze data directly in Amazon S3 and other data stores. Amazon Kinesis Data Analytics is built for analyzing data in real time after it’s been ingested by Kinesis Data Streams.
For those who want to explore their data visually, Amazon QuickSight is a powerful business intelligence tool for creating visualizations and discovering insights using natural language inputs and embedded analytics. Amazon QuickSight allows users to discover trends and patterns that would otherwise go unnoticed. It also enables data science and AI/ML engineers to make informed decisions on how they pursue more difficult research projects.
In addition to these processing, analytics, and visualization tools, AWS offers many others that give flexibility to implement data engineering practices that align with unique business requirements. The hard part is deciding exactly what to use and how to use it. Especially when considering downstream GenAI use cases.
How Can ClearScale Help with Data Engineering?
ClearScale is an AWS Premier Consulting Partner with deep data engineering experience. We’ve helped organizations in countless industries set up reliable and efficient data engineering workloads on AWS. We’ve earned 11 AWS competencies, including the Data & Analytics and Machine Learning competencies, and our individual experts have over 100 technical certifications.
What does this mean?
We know how to design data engineering solutions on the cloud that generate tangible results for our clients. For example, we worked with an asset management service company that wanted to revamp its data management ecosystem. We implemented data architecture, including Amazon RDS and a data lake, as well as data engineering tools like Amazon Redshift and AWS Glue. With our help, the client gained an automated, accurate, and efficient data engineering practice.
In another project, we helped migrate a marketing technology company to AWS and modernize key data infrastructure along the way. A big part of our work involved using services like Amazon EMR, Amazon Athena, and Amazon Redshift to add advanced data engineering around scalable data architecture. Our work helped open up new doors of analytical and revenue generation possibilities for the client.
So, what data engineering challenges are you facing today? What do you need on the data engineering front to unlock your GenAI practice?
We’d love to help you think through these questions or a bigger data engineering project. And if you need support on the data architecture side, we can also tackle that work first to establish a firm foundation for data engineering.
To learn more, download our eBook A Quick Start Guide to Data Readiness for Generative AI on AWS.