Remove category presentations
article thumbnail

Incremental refresh for Amazon Redshift materialized views on data lake tables

AWS Big Data

select mv_name, status, start_time, end_time from SYS_MV_REFRESH_HISTORY where mv_name='customer_mv' order by start_time DESC; Retrieve the current number of rows present in the materialized view customer_mv. It should now have one record as present in the customer.tbl.2 category"; Create a materialized view using the external schema.

article thumbnail

Agentic AI: 6 promising use cases for business

CIO Business Intelligence

The idea that presents itself is having this kind of catalog of the actions that can be done, and having an AI that is intelligent enough,” he says. For example, a bank customer will be able to say, “Take money from my account that has the most money in it and move it to my checking account.”

Insiders

Sign Up for our Newsletter

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

article thumbnail

How to talk to your board about tech debt

CIO Business Intelligence

In our recent report examining technical debt in the age of generative AI , we explored how companies need to break their technical debt down into four categories. Breaking it down into these categories also shows the impact on the business in a way that every board member will understand.

ROI
article thumbnail

Build an analytics pipeline that is resilient to Avro schema changes using Amazon Athena

AWS Big Data

However, managing schema evolution at scale presents significant challenges. This new iteration adds a text-based classification capability through a 'category' field (string type) to the sentiment structure. Building on the schema evolution example, we now introduce a third enhancement to the sensor data structure.

IoT
article thumbnail

Webinar: A Guide to the Six Types of Data Quality Dashboards

DataKitchen

Dimension-Focused Dashboards Bergh described these dashboards as providing a broad overview of data quality across standard categories, facilitating consistent evaluations. Each type serves a unique role in driving changes in data quality. By focusing on their needs, data consumers can become allies in driving data quality improvements.

article thumbnail

Unifying metadata governance across Amazon SageMaker and Collibra

AWS Big Data

The AWS solution we present here, in addition to these data assets, will import AWS projects and will link them to the assets ingested here that are published in these projects. Some business terms are also linked to data categories that are associated with data privacy, regulatory policies, and standards.

article thumbnail

Cost Optimized Vector Database: Introduction to Amazon OpenSearch Service quantization techniques

AWS Big Data

Binary quantization Byte quantization FP16 quantization Product quantization These techniques fall within the broader category of scalar and product quantization that we discussed earlier. This value presents a critical trade-off between accuracy and search efficiency.