Dataconomy

MAY 26, 2025

Definition and purpose of BDaaS Big Data as a Service encompasses a range of cloud-based data platforms that offer various functionalities tailored to meet specific data-related needs. Leading BDaaS solutions Some of the most recognized BDaaS solutions include Amazon EMR, Google Cloud Dataproc, and Azure HDInsight.

Big Data 160

Big Data Big Data Hadoop Cloud Computing 160

How to Learn Machine Learning

APRIL 26, 2025

The responsibilities of this phase can be handled with traditional databases (MySQL, PostgreSQL), cloud storage (AWS S3, Google Cloud Storage), and big data frameworks (Hadoop, Apache Spark). This definition specifically describes the Data Scientist as being the predictive powerhouse of the data science ecosystem.

Data Science 52

Data Science Data Analyst Data Scientist Machine Learning 52

O'Reilly Media

MAY 15, 2025

5:34 : You work with the folks at Azure, so presumably you know what actual enterprises are doing with generative AI. All the big companies have their own definitions. Id set the stage with my definition: a system that can take action on your half. 23:19 : Definitely. We have DeepSeek R1 available on Azure.

AI 84

AI AI Hadoop Python 84

Pickl AI

JUNE 7, 2024

Definition and Explanation of the ETL Process ETL is a data integration method that combines data from multiple sources. Key Features Out-of-the-Box Connectors: Includes connectors for databases like Hadoop, CRM systems, XML, JSON, and more. Cost Considerations: Implementing and maintaining Hadoop clusters can incur significant costs.

ETL 40

ETL Data Quality Data Pipeline Data Warehouse 40

IBM Journey to AI blog

JANUARY 18, 2023

This is an architecture that’s well suited for the cloud since AWS S3 or Azure DLS2 can provide the requisite storage. It can include technologies that range from Oracle, Teradata and Apache Hadoop to Snowflake on Azure, RedShift on AWS or MS SQL in the on-premises data center, to name just a few. Differences exist also.

Data Lakes 52

Data Lakes Data Warehouse Azure Apache Hadoop 52

DagsHub

OCTOBER 23, 2024

For instance, if you are working with several high-definition videos, storing them would take a lot of storage space, which could be costly. Popular data lake solutions include Amazon S3 , Azure Data Lake , and Hadoop. So, when working with unstructured data in an AI/ML project, you must consider storage space.

Machine Learning 52

Machine Learning Machine Learning Data Lakes AI 52

The MLOps Blog

MAY 31, 2023

You may also like Building a Machine Learning Platform [Definitive Guide] Consideration for data platform Setting up the Data Platform in the right way is key to the success of an ML Platform. 2 It also helps to standardize feature definitions across teams. How to set up a data processing platform?

ML 59

ML ML Algorithm Machine Learning 59