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

Knowledge Hut

APRIL 25, 2023

Data modeling: Data engineers should be able to design and develop data models that help represent complex data structures effectively. Data processing: Data engineers should know data processing frameworks like Apache Spark, Hadoop, or Kafka, which help process and analyze data at scale.

Data Engineering 52

Data Engineering Data Engineer Engineering Google Cloud 52

Knowledge Hut

MARCH 28, 2024

Role Level: Intermediate Responsibilities Design and develop big data solutions using Azure services like Azure HDInsight, Azure Databricks, and Azure Data Lake Storage. Implement data ingestion, processing, and analysis pipelines for large-scale data sets.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Warehouse 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

AltexSoft

MAY 14, 2021

Apache Hadoop. Apache Hadoop is a set of open-source software for storing, processing, and managing Big Data developed by the Apache Software Foundation in 2006. Hadoop architecture layers. As you can see, the Hadoop ecosystem consists of many components. Source: phoenixNAP. NoSQL databases. Apache Kafka.

Big Data 78

Big Data Data Analytics IT NoSQL 78

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

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