Monte Carlo

AUGUST 31, 2023

As data becomes not just an output but a financial commodity for many organizations, it’s important that this information can be trusted. As a result, companies are increasingly treating their data like code, applying frameworks and paradigms long-standard among software engineering teams to their data organizations and architectures.

Data Lake 52

Data Lake Data Pipeline Unstructured Data Data Warehouse 52

Monte Carlo

APRIL 24, 2023

Data ingestion When we think about the flow of data in a pipeline, data ingestion is where the data first enters our platform. There are two primary types of raw data.

Data Pipeline 52

Data Pipeline Building Data Ingestion BI 52

Monte Carlo

MAY 23, 2023

Business data assets at Assurance are loaded into the company’s lakehouse architecture through various methods, then stored in several data stores. The data team then uses tools like dbt and Airflow to refine, model and transform raw data into usable, query-able assets through Trino and Starburst.

Insurance 52

Insurance Business Intelligence Machine Learning Data 52

DareData

JULY 29, 2020

With these points in mind, I argue that the biggest hurdle to the widespread adoption of these advanced techniques in the healthcare industry is not intrinsic to the industry itself, or in any way related to its practitioners or patients, but simply the current lack of high-quality data pipelines.

Healthcare 52

Healthcare Data Pipeline Medical Pipeline-centric 52

Monte Carlo

AUGUST 30, 2023

More specifically, teams use dbt to write transformations as SQL queries, version control the code, and deploy transformations incrementally. dbt also happens to be an incredibly handy tool for running many data quality tests. Once the models are created and data transformed, `dbt test` should be executed.

SQL 52

SQL Datasets Database High Quality Data 52

AltexSoft

AUGUST 25, 2021

It’s called deep because it comprises many interconnected layers — the input layers (or synapses to continue with biological analogies) receive data and send it to hidden layers that perform hefty mathematical computations. Plus, you likely won’t be able to use too much data. Assessing text data quality.

Process 139

Process Deep Learning Datasets Machine Learning 139

DataKitchen

OCTOBER 2, 2023

The article illuminates how Data Journeys can enhance data governance, improve operational efficiency, and ultimately lead to organizational success by thoroughly examining different Data Journey types—’ Watcher,’ ‘Traveler,’ ‘Hub & Spoke,’ and ‘Payload.’ ’ What’s a Data Journey?

Data Ingestion 40

Data Ingestion Data Government Datasets 40