Amazon Redshift Spectrum vs Corral

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Amazon Redshift Spectrum
Amazon Redshift Spectrum

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Corral

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Amazon Redshift Spectrum vs Corral: What are the differences?

What is Amazon Redshift Spectrum? Exabyte-Scale In-Place Queries of S3 Data. 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.

What is Corral? A serverless MapReduce framework written for AWS Lambda. Corral is a MapReduce framework designed to be deployed to serverless platforms, like AWS Lambda. It presents a lightweight alternative to Hadoop MapReduce.

Amazon Redshift Spectrum and Corral belong to "Big Data Tools" category of the tech stack.

Corral is an open source tool with 611 GitHub stars and 16 GitHub forks. Here's a link to Corral's open source repository on GitHub.

- No public GitHub repository available -

What is 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.

What is Corral?

Corral is a MapReduce framework designed to be deployed to serverless platforms, like AWS Lambda. It presents a lightweight alternative to Hadoop MapReduce.
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            What tools integrate with Amazon Redshift Spectrum?
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            What are some alternatives to Amazon Redshift Spectrum and Corral?
            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 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.
            Apache Spark
            Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
            Apache Flink
            Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.
            Apache Hive
            Hive facilitates reading, writing, and managing large datasets residing in distributed storage using SQL. Structure can be projected onto data already in storage.
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