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.

Google Cloud 100

Google Cloud Cloud Big Data Ecosystem BI 100

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.

AWS 52

AWS Data Analytics Cloud Amazon Web Services 52

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.

Cloud 50

Cloud Hadoop Healthcare Big Data Ecosystem 50

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.

Data Collection 52

Data Collection Machine Learning Unstructured Data Non-relational Database 52

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.

Data Engineer 52

Data Engineer Data Engineering Engineering Data Governance 52

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.

Data Engineer 52

Data Engineer Data Engineering Engineering MongoDB 52