article thumbnail

Building a Machine Learning Application With Cloudera Data Science Workbench And Operational Database, Part 3: Productionization of ML models

Cloudera

In this last installment, we’ll discuss a demo application that uses PySpark.ML to make a classification model based off of training data stored in both Cloudera’s Operational Database (powered by Apache HBase) and Apache HDFS. Training Data in HBase and HDFS. Below is a simple screen recording of the demo application.

article thumbnail

Improving Meta’s global maps

Engineering at Meta

Instagram maps on Android Actus (from Meta’s New Product Experimentation team) Facebook Crisis Response Facebook check-ins Mapillary ( iOS , Android , Web ) Meta Quest Pro demo finder WhatsApp business directory on Android Fast rendering and up-to-date data We’re now serving several basemaps.

Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

DataMynd: Empowering Data Teams with Native Data Privacy Solutions

Snowflake

Rather than scrubbing or redacting sensitive fields — or worse, creating rules to generate “realistic” data from the ground up —you simply point our app at your production schema, train one of the included models, and generate as much synthetic data as you like. It’s basically an “easy button” for synthetic data.

Data 93
article thumbnail

How to Easily Connect Airbyte with Snowflake for Unleashing Data’s Power?

Workfall

Pre-filter and pre-aggregate data at the source level to optimize the data pipeline’s efficiency. Adapt to Changing Data Schemas: Data sources aren’t static; they evolve. Account for potential changes in data schemas and structures.

article thumbnail

A New Era of Lifecycle Marketing with the AI Data Cloud and AI Decisioning

Snowflake

Data integration As a Snowflake Native App, AI Decisioning leverages the existing data within an organization’s AI Data Cloud, including customer behaviors and product and offer details. During a one-time setup, your data owner maps your existing data schemas within the UI, which fuels AI Decisioning’s models.

Cloud 63
article thumbnail

17 Ways to Mess Up Self-Managed Schema Registry

Confluent

Therefore, not restricting access to the Schema Registry might allow an unauthorized user to mess with the service in such a way that client applications can no longer be served schemas to deserialize their data. Allow end user REST API calls to Schema Registry over HTTPS instead of the default HTTP.

article thumbnail

The Pros and Cons of Leading Data Management and Storage Solutions

The Modern Data Company

And by leveraging distributed storage and open-source technologies, they offer a cost-effective solution for handling large data volumes. In other words, the data is stored in its raw, unprocessed form, and the structure is imposed when a user or an application queries the data for analysis or processing.