Cloudera
MAY 15, 2019
He is a successful architect of healthcare data warehouses, clinical and business intelligence tools, big data ecosystems, and a health information exchange. The Enterprise Data Cloud – A Healthcare Perspective.
Knowledge Hut
DECEMBER 21, 2023
What is Data Engineering? Data engineering is the method to collect, process, validate and store data. It involves building and maintaining data pipelines, databases, and data warehouses. The purpose of data engineering is to analyze data and make decisions easier.
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Data Engineering Podcast
JUNE 11, 2021
Summary Google pioneered an impressive number of the architectural underpinnings of the broader big data ecosystem. In this episode Lak Lakshmanan enumerates the variety of services that are available for building your various data processing and analytical systems. No more scripts, just SQL.
Edureka
JUNE 1, 2023
Without spending a lot of money on hardware, it is possible to acquire virtual machines and install software to manage data replication, distributed file systems, and entire big data ecosystems. No infrastructure to maintain and scale : The customers just need to store, process, and analyze big data.
phData: Data Engineering
JANUARY 3, 2022
With that in place, data engineers can build data pipelines to allow data to flow out of the source systems. The result of this data pipeline is then stored in a separate location — generally in a highly available format for various business intelligence tools to query. This is not a simple task.
AltexSoft
JUNE 26, 2023
Data collection is a methodical practice aimed at acquiring meaningful information to build a consistent and complete dataset for a specific business purpose — such as decision-making, answering research questions, or strategic planning. Structured data is modeled to be easily searchable and occupy minimal storage space.
Expert insights. Personalized for you.
50 
Let's personalize your content