Precisely

MARCH 9, 2023

As cloud computing platforms make it possible to perform advanced analytics on ever larger and more diverse data sets, new and innovative approaches have emerged for storing, preprocessing, and analyzing information. In this article, we’ll focus on a data lake vs. data warehouse.

Data Warehouse 59

Data Warehouse Data Lakes Hadoop Big Data 59

Dataconomy

FEBRUARY 12, 2024

Businesses increasingly rely on up-to-the-moment information to respond swiftly to market shifts and consumer behaviors Unstructured data challenges : The surge in unstructured data—videos, images, social media interactions—poses a significant challenge to traditional ETL tools. Image credit ) 5.

ETL 195

ETL Data Governance Machine Learning Machine Learning 195

Dataconomy

MAY 27, 2025

For example, when working on a data warehouse project, a graduate with an MIS degree will factor in budget constraints, user experience, and departmental goals. Staying agile with emerging technologies Whether it is cloud computing, machine learning, or AI implementations, an MIS graduate is well-positioned to adapt.

Analytics 91

Analytics Analytics Database Data Warehouse 91

Precisely

OCTOBER 16, 2023

Consequently, managers now oversee IT costs for their operations and engage directly in cloud computing contracts. This shift has influenced how cloud resources are designed and marketed, focusing on easy access, modularity, and straightforward deployment. The movement of data also grows more complex with data democratization.

Analytics 72

Analytics Analytics Big Data Analytics Big Data Analytics 72

Alation

JANUARY 17, 2023

The modern data stack is a combination of various software tools used to collect, process, and store data on a well-integrated cloud-based data platform. It is known to have benefits in handling data due to its robustness, speed, and scalability. Data ingestion/integration services. Data orchestration tools.

Data Warehouse 52

Data Warehouse ETL Tableau Cloud Data 52

Dataconomy

FEBRUARY 23, 2023

Collecting, storing, and processing large datasets Data engineers are also responsible for collecting, storing, and processing large volumes of data. This involves working with various data storage technologies, such as databases and data warehouses, and ensuring that the data is easily accessible and can be analyzed efficiently.

Big Data 91

Big Data Big Data Data Engineering Data Engineering 91

Alation

JULY 13, 2021

This recent cloud migration applies to all who use data. We have seen the COVID-19 pandemic accelerate the timetable of cloud data migration , as companies evolve from the traditional data warehouse to a data cloud, which can host a cloud computing environment.

Cloud Data 52

Cloud Data Data Governance Data Warehouse Data Mining 52