Data Architecture Data Transformation Interactive 
AWS Big Data
NOVEMBER 22, 2024
The need for streamlined data transformations As organizations increasingly adopt cloud-based data lakes and warehouses, the demand for efficient data transformation tools has grown. This enables you to extract insights from your data without the complexity of managing infrastructure.
AWS Big Data
NOVEMBER 27, 2024
Together with price-performance, Amazon Redshift offers capabilities such as serverless architecture, machine learning integration within your data warehouse and secure data sharing across the organization. dbt Cloud is a hosted service that helps data teams productionize dbt deployments.
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CIO Business Intelligence
AUGUST 3, 2023
In the past, First Service Credit Union’s Chief data officer Ty Robbins struggled to integrate data from the legacy, non-relational, and often proprietary tabular databases on which many credit unions run. But before reaping such benefits, “you’ve got to get the infrastructure right and the data clean,” says Davis.
AWS Big Data
NOVEMBER 15, 2023
The difference lies in when and where data transformation takes place. In ETL, data is transformed before it’s loaded into the data warehouse. In ELT, raw data is loaded into the data warehouse first, then it’s transformed directly within the warehouse.
AWS Big Data
NOVEMBER 9, 2023
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.
AWS Big Data
DECEMBER 21, 2023
Overview of solution As a data-driven company, smava relies on the AWS Cloud to power their analytics use cases. smava ingests data from various external and internal data sources into a landing stage on the data lake based on Amazon Simple Storage Service (Amazon S3).
AWS Big Data
AUGUST 1, 2024
However, you might face significant challenges when planning for a large-scale data warehouse migration. The following diagram illustrates a scalable migration pattern for extract, transform, and load (ETL) scenario. The success criteria are the key performance indicators (KPIs) for each component of the data workflow.
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