article thumbnail

Optimize data layout by bucketing with Amazon Athena and AWS Glue to accelerate downstream queries

AWS Big Data

In the era of data, organizations are increasingly using data lakes to store and analyze vast amounts of structured and unstructured data. Data lakes provide a centralized repository for data from various sources, enabling organizations to unlock valuable insights and drive data-driven decision-making.

article thumbnail

Tackling AI’s data challenges with IBM databases on AWS

IBM Big Data Hub

  Redefining cloud database innovation: IBM and AWS In late 2023, IBM and AWS jointly announced the general availability of Amazon relational database service (RDS) for Db2. This service streamlines data management for AI workloads across hybrid cloud environments and facilitates the scaling of Db2 databases on AWS with minimal effort.

Insiders

Sign Up for our Newsletter

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

article thumbnail

Exploring the AI and data capabilities of watsonx

IBM Big Data Hub

Watsonx.data is built on 3 core integrated components: multiple query engines, a catalog that keeps track of metadata, and storage and relational data sources which the query engines directly access. 1 When comparing published 2023 list prices normalized for VPC hours of watsonx.data to several major cloud data warehouse vendors.

article thumbnail

Connect your data for faster decisions with AWS

AWS Big Data

Second, organizations still need transformations like cleansing, deduplication, and combining datasets for analysis and machine learning (ML). For these, AWS Glue provides fast, scalable data transformation. We are thrilled to announce that this zero-ETL integration is now generally available.

article thumbnail

Showpad accelerates data maturity to unlock innovation using Amazon QuickSight

AWS Big Data

Showpad also struggled with data quality issues in terms of consistency, ownership, and insufficient data access across its targeted user base due to a complex BI access process, licensing challenges, and insufficient education. The company also used the opportunity to reimagine its data pipeline and architecture.