Remove Data Integration Remove Data Platform Remove Python
article thumbnail

Improving air quality with generative AI

AWS Machine Learning Blog

This post presents a solution that uses a generative artificial intelligence (AI) to standardize air quality data from low-cost sensors in Africa, specifically addressing the air quality data integration problem of low-cost sensors. Some input data uses a pair of value type and value for a measurement.

article thumbnail

Recapping the Cloud Amplifier and Snowflake Demo

Towards AI

To start, get to know some key terms from the demo: Snowflake: The centralized source of truth for our initial data Magic ETL: Domo’s tool for combining and preparing data tables ERP: A supplemental data source from Salesforce Geographic: A supplemental data source (i.e., Instagram) used in the demo Why Snowflake?

ETL 111
professionals

Sign Up for our Newsletter

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

article thumbnail

Comparing Tools For Data Processing Pipelines

The MLOps Blog

Some of the popular cloud-based vendors are: Hevo Data Equalum AWS DMS On the other hand, there are vendors offering on-premise data pipeline solutions and are mostly preferred by organizations dealing with highly sensitive data. Dagster Supports end-to-end data management lifecycle. It supports multiple file formats.It

ETL 59
article thumbnail

Top 50+ Data Analyst Interview Questions & Answers

Pickl AI

During a data analysis project, I encountered a significant data discrepancy that threatened the accuracy of our analysis. I conducted thorough data validation, collaborated with stakeholders to identify the root cause, and implemented corrective measures to ensure data integrity. 10% group discount available.

article thumbnail

What is Hadoop and How Does It Work?

Pickl AI

Job Submission and Cluster Management: To take advantage of Hadoop, you generally use the Hadoop API to generate code in Java, Python, or other compatible languages. Aside from cluster management, responsibilities like data integration and data quality control can be difficult for organisations that use Hadoop systems.

article thumbnail

Learnings From Building the ML Platform at Mailchimp

The MLOps Blog

I actually did not pick up Python until about a year before I made the transition to a data scientist role. You see them all the time with a headline like: “data science, machine learning, Java, Python, SQL, or blockchain, computer vision.” The only decorator that comes to my mind is a Python decorator.

ML 52