Databand.ai

AUGUST 30, 2023

DataOps is a collaborative approach to data management that combines the agility of DevOps with the power of data analytics. It aims to streamline data ingestion, processing, and analytics by automating and integrating various data workflows.

Architecture 52

Architecture Data Ingestion Data Governance Data Cleanse 52

Knowledge Hut

SEPTEMBER 25, 2023

Microsoft Azure is a cloud computing platform that gives businesses fantastic services. This demonstrates how in-demand Microsoft Certified Data Engineers are becoming. They are moving their servers and on-premises data to Azure Cloud. What does all of this mean for Data Engineering professionals?

Data Engineering 52

Data Engineering Data Engineer Engineering Data Lake 52

Data Engineering Weekly

OCTOBER 30, 2022

Editor’s Note: The current state of the Data Catalog The results are out for our poll on the current state of the Data Catalogs. The highlights are that 59% of folks think data catalogs are sometimes helpful. We saw in the Data Catalog poll how far it has to go to be helpful and active within a data workflow.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Ingestion 52

Data Engineering Podcast

MAY 26, 2024

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Data lakes are notoriously complex. SQL Server version upgrade) Section 2: Types of Migrations for Infrastructure Focus Storage migration: Moving data between systems (HDD to SSD, SAN to NAS, etc.)

Systems 130

Systems Data Lake High Quality Data Google Cloud 130

dbt Developer Hub

MARCH 23, 2023

While we can surely rely on that overview to validate the final refactored model with its legacy counterpart, it can be less useful while we are in the middle of the process of rebuilding a data workflow, where we need to track down which are exactly the columns that are causing incompatibility issues and what is wrong with them.

Data Warehouse 64

Data Warehouse Generalist SQL Coding 64

Databand.ai

AUGUST 30, 2023

Poor data quality can lead to incorrect or misleading insights, which can have significant consequences for an organization. DataOps tools help ensure data quality by providing features like data profiling, data validation, and data cleansing.

Data Cleanse 52

Data Cleanse Data Pipeline Data Ingestion Data Validation 52

dbt Developer Hub

APRIL 24, 2023

One example of a popular drag-and-drop transformation tool is Alteryx which allows business analysts to transform data by dragging and dropping operators in a canvas. In this sense, dbt may be a more suitable solution to building resilient and modular data pipelines due to its focus on data modeling.

BI 83

BI Data Workflow SQL Data Pipeline 83