Amazon Redshift Spectrum vs BDS

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Amazon Redshift Spectrum
Amazon Redshift Spectrum

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BDS

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Amazon Redshift Spectrum vs BDS: What are the differences?

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; BDS: *Blockchain data parsing and persisting results *. It is a realtime data aggregating, analyzing and visualization service for chain-like unstructured data from all kinds of 3rd party Blockchains.

Amazon Redshift Spectrum can be classified as a tool in the "Big Data Tools" category, while BDS is grouped under "Blockchain".

BDS is an open source tool with 324 GitHub stars and 42 GitHub forks. Here's a link to BDS's open source repository on GitHub.

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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 BDS?

It is a realtime data aggregating, analyzing and visualization service for chain-like unstructured data from all kinds of 3rd party Blockchains.
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            What are some alternatives to Amazon Redshift Spectrum and BDS?
            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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