Monte Carlo

AUGUST 31, 2023

As the data analyst or engineer responsible for managing this data and making it usable, accessible, and trustworthy, rarely a day goes by without having to field some request from your stakeholders. But what happens when the data is wrong? In our opinion, data quality frequently gets a bad rep.

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. And data orchestration tools are generally easy to stand-up for initial use-cases. Missed Nishith’s 5 considerations?

Data Pipeline 52

Data Pipeline Building Data Ingestion BI 52

DataKitchen

JULY 27, 2023

The Ten Standard Tools To Develop Data Pipelines In Microsoft Azure. While working in Azure with our customers, we have noticed several standard Azure tools people use to develop data pipelines and ETL or ELT processes. We counted ten ‘standard’ ways to transform and set up batch data pipelines in Microsoft Azure.

Data Pipeline 52

Data Pipeline BI Machine Learning Data Preparation 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

DataKitchen

SEPTEMBER 21, 2023

Data in Place refers to the organized structuring and storage of data within a specific storage medium, be it a database, bucket store, files, or other storage platforms. In the contemporary data landscape, data teams commonly utilize data warehouses or lakes to arrange their data into L1, L2, and L3 layers.

Raw Data 70

Raw Data Data Business Intelligence High Quality Data 70

Monte Carlo

AUGUST 30, 2023

Run the test again to validate that the initial problem is solved and that your data meets your quality and accuracy standards. Schedule and automate Just like schema tests, custom data tests in dbt are typically not run just once but are incorporated into your regular data pipeline to ensure ongoing data quality.

SQL 52

SQL Datasets Database High Quality Data 52