Towards Data Science

NOVEMBER 4, 2023

I’d like to discuss some popular Data engineering questions: Modern data engineering (DE). Does your DE work well enough to fuel advanced data pipelines and Business intelligence (BI)? Are your data pipelines efficient? It will be a great tool for those with minimal Python knowledge. What is it?

Data Engineering 98

Data Engineering Data Engineer Engineering BI 98

Databand.ai

JULY 19, 2023

However, ETL can be a better choice in scenarios where data quality and consistency are paramount, as the transformation process can include rigorous data cleaning and validation steps. The data pipeline should be designed to handle the volume, variety, and velocity of the data.

Data Cleanse 52

Data Cleanse Data Storage Raw Data Data Warehouse 52

Knowledge Hut

MARCH 22, 2024

Versatility: The versatile nature of MongoDB enables it to easily deal with a broad spectrum of data types , structured and unstructured, and therefore, it is perfect for modern applications that need flexible data schemas. Extracting, transforming, and loading data from various sources into MongoDB. Python, Java).

MongoDB 52

MongoDB Amazon Web Services Computer Science Education 52

Rockset

DECEMBER 9, 2021

Real-time data streams typically arrive raw and semi-structured, say in the form of a JSON document, with many levels of nesting. Moreover, new fields and columns of data are constantly appearing. These can easily break rigid data pipelines in the batch world. Destination: Data Apps and Microservices.

Transportation 52

Transportation BI SQL Database 52

phData: Data Engineering

SEPTEMBER 27, 2024

Both persistent staging and data lakes involve storing large amounts of raw data. But persistent staging is typically more structured and integrated into your overall customer data pipeline. It’s not just a dumping ground for data, but a crucial step in your customer data processing workflow.

Data 52

Data Raw Data Data Lake Architecture 52

Ascend.io

FEBRUARY 25, 2025

Continuous Integration and Continuous Delivery (CI/CD) has transformed software development by enabling faster, safer deployments and data teams are now realizing these same benefits must extend to data pipelines and analytics code. But applying CI/CD in a data context comes with unique challenges.

Data Pipeline 52

Data Pipeline Data SQL Coding 52