Druid vs Google Cloud Dataflow vs Apache Spark

Druid

239
476
+ 1
22
Google Cloud Dataflow

142
230
+ 1
3
Apache Spark

1.9K
2K
+ 1
122
Pros of Druid
Pros of Google Cloud Dataflow
Pros of Apache Spark
Cons of Druid
Cons of Google Cloud Dataflow
Cons of Apache Spark
    No cons available
    - No public GitHub repository available -

    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 Google Cloud Dataflow?

    Google Cloud Dataflow is a unified programming model and a managed service for developing and executing a wide range of data processing patterns including ETL, batch computation, and continuous computation. Cloud Dataflow frees you from operational tasks like resource management and performance optimization.

    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.
    What companies use Druid?
    What companies use Google Cloud Dataflow?
    What companies use Apache Spark?
    What tools integrate with Druid?
    What tools integrate with Google Cloud Dataflow?
    What tools integrate with Apache Spark?
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