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In today’s data-driven era, you have more raw data than ever before. However, to leverage the power of big data, you need to convert raw data into valuable insights for informed decision-making. When it comes to preparing data for analysis, you will always come across the terms “data wrangling” and “ETL.” While they may sound […]View the full article

Starting out on your data journey? Here’s a 7-step learning path to master data wrangling with pandas.View the full article

Amazon Personalize is integrating with Amazon SageMaker Data Wrangler to make it easier for customers to import and prepare their data. Amazon Personalize enables developers to improve customer engagement through personalized product and content recommendations – no ML expertise required. The quality of data used for model training affects the quality of the recommendations, which makes data aggregation and preparation a critical step to get high-quality recommendations using Amazon Personalize. With this launch, Amazon Personalize gives you the ability to prepare your data through Amazon SageMaker Data Wrangler before using it in Amazon Personalize. Customers can use Amazon SageMaker Data Wrangler to import data from 40+ supported data sources and perform end-to-end data preparation (including data selection, cleansing, exploration, visualization, and processing at scale) in a single user interface using little to no code. This allows customers to rapidly prepare their users, items or interactions dataset using Amazon SageMaker Data Wrangler by leveraging over 300 built-in data transformations, retrieving data insights, and quickly iterating by fixing data issues. View the full article