Remove 01 how-is-ai-improving-the-data-management-systems
article thumbnail

Accelerate queries on Apache Iceberg tables through AWS Glue auto compaction

AWS Big Data

Data lakes were originally designed to store large volumes of raw, unstructured, or semi-structured data at a low cost, primarily serving big data and analytics use cases. By using features like Icebergs compaction, OTFs streamline maintenance, making it straightforward to manage object and metadata versioning at scale.

article thumbnail

Connect, share, and query where your data sits using Amazon SageMaker Unified Studio

AWS Big Data

The ability for organizations to quickly analyze data across multiple sources is crucial for maintaining a competitive advantage. Traditionally, answering this question would involve multiple data exports, complex extract, transform, and load (ETL) processes, and careful data synchronization across systems.

Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

Real-Real-World Programming with ChatGPT

O'Reilly on Data

If you’re reading this, chances are you’ve played around with using AI tools like ChatGPT or GitHub Copilot to write code for you. So far I’ve read a gazillion blog posts about people’s experiences with these AI coding assistance tools. or “ha look how incompetent it is … it couldn’t even get my simple question right!”

article thumbnail

Talk to Your Graph Client for GraphDB

Ontotext

Introduction Since I became the product manager of GraphDB , I was expected to stop writing code but I couldnt help it. The first version of Talk to Your Graph (or TTYG for short) was released in 2023 and it was my baby. What a human and an AI model can do together is truly impressive. So, lets create a TTYG client together!