Remove Data Transformation Remove Document Remove Structured Data
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Semantization of Regulatory Documents in AECO

Ontotext

But even though technologies like Building Information Modelling (BIM) have finally introduced symbolic representation, in many ways, AECO still clings to outdated, analog practices and documents. Here, one of the challenges involves digitizing the national specifics of regulatory documents and building codes in multiple languages.

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Ensuring Data Transformation Quality with dbt Core

Wayne Yaddow

How dbt Core aids data teams test, validate, and monitor complex data transformations and conversions Photo by NASA on Unsplash Introduction dbt Core, an open-source framework for developing, testing, and documenting SQL-based data transformations, has become a must-have tool for modern data teams as the complexity of data pipelines grows.

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Enriching metadata for accurate text-to-SQL generation for Amazon Athena

AWS Big Data

Amazon Athena provides interactive analytics service for analyzing the data in Amazon Simple Storage Service (Amazon S3). Amazon Redshift is used to analyze structured and semi-structured data across data warehouses, operational databases, and data lakes.

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Ingest data from Google Analytics 4 and Google Sheets to Amazon Redshift using Amazon AppFlow

AWS Big Data

With Amazon AppFlow, you can run data flows at nearly any scale and at the frequency you chooseon a schedule, in response to a business event, or on demand. You can configure data transformation capabilities such as filtering and validation to generate rich, ready-to-use data as part of the flow itself, without additional steps.

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Create a modern data platform using the Data Build Tool (dbt) in the AWS Cloud

AWS Big Data

In this post, we delve into a case study for a retail use case, exploring how the Data Build Tool (dbt) was used effectively within an AWS environment to build a high-performing, efficient, and modern data platform. It does this by helping teams handle the T in ETL (extract, transform, and load) processes.

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Apply fine-grained access and transformation on the SUPER data type in Amazon Redshift

AWS Big Data

SUPER data type columns in Amazon Redshift contain semi-structured data like JSON documents. Previously, data masking in Amazon Redshift only worked with regular table columns, but now you can apply masking policies specifically to elements within SUPER columns. All columns should masked for them.

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Gain insights from historical location data using Amazon Location Service and AWS analytics services

AWS Big Data

You can also use the data transformation feature of Data Firehose to invoke a Lambda function to perform data transformation in batches. Query the data using Athena Athena is a serverless, interactive analytics service built to analyze unstructured, semi-structured, and structured data where it is hosted.

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