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Amazon Redshift
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Amazon Redshift vs Oracle: What are the differences?

Developers describe Amazon Redshift as "Fast, fully managed, petabyte-scale data warehouse service". Redshift makes it simple and cost-effective to efficiently analyze all your data using your existing business intelligence tools. It is optimized for datasets ranging from a few hundred gigabytes to a petabyte or more and costs less than $1,000 per terabyte per year, a tenth the cost of most traditional data warehousing solutions. On the other hand, Oracle is detailed as "An RDBMS that implements object-oriented features such as user-defined types, inheritance, and polymorphism". Oracle Database is an RDBMS. An RDBMS that implements object-oriented features such as user-defined types, inheritance, and polymorphism is called an object-relational database management system (ORDBMS). Oracle Database has extended the relational model to an object-relational model, making it possible to store complex business models in a relational database.

Amazon Redshift and Oracle are primarily classified as "Big Data as a Service" and "Databases" tools respectively.

"Data Warehousing" is the primary reason why developers consider Amazon Redshift over the competitors, whereas "Reliable" was stated as the key factor in picking Oracle.

According to the StackShare community, Amazon Redshift has a broader approval, being mentioned in 269 company stacks & 67 developers stacks; compared to Oracle, which is listed in 106 company stacks and 92 developer stacks.

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What is Amazon Redshift?

It is optimized for data sets ranging from a few hundred gigabytes to a petabyte or more and costs less than $1,000 per terabyte per year, a tenth the cost of most traditional data warehousing solutions.

What is Oracle?

Oracle Database is an RDBMS. An RDBMS that implements object-oriented features such as user-defined types, inheritance, and polymorphism is called an object-relational database management system (ORDBMS). Oracle Database has extended the relational model to an object-relational model, making it possible to store complex business models in a relational database.
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    What are some alternatives to Amazon Redshift and Oracle?
    Google BigQuery
    Run super-fast, SQL-like queries against terabytes of data in seconds, using the processing power of Google's infrastructure. Load data with ease. Bulk load your data using Google Cloud Storage or stream it in. Easy access. Access BigQuery by using a browser tool, a command-line tool, or by making calls to the BigQuery REST API with client libraries such as Java, PHP or Python.
    Amazon Athena
    Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.
    Amazon DynamoDB
    With it , you can offload the administrative burden of operating and scaling a highly available distributed database cluster, while paying a low price for only what you use.
    Amazon Redshift Spectrum
    With Redshift Spectrum, you can extend the analytic power of Amazon Redshift beyond data stored on local disks in your data warehouse to query vast amounts of unstructured data in your Amazon S3 “data lake” -- without having to load or transform any data.
    Hadoop
    The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.
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    Decisions about Amazon Redshift and Oracle
    Ankit Sobti
    Ankit Sobti
    CTO at Postman Inc · | 10 upvotes · 72.4K views
    atPostmanPostman
    dbt
    dbt
    Amazon Redshift
    Amazon Redshift
    Stitch
    Stitch
    Looker
    Looker

    Looker , Stitch , Amazon Redshift , dbt

    We recently moved our Data Analytics and Business Intelligence tooling to Looker . It's already helping us create a solid process for reusable SQL-based data modeling, with consistent definitions across the entire organizations. Looker allows us to collaboratively build these version-controlled models and push the limits of what we've traditionally been able to accomplish with analytics with a lean team.

    For Data Engineering, we're in the process of moving from maintaining our own ETL pipelines on AWS to a managed ELT system on Stitch. We're also evaluating the command line tool, dbt to manage data transformations. Our hope is that Stitch + dbt will streamline the ELT bit, allowing us to focus our energies on analyzing data, rather than managing it.

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    Interest over time
    Reviews of Amazon Redshift and Oracle
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    How developers use Amazon Redshift and Oracle
    Avatar of Olo
    Olo uses Amazon RedshiftAmazon Redshift

    Aggressive archiving of historical data to keep the production database as small as possible. Using our in-house soon-to-be-open-sourced ETL library, SharpShifter.

    Avatar of Onezino Gabriel
    Onezino Gabriel uses OracleOracle

    Gerenciamento de banco de dados utilizados por odos os serviços/aplicações criados

    Avatar of Adrian Harabulă
    Adrian Harabulă uses OracleOracle

    recommended solution at school, also used to try out alternatives to MySQL

    Avatar of Satoru Ishikawa
    Satoru Ishikawa uses OracleOracle

    データベース構成設計や実際のデータ操作など。実作業では9i, 10g, 11gを触った。

    Avatar of Christian Moeller
    Christian Moeller uses Amazon RedshiftAmazon Redshift

    Connected to BI (Pentaho)

    Avatar of Hyunwoo Shim
    Hyunwoo Shim uses OracleOracle

    Oracle을 통해 RDB를 학습하였습니다.

    Avatar of douglasresende
    douglasresende uses OracleOracle

    I'm expert database.

    Avatar of Kovid Rathee
    Kovid Rathee uses Amazon RedshiftAmazon Redshift

    OLAP and BI

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