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Apache Impala

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301
+ 1
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Singer

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+ 1
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Impala vs Singer: What are the differences?

Developers describe Impala as "Real-time Query for Hadoop". Impala is a modern, open source, MPP SQL query engine for Apache Hadoop. Impala is shipped by Cloudera, MapR, and Amazon. With Impala, you can query data, whether stored in HDFS or Apache HBase – including SELECT, JOIN, and aggregate functions – in real time. On the other hand, Singer is detailed as "Simple, Composable, Open Source ETL". 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.

Impala and Singer can be categorized as "Big Data" tools.

Impala and Singer are both open source tools. It seems that Impala with 2.18K GitHub stars and 824 forks on GitHub has more adoption than Singer with 178 GitHub stars and 40 GitHub forks.

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Pros of Apache Impala
Pros of Singer
  • 11
    Super fast
  • 1
    Massively Parallel Processing
  • 1
    Load Balancing
  • 1
    Replication
  • 1
    Scalability
  • 1
    Distributed
  • 1
    High Performance
  • 1
    Open Sourse
  • 1
    Multiple inputs "taps"
  • 1
    Open source

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What is Apache Impala?

Impala is a modern, open source, MPP SQL query engine for Apache Hadoop. Impala is shipped by Cloudera, MapR, and Amazon. With Impala, you can query data, whether stored in HDFS or Apache HBase – including SELECT, JOIN, and aggregate functions – in real time.

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 companies use Apache Impala?
What companies use Singer?
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What tools integrate with Apache Impala?
What tools integrate with Singer?

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What are some alternatives to Apache Impala and Singer?
Presto
Distributed SQL Query Engine for Big Data
Apache Drill
Apache Drill is a distributed MPP query layer that supports SQL and alternative query languages against NoSQL and Hadoop data storage systems. It was inspired in part by Google's Dremel.
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.
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.
HBase
Apache HBase is an open-source, distributed, versioned, column-oriented store modeled after Google' Bigtable: A Distributed Storage System for Structured Data by Chang et al. Just as Bigtable leverages the distributed data storage provided by the Google File System, HBase provides Bigtable-like capabilities on top of Apache Hadoop.
See all alternatives