Knowledge Hut

JANUARY 25, 2024

The specific methods and steps for data cleaning may vary depending on the dataset, but its importance remains constant in the data science workflow. Why Is Data Cleaning So Important? These issues can stem from various sources such as human error, data scraping, or the integration of data from multiple sources.

Data Cleanse 52

Data Cleanse Datasets Data Preparation Data Science 52

Databand.ai

JULY 10, 2023

Observability platforms not only supply raw data but also offer actionable insights through visualizations, dashboards, and alerts. Databand allows data engineering and data science teams to define data quality rules, monitor data consistency, and identify data drift or anomalies.

Data Pipeline 52

Data Pipeline Algorithm Data Engineering Data Engineer 52

Monte Carlo

MAY 23, 2023

As the company scales, various teams within Assurance—including data engineering, machine learning engineering, data science, business intelligence, and analytics engineering—leverage the platform to create new data assets. Requirements for such a tool included: 1.

Insurance 52

Insurance Business Intelligence Machine Learning Data 52

Monte Carlo

JUNE 23, 2022

Whether the end result is a weekly report, dashboard, or embedded in a customer facing application, data products require a level of polish and data curation that is antithetical to unorganized sprawl. Your ability to pipe data is virtually limitless, but you are constrained by the capacity of humans to make it sustainably meaningful.

Big Data 40

Big Data Management Machine Learning Data Warehouse 40

Monte Carlo

AUGUST 31, 2023

Not long after data warehouses moved to the cloud, so too did data lakes (a place to transform and store unstructured data), giving data teams even greater flexibility when it comes to managing their data assets. That is the question – at least if you ask a data engineer.

Data Lake 52

Data Lake Data Pipeline Unstructured Data Data Warehouse 52

Monte Carlo

AUGUST 30, 2023

The `dbt run` command will compile and execute your models, thus transforming your raw data into analysis-ready tables. Once the models are created and data transformed, `dbt test` should be executed. This command runs all tests defined in your dbt project against the transformed data.

SQL 52

SQL Datasets Database High Quality Data 52

DataKitchen

OCTOBER 2, 2023

Data Teams and Their Types of Data Journeys In the rapidly evolving landscape of data management and analytics, data teams face various challenges ranging from data ingestion to end-to-end observability. The Hub Data Journey provides the raw data and adds value through a ‘contract.

Data Ingestion 40

Data Ingestion Data Government Datasets 40