Amazon Redshift vs Hadoop

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

What is Amazon Redshift? 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.

What is Hadoop? Open-source software for reliable, scalable, distributed computing. 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.

Amazon Redshift belongs to "Big Data as a Service" category of the tech stack, while Hadoop can be primarily classified under "Databases".

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

Hadoop is an open source tool with 9.27K GitHub stars and 5.78K GitHub forks. Here's a link to Hadoop's open source repository on GitHub.

Airbnb, Uber Technologies, and Spotify are some of the popular companies that use Hadoop, whereas Amazon Redshift is used by Lyft, Coursera, and 9GAG. Hadoop has a broader approval, being mentioned in 237 company stacks & 127 developers stacks; compared to Amazon Redshift, which is listed in 270 company stacks and 68 developer stacks.

- No public GitHub repository available -

What is Amazon Redshift?

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.

What is 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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      What are some alternatives to Amazon Redshift and Hadoop?
      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
      All data items are stored on Solid State Drives (SSDs), and are replicated across 3 Availability Zones for high availability and durability. With DynamoDB, 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.
      Amazon EMR
      Amazon EMR is used in a variety of applications, including log analysis, web indexing, data warehousing, machine learning, financial analysis, scientific simulation, and bioinformatics. Customers launch millions of Amazon EMR clusters every year.
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      What tools integrate with Amazon Redshift?
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          How developers use Amazon Redshift and Hadoop
          Avatar of Pinterest
          Pinterest uses HadoopHadoop

          The MapReduce workflow starts to process experiment data nightly when data of the previous day is copied over from Kafka. At this time, all the raw log requests are transformed into meaningful experiment results and in-depth analysis. To populate experiment data for the dashboard, we have around 50 jobs running to do all the calculations and transforms of data.

          Avatar of Yelp
          Yelp uses HadoopHadoop

          in 2009 we open sourced mrjob, which allows any engineer to write a MapReduce job without contending for resources. We’re only limited by the amount of machines in an Amazon data center (which is an issue we’ve rarely encountered).

          Avatar of Pinterest
          Pinterest uses HadoopHadoop

          The massive volume of discovery data that powers Pinterest and enables people to save Pins, create boards and follow other users, is generated through daily Hadoop jobs...

          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 Robert Brown
          Robert Brown uses HadoopHadoop

          Importing/Exporting data, interpreting results. Possible integration with SAS

          Avatar of Rohith Nandakumar
          Rohith Nandakumar uses HadoopHadoop

          TBD. Good to have I think. Analytics on loads of data, recommendations?

          Avatar of Christian Moeller
          Christian Moeller uses Amazon RedshiftAmazon Redshift

          Connected to BI (Pentaho)

          Avatar of Kovid Rathee
          Kovid Rathee uses Amazon RedshiftAmazon Redshift

          OLAP and BI

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