Data Preparation ETL Tools Structured Data 
ProjectPro
JUNE 6, 2025
Experts predict that by 2025, the global big data and data engineering market will reach $125.89 billion, and those with skills in cloud-based ETL tools and distributed systems will be in the highest demand. Clean, reformat, and aggregate data to ensure consistency and readiness for analysis.
Edureka
JULY 5, 2024
Tableau Prep is a fast and efficient data preparation and integration solution (Extract, Transform, Load process) for preparing data for analysis in other Tableau applications, such as Tableau Desktop. simultaneously making raw data efficient to form insights.
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
ProjectPro
JUNE 6, 2025
Imagine your organization has a mix of structured and semi-structured data. How can DBT handle transformations for both types of data? For structured data, standard DBT models can be created using SQL transformations that take advantage of relational databases' capabilities.
Snowflake
MARCH 30, 2023
A 2016 data science report from data enrichment platform CrowdFlower found that data scientists spend around 80% of their time in data preparation (collecting, cleaning, and organizing of data) before they can even begin to build machine learning (ML) models to deliver business value. Enter Snowpark !
ProjectPro
JUNE 6, 2025
Project Idea : Build a data engineering pipeline to ingest and transform data, focusing on runs, wickets, and strike rates. Use the ESPNcricinfo Ball-by-Ball Dataset to process match data. Store raw data in AWS S3, preprocess it using AWS Lambda, and query structured data in Amazon Athena.
Knowledge Hut
JANUARY 29, 2024
Google DataPrep: A data service provided by Google that explores, cleans, and prepares data, offering a user-friendly approach. Data Wrangler: Another data cleaning and transformation tool, offering flexibility in data preparation.
Knowledge Hut
JANUARY 30, 2024
Goal To extract and transform data from its raw form into a structured format for analysis. To uncover hidden knowledge and meaningful patterns in data for decision-making. Data Source Typically starts with unprocessed or poorly structured data sources. Analyzing and deriving valuable insights from data.
Expert insights. Personalized for you.
40 
Let's personalize your content