Need advice about which tool to choose?Ask the StackShare community!
Add tool
Apache Impala vs MongoDB: What are the differences?
# Introduction
Apache Impala and MongoDB are both popular data processing technologies used in big data analytics and management. However, they differ in various aspects that make them suitable for different use cases.
1. **Data Storage Model**: Apache Impala is a SQL query engine for Apache Hadoop, which allows for real-time querying of data stored in Hadoop. On the other hand, MongoDB is a NoSQL database that stores data in JSON-like documents, providing high flexibility for unstructured data.
2. **Data Schema**: Apache Impala requires a predefined schema for data stored in Hadoop, making it suitable for structured data analysis. MongoDB, being a NoSQL database, allows for schema-less data storage, making it more adaptable for evolving data structures.
3. **Query Language**: Apache Impala uses SQL for querying data, making it easier for users familiar with SQL syntax to perform data analysis tasks. MongoDB, on the other hand, uses a rich query language that supports document-based queries, ideal for working with JSON-like data.
4. **Scalability**: Apache Impala is designed for real-time querying of data stored in Hadoop, providing horizontal scalability by adding more nodes to the cluster. MongoDB also offers horizontal scalability by sharding data across multiple nodes, making it suitable for large-scale applications.
5. **Consistency**: Apache Impala provides strong consistency guarantees for querying data in Hadoop, ensuring that users get accurate results. MongoDB, being a NoSQL database, offers eventual consistency, which may lead to discrepancies in data retrieval under certain conditions.
6. **Use Case**: Apache Impala is well-suited for interactive data analysis tasks that require real-time querying of large datasets stored in Hadoop. MongoDB, on the other hand, is often used for applications that demand flexible data structures and high availability for web and mobile applications.
In Summary, Apache Impala and MongoDB differ in their data storage model, schema requirements, query language, scalability options, consistency guarantees, and ideal use cases.
Manage your open source components, licenses, and vulnerabilities
Learn MorePros of Apache Impala
Pros of MongoDB
Pros of Apache Impala
- Super fast11
- Massively Parallel Processing1
- Load Balancing1
- Replication1
- Scalability1
- Distributed1
- High Performance1
- Open Sourse1
Pros of MongoDB
- Document-oriented storage829
- No sql594
- Ease of use554
- Fast465
- High performance410
- Free255
- Open source218
- Flexible180
- Replication & high availability145
- Easy to maintain112
- Querying42
- Easy scalability39
- Auto-sharding38
- High availability37
- Map/reduce31
- Document database27
- Easy setup25
- Full index support25
- Reliable16
- Fast in-place updates15
- Agile programming, flexible, fast14
- No database migrations12
- Easy integration with Node.Js8
- Enterprise8
- Enterprise Support6
- Great NoSQL DB5
- Support for many languages through different drivers4
- Schemaless3
- Aggregation Framework3
- Drivers support is good3
- Fast2
- Managed service2
- Easy to Scale2
- Awesome2
- Consistent2
- Good GUI1
- Acid Compliant1
Sign up to add or upvote prosMake informed product decisions
Cons of Apache Impala
Cons of MongoDB
Cons of Apache Impala
Be the first to leave a con
Cons of MongoDB
- Very slowly for connected models that require joins6
- Not acid compliant3
- Proprietary query language2
Sign up to add or upvote consMake informed product decisions
2.1K
623
1.4K
175.9K
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 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.
Need advice about which tool to choose?Ask the StackShare community!
Jobs that mention Apache Impala and MongoDB as a desired skillset
What companies use Apache Impala?
What companies use MongoDB?
What companies use Apache Impala?
What companies use MongoDB?
Manage your open source components, licenses, and vulnerabilities
Learn MoreSign up to get full access to all the companiesMake informed product decisions
What tools integrate with Apache Impala?
What tools integrate with MongoDB?
What tools integrate with Apache Impala?
Sign up to get full access to all the tool integrationsMake informed product decisions
Blog Posts
+11 2
2494
+17 16
6950
+32 17
6128
+12 9
3190
+29 14
13707
+34 29
42805
+6 14
15714
+30 27
18864
What are some alternatives to Apache Impala and MongoDB?
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.