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Apache Impala

145
301
+ 1
18
Vespa

11
28
+ 1
0
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Impala vs Vespa: What are the differences?

What is Impala? 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.

What is Vespa? Store, search, rank and organize big data. Vespa is an engine for low-latency computation over large data sets. It stores and indexes your data such that queries, selection and processing over the data can be performed at serving time.

Impala and Vespa can be primarily classified as "Big Data" tools.

Impala and Vespa are both open source tools. Vespa with 2.85K GitHub stars and 339 forks on GitHub appears to be more popular than Impala with 2.18K GitHub stars and 824 GitHub forks.

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Pros of Apache Impala
Pros of Vespa
  • 11
    Super fast
  • 1
    Massively Parallel Processing
  • 1
    Load Balancing
  • 1
    Replication
  • 1
    Scalability
  • 1
    Distributed
  • 1
    High Performance
  • 1
    Open Sourse
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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 Vespa?

    Vespa is an engine for low-latency computation over large data sets. It stores and indexes your data such that queries, selection and processing over the data can be performed at serving time.

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

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    What are some alternatives to Apache Impala and Vespa?
    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