Amazon Athena vs Druid vs Apache Spark

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Amazon Athena

500
839
+ 1
49
Druid

382
867
+ 1
32
Apache Spark

3K
3.5K
+ 1
140
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Pros of Amazon Athena
Pros of Druid
Pros of Apache Spark
  • 16
    Use SQL to analyze CSV files
  • 8
    Glue crawlers gives easy Data catalogue
  • 7
    Cheap
  • 6
    Query all my data without running servers 24x7
  • 4
    No data base servers yay
  • 3
    Easy integration with QuickSight
  • 2
    Query and analyse CSV,parquet,json files in sql
  • 2
    Also glue and athena use same data catalog
  • 1
    No configuration required
  • 0
    Ad hoc checks on data made easy
  • 15
    Real Time Aggregations
  • 6
    Batch and Real-Time Ingestion
  • 5
    OLAP
  • 3
    OLAP + OLTP
  • 2
    Combining stream and historical analytics
  • 1
    OLTP
  • 61
    Open-source
  • 48
    Fast and Flexible
  • 8
    One platform for every big data problem
  • 8
    Great for distributed SQL like applications
  • 6
    Easy to install and to use
  • 3
    Works well for most Datascience usecases
  • 2
    Interactive Query
  • 2
    Machine learning libratimery, Streaming in real
  • 2
    In memory Computation

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Cons of Amazon Athena
Cons of Druid
Cons of Apache Spark
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    • 3
      Limited sql support
    • 2
      Joins are not supported well
    • 1
      Complexity
    • 4
      Speed

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    What is 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.

    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 Athena?
    What companies use Druid?
    What companies use Apache Spark?

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    What tools integrate with Amazon Athena?
    What tools integrate with Druid?
    What tools integrate with Apache Spark?

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    Dec 22 2021 at 5:41AM

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    What are some alternatives to Amazon Athena, Druid, and Apache Spark?
    Presto
    Distributed SQL Query Engine for Big Data
    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.
    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.
    Cassandra
    Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.
    Spectrum
    The community platform for the future.
    See all alternatives