Amazon Redshift Spectrum vs Druid vs Apache Spark

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Amazon Redshift Spectrum

94
133
+ 1
3
Druid

321
704
+ 1
29
Apache Spark

2.5K
2.9K
+ 1
132
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Pros of Amazon Redshift Spectrum
Pros of Druid
Pros of Apache Spark
  • 1
    Good Performance
  • 1
    Great Documentation
  • 1
    Economical
  • 14
    Real Time Aggregations
  • 5
    Batch and Real-Time Ingestion
  • 4
    OLAP
  • 3
    OLAP + OLTP
  • 2
    Combining stream and historical analytics
  • 1
    OLTP
  • 58
    Open-source
  • 48
    Fast and Flexible
  • 7
    One platform for every big data problem
  • 6
    Easy to install and to use
  • 6
    Great for distributed SQL like applications
  • 3
    Works well for most Datascience usecases
  • 2
    Machine learning libratimery, Streaming in real
  • 2
    In memory Computation
  • 0
    Interactive Query

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Cons of Amazon Redshift Spectrum
Cons of Druid
Cons of Apache Spark
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    • 3
      Limited sql support
    • 2
      Joins are not supported well
    • 1
      Complexity
    • 3
      Speed

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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 Druid?

    Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.

    What is 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.

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    What companies use Amazon Redshift Spectrum?
    What companies use Druid?
    What companies use Apache Spark?

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    What tools integrate with Amazon Redshift Spectrum?
    What tools integrate with Druid?
    What tools integrate with Apache Spark?

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    Blog Posts

    Dec 22 2021 at 5:41AM

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    Pinterest

    GitJenkinsKafka+7
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    MySQLKafkaApache Spark+6
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    What are some alternatives to Amazon Redshift Spectrum, Druid, and Apache Spark?
    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.
    Splunk
    It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.
    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.
    See all alternatives