At Adventure Works, you need to extract, transform, and load data from various data sources to consolidate them into the company's data warehouse. What critical elements will you focus on during the Transform step of the Extract, Transform, Load (ETL) process? 1 pointPrioritizing real-time data ingestion over batch processing.Ensuring the transformed data is consistent, accurate, and complete.Implementing machine learning models for predictive analysis.Organizing file structure for data accessibility.

3.Question 3What is the Extract, Transform, and Load (ETL) process’s primary purpose in data management?1 pointTo create data pipelines for real-time data movement.To manage data repositories and databases.To extract data from data repositories and store it in raw form.To convert raw data into analysis-ready data by extracting, cleaning, standardizing, and transforming it.

Question 13In the data analysis process, what is the primary purpose of the ETL (Extract, Transform, Load) process? Select all that apply.1 pointPerforming complex calculations on the data.Transforming raw data into a structured format suitable for analysis.Consolidating data from multiple sources into a single source for easy access.Loading the transformed data into a suitable storage space.

During the ETL process, the task of data integration can inolve: 1. Data normalization. 2. Designing star schema with identification of fact measures. 3. Creating visualisations and dashboards. 4. Developing KPIs and Balanced scorecard. 5. None of the answers are correct. 6. 1 and 2.

.Question 1Which Data Science category do you extract, transform, and load data?1 pointData VisualizationData ManagementData Integration and TransformationModel Building

Data Warehouse é “um banco de dados relacional projetado para consulta e análise, em vez de processamento de transações”. Está inserido dentro de um ambiente de processos de extração (extract), transporte (carga/load) e transformação (transform) – ETL – das informações, além de um mecanismo de processamento analítico online (OLAP), entre outras ferramentas que possam agregar informações diversas para auxiliar na tomada de decisão. Algumas características são:I. Baixo nível de granularidadeII. Grande quantidade de dados históricosIII. Tecnologia otimizada para armazenamento e gerência de grandes quantidades de dadosIV. Tecnologia otimizada para acesso de consultas rápidasV. Cada área departamental possui suas características específicasDas alternativas, quais características se referem ao Data Warehouse?I, II e IIII, IV e VIV e VApenas IIIIII, IV e V