November 2020 Summaries

This blog post demonstrates how to use Gretel for Named Entity Recognition (NER) on sample data and load the records into Elasticsearch. It provides a blueprint using Docker to set up a local Elasticsearch instance, explains the structure of Gretel records including NER results, and outlines a simple workflow for loading data into an Elasticsearch cluster. The post also discusses how to explore the records in Kibana and perform queries programmatically. Overall, this combination of tools offers a powerful means to monitor trends in data over time or spot unusual patterns.

Gretel has successfully raised $12 million in Series A funding led by Greylock. The company aims to help developers create safe data and is working towards enabling an open data economy where synthetic data democratizes the playing field, fostering innovation. Developers can explore Gretel's free public beta and open-source synthetic library for generating synthetic datasets. Interested individuals can follow Gretel on Twitter, contribute to their GitHub repository, or join their community Slack channel.

Gretel has released new features that enhance data management and workflows. The updates include a detailed summary of individual records, making it easier to identify outliers and see relationships in the data. Additionally, Gretel Blueprints have been introduced, which provide templates for auto-anonymizing datasets without requiring any coding. These blueprints can be accessed through the new transform page within projects. Furthermore, users can now create a new project from scratch or select from sample datasets with included blueprints to get started anonymizing and balancing datasets quickly.