Amazon Redshift Spectrum vs AWS Glue

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Amazon Redshift Spectrum
Amazon Redshift Spectrum

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Amazon Redshift Spectrum vs AWS Glue: What are the differences?

Developers describe Amazon Redshift Spectrum as "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. On the other hand, AWS Glue is detailed as "Fully managed extract, transform, and load (ETL) service". A fully managed extract, transform, and load (ETL) service that makes it easy for customers to prepare and load their data for analytics.

Amazon Redshift Spectrum and AWS Glue can be primarily classified as "Big Data" tools.

According to the StackShare community, AWS Glue has a broader approval, being mentioned in 13 company stacks & 7 developers stacks; compared to Amazon Redshift Spectrum, which is listed in 5 company stacks and 4 developer stacks.

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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 AWS Glue?

A fully managed extract, transform, and load (ETL) service that makes it easy for customers to prepare and load their data for analytics.
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          What are some alternatives to Amazon Redshift Spectrum and AWS Glue?
          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
          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.
          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.
          Presto
          Presto is an open source distributed SQL query engine for running interactive analytic queries against data sources of all sizes ranging from gigabytes to petabytes.
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