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Hadoop vs Vertica: What are the differences?


In this article, we will discuss the key differences between Hadoop and Vertica, two popular technologies used for big data processing and analytics.

  1. Scalability:
  2. Hadoop: Hadoop is a distributed processing framework that can handle a massive volume of data by storing and processing it across multiple nodes in a cluster. It provides horizontal scalability, meaning more machines can be added to the cluster to handle increasing data volumes.
  3. Vertica: Vertica is a columnar database that also supports distributed processing. However, it differs from Hadoop in terms of scalability. Vertica offers vertical scalability, where a single node can be scaled vertically by adding more resources (CPU, RAM) to handle larger data volumes.

  4. Data Structure:

  5. Hadoop: Hadoop is based on the Hadoop Distributed File System (HDFS) which is designed for storing large data sets across multiple nodes. It is a file-based system and is suitable for unstructured and semi-structured data.

  6. Vertica: Vertica uses a column-oriented database structure that is optimized for analytical queries. It organizes data column by column, allowing for efficient data compression and faster query performance especially for structured data.

  7. Data Processing Paradigm:

  8. Hadoop: Hadoop follows a batch processing paradigm where data is processed in batches, typically running MapReduce jobs. This makes it suitable for offline, non-real-time processing and analysis.

  9. Vertica: Vertica, on the other hand, supports both batch processing and real-time data processing. It provides built-in support for real-time data ingestion and analytics, making it a better choice for real-time analytics and decision-making.

  10. Query Performance:

  11. Hadoop: Hadoop provides good performance for large-scale data processing tasks but is relatively slower for ad-hoc querying and real-time analytics due to the batch processing nature of MapReduce jobs.

  12. Vertica: Vertica is optimized for high-speed analytics, offering faster query performance especially for complex analytical queries. It leverages columnar storage and advanced query optimization techniques to provide near real-time responses even for ad-hoc queries.

  13. Data Storage Optimization:

  14. Hadoop: Hadoop does not offer advanced data storage optimization techniques like indexing or compression out of the box. It relies on distributed file storage and replication for data durability.

  15. Vertica: Vertica provides several storage optimization techniques like columnar compression, projection and partitioning, and indexing. These techniques help reduce storage requirements and improve query performance, making it more efficient for data storage and retrieval.

  16. Ecosystem and Tooling:

  17. Hadoop: Hadoop has a vast ecosystem with various tools and technologies built around it, such as Hive, Pig, Spark, and HBase. These tools provide additional capabilities for data processing, querying, and machine learning, enhancing the functionality and versatility of Hadoop.

  18. Vertica: Vertica has a smaller ecosystem compared to Hadoop but offers integrations with popular tools and frameworks such as Apache Kafka, Apache Spark, and AWS S3. This allows Vertica to leverage the power of these tools for data ingestion, processing, and integration.

In summary, Hadoop is a distributed processing framework suitable for big data storage and batch processing, while Vertica is a columnar database optimized for high-speed analytics and real-time data processing. Hadoop provides horizontal scalability and is suitable for unstructured data, while Vertica offers vertical scalability and excels in structured data processing.

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Pros of Hadoop
Pros of Vertica
  • 39
    Great ecosystem
  • 11
    One stack to rule them all
  • 4
    Great load balancer
  • 1
    Amazon aws
  • 1
    Java syntax
  • 3
    Shared nothing or shared everything architecture
  • 1
    Reduce costs as reduced hardware is required
  • 1
    Offers users the freedom to choose deployment mode
  • 1
    Flexible architecture suits nearly any project
  • 1
    End-to-End ML Workflow Support
  • 1
    All You Need for IoT, Clickstream or Geospatial
  • 1
    Freedom from Underlying Storage
  • 1
    Pre-Aggregation for Cubes (LAPS)
  • 1
    Automatic Data Marts (Flatten Tables)
  • 1
    Near-Real-Time Analytics in pure Column Store
  • 1
    Fully automated Database Designer tool
  • 1
    Query-Optimized Storage
  • 1
    Vertica is the only product which offers partition prun
  • 1
    Partition pruning and predicate push down on Parquet

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What is Hadoop?

The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.

What is Vertica?

It provides a best-in-class, unified analytics platform that will forever be independent from underlying infrastructure.

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MySQLKafkaApache Spark+6
Aug 28 2019 at 3:10AM


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What are some alternatives to Hadoop and Vertica?
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.
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.
Elasticsearch is a distributed, RESTful search and analytics engine capable of storing data and searching it in near real time. Elasticsearch, Kibana, Beats and Logstash are the Elastic Stack (sometimes called the ELK Stack).
It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.
Snowflake eliminates the administration and management demands of traditional data warehouses and big data platforms. Snowflake is a true data warehouse as a service running on Amazon Web Services (AWS)—no infrastructure to manage and no knobs to turn.
See all alternatives