Database, High Quality Data and Raw Data

Database

High Quality Data

Raw Data

Fueling Data-Driven Decision-Making with Data Validation and Enrichment Processes

Precisely

SEPTEMBER 25, 2023

Data validation performs a check against existing values in a database to ensure that they fall within valid parameters. Data enrichment is the process of enhancing your data by appending relevant context from additional sources – improving its overall value, accuracy, and usability.

Data Validation

Data Validation Process Raw Data Data Cleanse

How to Use DBT to Get Actionable Insights from Data?

Workfall

JULY 4, 2023

Imagine a team of skilled data engineers on an exciting quest to transform raw data into a treasure trove of insights. With DBT, they weave powerful SQL spells to create data models that capture the essence of their organization’s information. The data warehouse, role, database, schema, credentials, etc.

Data Warehouse

Data Warehouse SQL PostgreSQL Database

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Leading the Development of Profitable and Sustainable Products

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Trending Sources

Business Intelligence vs. Data Mining: A Comparison

Knowledge Hut

JUNE 28, 2023

Data Sources Diverse and vast data sources, including structured, unstructured, and semi-structured data. Structured data from databases, data warehouses, and operational systems. Goal Extracting valuable information from raw data for predictive or descriptive purposes.

Data Mining

Data Mining Business Intelligence BI Datasets

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Leading the Development of Profitable and Sustainable Products

How To Get Promoted In Product Management

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AI Implementation: The Roadmap to Leveraging AI in Your Organization

Ascend.io

JANUARY 10, 2024

AI models are only as good as the data they consume, making continuous data readiness crucial. Here are the key processes that need to be in place to guarantee consistently high-quality data for AI models: Data Availability: Establish a process to regularly check on data availability. Actionable tip?

Data Pipeline

Data Pipeline Government Data Governance Raw Data

Data Quality Testing: 7 Essential Tests

Monte Carlo

DECEMBER 19, 2022

tests/test_not_null.sql {% test not_null(model, column_name) %} select * from {{ model }} where {{ column_name }} is null {% endtest %} Volume tests Is data coming in? These are all data quality issues related to the volume of data entering your database. Is it too little?

High Quality Data

High Quality Data Data SQL Bytes

A Day in the Life of a Data Scientist

Knowledge Hut

JANUARY 24, 2024

They employ a wide array of tools and techniques, including statistical methods and machine learning, coupled with their unique human understanding, to navigate the complex world of data. A significant part of their role revolves around collecting, cleaning, and manipulating data, as raw data is seldom pristine.

Database-centric

Database-centric Data Science Machine Learning Datasets

What is dbt Testing? Definition, Best Practices, and More

Monte Carlo

AUGUST 30, 2023

When you run dbt test, dbt will execute these SQL tests against your models in your transformed database. You will also need to securely store and provide dbt with the necessary credentials to access your target database. Once the models are created and data transformed, `dbt test` should be executed.

SQL

SQL Datasets Database High Quality Data

Observability Platforms: 8 Key Capabilities and 6 Notable Solutions

Databand.ai

JULY 10, 2023

Observability platforms not only supply raw data but also offer actionable insights through visualizations, dashboards, and alerts. Security: Observability platforms often include built-in security features to ensure the integrity and confidentiality of your data.

Data Pipeline

Data Pipeline Algorithm Raw Data Aggregated Data

Data Pipelines in the Healthcare Industry

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

Healthcare Data Pipeline Medical Pipeline-centric

Build vs Buy Data Pipeline Guide

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. At Uber, for example, data ingestion scale quickly surpassed what out-of-the-box solutions could support.

Data Pipeline

Data Pipeline Building Data Ingestion BI

Data Teams and Their Types of Data Journeys

DataKitchen

OCTOBER 2, 2023

This comprehensive article delves into the complexities encountered by various types of data teams—Data Ingestion Teams, End-to-End Data Product Teams, and Enterprise Data Enablement Teams—to name a few. It explores why DataKitchen’s ‘Data Journeys’ capability can solve these challenges.

Data Ingestion

Data Ingestion Data Government Datasets

[O’Reilly Book] Chapter 1: Why Data Quality Deserves Attention Now

Monte Carlo

AUGUST 31, 2023

These conversations inspired us to write this book to convey some of the best practices we’ve learned and developed related to managing data quality at each stage of the data pipeline, from ingestion to analytics, and share how data teams in similar situations may be able to prevent their own data downtime.

Data Lake

Data Lake Data Pipeline Unstructured Data Data Warehouse

Natural Language Processing: A Guide to NLP Use Cases, Approaches, and Tools

AltexSoft

AUGUST 25, 2021

Another way to handle unstructured text data using NLP is information extraction (IE). and organize it in a database. Information extraction is a key technology in many high level tasks including: Machine translation. Plus, you likely won’t be able to use too much data. Assessing text data quality.

Process

Process Deep Learning Datasets Machine Learning

Bridging the Gap: How ‘Data in Place’ and ‘Data in Use’ Define Complete Data Observability

DataKitchen

SEPTEMBER 21, 2023

What is Data in Place? 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. There are multiple locations where problems can happen in a data and analytic system. What is Data in Use?

Raw Data

Raw Data Data Business Intelligence High Quality Data

The Ten Standard Tools To Develop Data Pipelines In Microsoft Azure

DataKitchen

JULY 27, 2023

Azure Databricks Delta Live Table s: These provide a more straightforward way to build and manage Data Pipelines for the latest, high-quality data in Delta Lake. Azure Blob Storage serves as the data lake to store raw data. We call that multi-tool data assembly line a ‘ Data Journey.’

Data Pipeline

Data Pipeline BI Machine Learning Data Preparation

Data Engineering Digest

Fueling Data-Driven Decision-Making with Data Validation and Enrichment Processes

How to Use DBT to Get Actionable Insights from Data?

Webinars

Trending Sources

Business Intelligence vs. Data Mining: A Comparison

Webinars

AI Implementation: The Roadmap to Leveraging AI in Your Organization

Data Quality Testing: 7 Essential Tests

A Day in the Life of a Data Scientist

What is dbt Testing? Definition, Best Practices, and More

Observability Platforms: 8 Key Capabilities and 6 Notable Solutions

Data Pipelines in the Healthcare Industry

Build vs Buy Data Pipeline Guide

Data Teams and Their Types of Data Journeys

[O’Reilly Book] Chapter 1: Why Data Quality Deserves Attention Now

Natural Language Processing: A Guide to NLP Use Cases, Approaches, and Tools

Bridging the Gap: How ‘Data in Place’ and ‘Data in Use’ Define Complete Data Observability

The Ten Standard Tools To Develop Data Pipelines In Microsoft Azure

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