Knowledge Hut

JUNE 20, 2023

Apache Spark, Microsoft Azure, Amazon Web services, etc. Skills A data engineer should have good programming and analytical skills with big data knowledge. A machine learning engineer should know deep learning, scaling on the cloud, working with APIs, etc.

Machine Learning 52

Machine Learning Data Engineering Data Engineer Engineering 52

ProjectPro

JANUARY 24, 2023

Integration with other AWS services: SageMaker integrates seamlessly with other services, such as Amazon Simple Storage Service(S3) and Amazon Elastic Compute Cloud (EC2), making it easy to incorporate machine learning into existing workflow and infrastructure. Amazon launched SageMaker in November 2017.

Building 52

Building Algorithm Machine Learning AWS 52

U-Next

SEPTEMBER 7, 2022

Data lakes, however, are sometimes used as cheap storage with the expectation that they are used for analytics. For building data lakes, the following technologies provide flexible and scalable data lake storage : . Amazon Web Services S3 . Gen 2 Azure Data Lake Storage .

Data Lake 52

Data Lake Data Warehouse Unstructured Data Amazon Web Services 52

ProjectPro

SEPTEMBER 26, 2021

Data Warehousing: Data warehouses store massive pieces of information for querying and data analysis. Your organization will use internal and external sources to port the data. You must be aware of Amazon Web Services (AWS) and the data warehousing concept to effectively store the data sets.

Big Data 52

Big Data Data Engineering Data Engineer Engineering 52

ProjectPro

SEPTEMBER 6, 2021

Google launched its Cloud Platform in 2008, six years after Amazon Web Services launched in 2002. Amazon brought innovation in technology and enjoyed a massive head start compared to Google Cloud, Microsoft Azure , and other cloud computing services. Let’s get started! Launched in 2006.

AWS 52

AWS Amazon Web Services Google Cloud Cloud Storage 52

ProjectPro

JANUARY 31, 2022

Moreover, numerous sources offer unique third-party data that is instantly accessible when needed. Provides Powerful Computing Resources for Data Processing Before inputting data into advanced machine learning models and deep learning tools, data scientists require sufficient computing resources to analyze and prepare it.

Architecture 52

Architecture IT Data Warehouse Amazon Web Services 52

ProjectPro

AUGUST 24, 2021

Source Code: Analyse Movie Ratings Data Unlock the ProjectPro Learning Experience for FREE 11) Retail Analytics Project Example For retail stores , inventory levels, supply chain movement, customer demand, sales, etc. There are three stages in this real-world data engineering project. The second stage is data preparation.

Data Engineering 52

Data Engineering Data Engineer Coding Project 52