article thumbnail

Large Language Models and Data Management

Ontotext

I did some research because I wanted to create a basic framework on the intersection between large language models (LLM) and data management. But there are also a host of other issues (and cautions) to take into consideration. Cleaning, refining, and aligning your data to shared meaning is the right strategic approach.

article thumbnail

How smava makes loans transparent and affordable using Amazon Redshift Serverless

AWS Big Data

To create and manage the data products, smava uses Amazon Redshift , a cloud data warehouse. In this post, we show how smava optimized their data platform by using Amazon Redshift Serverless and Amazon Redshift data sharing to overcome right-sizing challenges for unpredictable workloads and further improve price-performance.

Insiders

Sign Up for our Newsletter

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

Trending Sources

article thumbnail

Enhance query performance using AWS Glue Data Catalog column-level statistics

AWS Big Data

Today, we’re making available a new capability of AWS Glue Data Catalog that allows generating column-level statistics for AWS Glue tables. These statistics are now integrated with the cost-based optimizers (CBO) of Amazon Athena and Amazon Redshift Spectrum , resulting in improved query performance and potential cost savings.

article thumbnail

5 ways to deploy your own large language model

CIO Business Intelligence

Then the question and the relevant information is sent to the LLM and embedded into an optimized prompt that might also specify the preferred format of the answer and tone of voice the LLM should use. Similarly, it’s optimized to use different models on the back end, because that’s how clients want it. “We

Modeling 139
article thumbnail

Big Data Ingestion: Parameters, Challenges, and Best Practices

datapine

Operations data: Data generated from a set of operations such as orders, online transactions, competitor analytics, sales data, point of sales data, pricing data, etc. The gigantic evolution of structured, unstructured, and semi-structured data is referred to as Big data. Self-Service.

Big Data 100
article thumbnail

Quantitative and Qualitative Data: A Vital Combination

Sisense

And, as industrial, business, domestic, and personal Internet of Things devices become increasingly intelligent, they communicate with each other and share data to help calibrate performance and maximize efficiency. The result, as Sisense CEO Amir Orad wrote , is that every company is now a data company. This is quantitative data.

article thumbnail

Create an end-to-end data strategy for Customer 360 on AWS

AWS Big Data

With QuickSight, you can embed dashboards to external websites and applications , and the SPICE engine enables rapid, interactive data visualization at scale. Data warehouse Data warehouses are efficient in consolidating structured data from multifarious sources and serving analytics queries from a large number of concurrent users.