AWS Big Data Blog
Category: Artificial Intelligence
How Chime Financial uses AWS to build a serverless stream analytics platform and defeat fraudsters
This is a guest post by Khandu Shinde, Staff Software Engineer and Edward Paget, Senior Software Engineering at Chime Financial. Chime is a financial technology company founded on the premise that basic banking services should be helpful, easy, and free. Chime partners with national banks to design member first financial products. This creates a more […]
Explore real-world use cases for Amazon CodeWhisperer powered by AWS Glue Studio notebooks
Many customers are interested in boosting productivity in their software development lifecycle by using generative AI. Recently, AWS announced the general availability of Amazon CodeWhisperer, an AI coding companion that uses foundational models under the hood to improve software developer productivity. With Amazon CodeWhisperer, you can quickly accept the top suggestion, view more suggestions, or […]
Perform time series forecasting using Amazon Redshift ML and Amazon Forecast
Amazon Redshift is a fully managed, petabyte-scale data warehouse service in the cloud. Tens of thousands of customers use Amazon Redshift to process exabytes of data every day to power their analytics workloads. Many businesses use different software tools to analyze historical data and past patterns to forecast future demand and trends to make more […]
Build data integration jobs with AI companion on AWS Glue Studio notebook powered by Amazon CodeWhisperer
Data is essential for businesses to make informed decisions, improve operations, and innovate. Integrating data from different sources can be a complex and time-consuming process. AWS offers AWS Glue to help you integrate your data from multiple sources on serverless infrastructure for analysis, machine learning (ML), and application development. AWS Glue provides different authoring experiences […]
Harmonize data using AWS Glue and AWS Lake Formation FindMatches ML to build a customer 360 view
In today’s digital world, data is generated by a large number of disparate sources and growing at an exponential rate. Companies are faced with the daunting task of ingesting all this data, cleansing it, and using it to provide outstanding customer experience. Typically, companies ingest data from multiple sources into their data lake to derive […]
Amazon OpenSearch Service’s vector database capabilities explained
Using Amazon OpenSearch Service’s vector database capabilities, you can implement semantic search, Retrieval Augmented Generation (RAG) with LLMs, recommendation engines, and search in rich media. Learn how.
Real-time time series anomaly detection for streaming applications on Amazon Managed Service for Apache Flink
August 30, 2023: Amazon Kinesis Data Analytics has been renamed to Amazon Managed Service for Apache Flink. Read the announcement in the AWS News Blog and learn more. Detecting anomalies in real time from high-throughput streams is key for informing on timely decisions in order to adapt and respond to unexpected scenarios. Stream processing frameworks […]
How Encored Technologies built serverless event-driven data pipelines with AWS
This post is a guest post co-written with SeonJeong Lee, JaeRyun Yim, and HyeonSeok Yang from Encored Technologies. Encored Technologies (Encored) is an energy IT company in Korea that helps their customers generate higher revenue and reduce operational costs in renewable energy industries by providing various AI-based solutions. Encored develops machine learning (ML) applications predicting […]
Automate discovery of data relationships using ML and Amazon Neptune graph technology
Data mesh is a new approach to data management. Companies across industries are using a data mesh to decentralize data management to improve data agility and get value from data. However, when a data producer shares data products on a data mesh self-serve web portal, it’s neither intuitive nor easy for a data consumer to […]
Build a semantic search engine for tabular columns with Transformers and Amazon OpenSearch Service
Finding similar columns in a data lake has important applications in data cleaning and annotation, schema matching, data discovery, and analytics across multiple data sources. The inability to accurately find and analyze data from disparate sources represents a potential efficiency killer for everyone from data scientists, medical researchers, academics, to financial and government analysts. Conventional […]