Remove Building Remove Data Schemas Remove Demo
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. As a result, I decided to use an open-source Occupancy Detection Data Set to build this application.

article thumbnail

DataMynd: Empowering Data Teams with Native Data Privacy Solutions

Snowflake

Welcome to Snowflake’s Startup Spotlight, where we ask startup founders about the problems they’re solving, the apps they’re building, and the lessons they’ve learned during their startup journey. It’s basically an “easy button” for synthetic data. You can even train ML models on our synthetic data, or use it for data sharing purposes.

Data 82
Insiders

Sign Up for our Newsletter

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

article thumbnail

Improving Meta’s global maps

Engineering at Meta

We’re Meta now, but our mission remains the same: Giving people the power to build community and bring the world closer together. This new data schema was born partly out of our cartographic tiling logic, and it includes everything necessary to make a map of the world. Icon versus icon Our initial basemaps eschewed icons.

article thumbnail

A Guide to Data Pipelines (And How to Design One From Scratch)

Striim

In this guide, we’ll dive into everything you need to know about data pipelines—whether you’re just getting started or looking to optimize your existing setup. We’ll answer the question, “What are data pipelines?” Then, we’ll dive deeper into how to build data pipelines and why it’s imperative to make your data pipelines work for you.

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

The JaffleGaggle Story: Data Modeling for a Customer 360 View

dbt Developer Hub

It includes a set of demo CSV files, which you can use as dbt seeds to test Donny's project for yourself. If not, I’d recommend taking a second to look at Claire Carroll’s README for the original Jaffle Shop demo project (otherwise this playbook is probably going to be a little weird, but still useful, to read).

article thumbnail

Why Data Cleaning is Failing Your ML Models – And What To Do About It

Monte Carlo

Unbeknownst to you, the training data contains a table with aggregated visitor website data with columns that haven’t been updated in a month. It turns out the marketing operations team upgraded to Google Analytics 4 to get ahead of the July 2023 deadline which changed the data schema.

IT 52