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  1. Stackups
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  5. Apache Impala vs Oracle

Apache Impala vs Oracle

OverviewComparisonAlternatives

Overview

Apache Impala
Apache Impala
Stacks145
Followers301
Votes18
GitHub Stars34
Forks33
Oracle
Oracle
Stacks2.6K
Followers1.8K
Votes113

Apache Impala vs Oracle: What are the differences?

Introduction

Apache Impala and Oracle are two popular database management systems used for processing and analyzing large volumes of data. Although they serve similar purposes, there are key differences between the two.

  1. Architecture: Apache Impala is designed as a massively parallel processing (MPP) analytical database engine, utilizing a distributed processing approach. On the other hand, Oracle follows a shared-disk architecture, where multiple nodes access and share the same disk storage.

  2. Performance: Impala is specifically optimized for running queries on large datasets in a real-time manner, making it exceptionally fast for analytical workloads. Oracle, on the other hand, is a comprehensive database management system that encompasses various functionalities, including online transaction processing (OLTP), which can slightly impact its analytical processing performance.

  3. Scalability: Impala is highly scalable, allowing users to add more nodes to a cluster to handle increasing workloads seamlessly. Oracle also offers scalability, but its shared-disk architecture introduces some limitations in terms of scaling for large analytical workloads.

  4. Data Types and SQL Functions: Oracle provides comprehensive support for different data types and a wide range of SQL functions. Impala, while offering a significant set of data types and SQL functions, may not have the same level of completeness and compatibility as Oracle.

  5. Cost: Oracle is a commercial database management system that requires licensing and often involves significant upfront costs. Apache Impala, on the other hand, is an open-source project under the Apache Software Foundation, making it a cost-effective option for organizations looking to minimize expenses.

  6. Ecosystem and Integration: Oracle has a mature ecosystem and extensive integration capabilities with other Oracle products, making it suitable for organizations heavily invested in Oracle technologies. Impala, being part of the Apache Hadoop ecosystem, provides seamless integration with various Hadoop tools like HDFS, Hive, and HBase, enabling organizations to leverage their existing big data infrastructure.

In summary, Apache Impala and Oracle differ in architecture, performance, scalability, available data types and SQL functions, cost, and ecosystem/integration capabilities.

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Detailed Comparison

Apache Impala
Apache Impala
Oracle
Oracle

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.

Oracle Database is an RDBMS. An RDBMS that implements object-oriented features such as user-defined types, inheritance, and polymorphism is called an object-relational database management system (ORDBMS). Oracle Database has extended the relational model to an object-relational model, making it possible to store complex business models in a relational database.

Do BI-style Queries on Hadoop;Unify Your Infrastructure;Implement Quickly;Count on Enterprise-class Security;Retain Freedom from Lock-in;Expand the Hadoop User-verse
-
Statistics
GitHub Stars
34
GitHub Stars
-
GitHub Forks
33
GitHub Forks
-
Stacks
145
Stacks
2.6K
Followers
301
Followers
1.8K
Votes
18
Votes
113
Pros & Cons
Pros
  • 11
    Super fast
  • 1
    Load Balancing
  • 1
    High Performance
  • 1
    Distributed
  • 1
    Scalability
Pros
  • 44
    Reliable
  • 33
    Enterprise
  • 15
    High Availability
  • 5
    Hard to maintain
  • 5
    Expensive
Cons
  • 14
    Expensive
Integrations
Hadoop
Hadoop
Mode
Mode
Redash
Redash
Apache Kudu
Apache Kudu
No integrations available

What are some alternatives to Apache Impala, Oracle?

MongoDB

MongoDB

MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.

MySQL

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.

PostgreSQL

PostgreSQL

PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions.

Microsoft SQL Server

Microsoft SQL Server

Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.

SQLite

SQLite

SQLite is an embedded SQL database engine. Unlike most other SQL databases, SQLite does not have a separate server process. SQLite reads and writes directly to ordinary disk files. A complete SQL database with multiple tables, indices, triggers, and views, is contained in a single disk file.

Cassandra

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.

Memcached

Memcached

Memcached is an in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls, or page rendering.

MariaDB

MariaDB

Started by core members of the original MySQL team, MariaDB actively works with outside developers to deliver the most featureful, stable, and sanely licensed open SQL server in the industry. MariaDB is designed as a drop-in replacement of MySQL(R) with more features, new storage engines, fewer bugs, and better performance.

RethinkDB

RethinkDB

RethinkDB is built to store JSON documents, and scale to multiple machines with very little effort. It has a pleasant query language that supports really useful queries like table joins and group by, and is easy to setup and learn.

ArangoDB

ArangoDB

A distributed free and open-source database with a flexible data model for documents, graphs, and key-values. Build high performance applications using a convenient SQL-like query language or JavaScript extensions.

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