Amazon Redshift Spectrum vs Singer

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Amazon Redshift Spectrum
Amazon Redshift Spectrum

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Singer
Singer

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Amazon Redshift Spectrum vs Singer: 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 ‚Äúdata lake‚ÄĚ -- without having to load or transform any data. On the other hand, Singer is detailed as "Simple, Composable, Open Source ETL". Singer powers data extraction and consolidation for all of your organization‚Äôs tools: advertising platforms, web analytics, payment processors, email service providers, marketing automation, databases, and more.

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

Singer is an open source tool with 178 GitHub stars and 40 GitHub forks. Here's a link to Singer'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 ‚Äúdata lake‚ÄĚ -- without having to load or transform any data.

What is Singer?

Singer powers data extraction and consolidation for all of your organization’s tools: advertising platforms, web analytics, payment processors, email service providers, marketing automation, databases, and more.
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          What are some alternatives to Amazon Redshift Spectrum and Singer?
          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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