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Learn MorePros of Apache Impala
Pros of MySQL
Pros of Apache Impala
- Super fast11
- Massively Parallel Processing1
- Load Balancing1
- Replication1
- Scalability1
- Distributed1
- High Performance1
- Open Sourse1
Pros of MySQL
- Sql800
- Free679
- Easy562
- Widely used528
- Open source490
- High availability180
- Cross-platform support160
- Great community104
- Secure79
- Full-text indexing and searching75
- Fast, open, available26
- Reliable16
- SSL support16
- Robust15
- Enterprise Version9
- Easy to set up on all platforms7
- NoSQL access to JSON data type3
- Relational database1
- Easy, light, scalable1
- Sequel Pro (best SQL GUI)1
- Replica Support1
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Cons of Apache Impala
Cons of MySQL
Cons of Apache Impala
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Cons of MySQL
- Owned by a company with their own agenda16
- Can't roll back schema changes3
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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 MySQL?
The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.
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What companies use Apache Impala?
What companies use MySQL?
What companies use Apache Impala?
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What tools integrate with Apache Impala?
What tools integrate with MySQL?
What tools integrate with Apache Impala?
What tools integrate with MySQL?
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What are some alternatives to Apache Impala and MySQL?
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.