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Cassandra vs Vertica: What are the differences?

Introduction

Cassandra and Vertica are both popular database management systems used for different purposes. However, they have key differences that set them apart from each other. This article aims to provide a concise overview of these differences.

  1. Data Model: One significant difference between Cassandra and Vertica is their data model. Cassandra is a NoSQL database that follows a wide-column data model. It is designed for scalability and high availability, allowing for massive amounts of structured, semi-structured, and unstructured data. On the other hand, Vertica is a traditional relational database that follows a columnar data model, which optimizes query performance for analytical workloads.

  2. Scalability: Cassandra and Vertica handle scalability differently. Cassandra is known for its linear scalability, allowing it to handle large amounts of data across nodes in a distributed environment. It achieves this through its masterless architecture, where there is no central coordinator that becomes a bottleneck. In contrast, Vertica has a shared-nothing architecture, which utilizes a cluster of interconnected nodes that distribute and parallelize the workload for high-performance analytics.

  3. Consistency Model: Another key difference lies in the consistency model offered by Cassandra and Vertica. Cassandra follows a tunable consistency model, providing flexibility in balancing consistency and availability. It offers consistency levels ranging from strong consistency (quorum-based) to eventual consistency. On the contrary, Vertica ensures strong consistency within a single transaction, maintaining ACID (Atomicity, Consistency, Isolation, Durability) properties traditionally associated with relational databases.

  4. Query Language: Cassandra and Vertica have different query languages. Cassandra uses Cassandra Query Language (CQL), which is similar to traditional SQL but specifically tailored for the Cassandra database. It supports CQL version 3, providing features like flexible data types, lightweight transactions, and secondary indexes. Vertica, being a relational database, supports SQL for querying and manipulating data, with additional optimizations for analytics and data processing.

  5. Workload Types: Cassandra and Vertica are optimized for different workload types. Cassandra is designed for high write throughput and can handle real-time applications that require low-latency data access. It excels in use cases such as time-series data, IoT (Internet of Things) data, and high-volume event logging. On the other hand, Vertica is built for analytics workloads and is often used for business intelligence, data warehousing, and advanced analytics tasks that involve complex queries and aggregations on large datasets.

  6. Data Replication: Cassandra and Vertica have different approaches to data replication. Cassandra utilizes a distributed architecture with peer-to-peer replication, ensuring high availability and fault tolerance. It replicates data across multiple nodes using techniques like virtual nodes and consistent hashing. In contrast, Vertica supports data replication through its own replication strategy, where it replicates data to multiple nodes for redundancy and disaster recovery.

In summary, Cassandra and Vertica differ in their data models, scalability approaches, consistency models, query languages, workload optimizations, and data replication strategies. These differences make them suitable for different use cases and highlight the specific strengths of each database management system.

Advice on Cassandra and Vertica
Vinay Mehta
Needs advice
on
CassandraCassandra
and
ScyllaDBScyllaDB

The problem I have is - we need to process & change(update/insert) 55M Data every 2 min and this updated data to be available for Rest API for Filtering / Selection. Response time for Rest API should be less than 1 sec.

The most important factors for me are processing and storing time of 2 min. There need to be 2 views of Data One is for Selection & 2. Changed data.

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Replies (4)
Recommends
on
ScyllaDBScyllaDB

Scylla can handle 1M/s events with a simple data model quite easily. The api to query is CQL, we have REST api but that's for control/monitoring

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Alex Peake
Recommends
on
CassandraCassandra

Cassandra is quite capable of the task, in a highly available way, given appropriate scaling of the system. Remember that updates are only inserts, and that efficient retrieval is only by key (which can be a complex key). Talking of keys, make sure that the keys are well distributed.

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Recommends
on
ScyllaDBScyllaDB

By 55M do you mean 55 million entity changes per 2 minutes? It is relatively high, means almost 460k per second. If I had to choose between Scylla or Cassandra, I would opt for Scylla as it is promising better performance for simple operations. However, maybe it would be worth to consider yet another alternative technology. Take into consideration required consistency, reliability and high availability and you may realize that there are more suitable once. Rest API should not be the main driver, because you can always develop the API yourself, if not supported by given technology.

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Pankaj Soni
Chief Technical Officer at Software Joint · | 2 upvotes · 152.1K views
Recommends
on
CassandraCassandra

i love syclla for pet projects however it's license which is based on server model is an issue. thus i recommend cassandra

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Pros of Cassandra
Pros of Vertica
  • 119
    Distributed
  • 98
    High performance
  • 81
    High availability
  • 74
    Easy scalability
  • 53
    Replication
  • 26
    Reliable
  • 26
    Multi datacenter deployments
  • 10
    Schema optional
  • 9
    OLTP
  • 8
    Open source
  • 2
    Workload separation (via MDC)
  • 1
    Fast
  • 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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Cons of Cassandra
Cons of Vertica
  • 3
    Reliability of replication
  • 1
    Size
  • 1
    Updates
    Be the first to leave a con

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    - No public GitHub repository available -

    What is 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.

    What is Vertica?

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

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    What companies use Cassandra?
    What companies use Vertica?
    See which teams inside your own company are using Cassandra or Vertica.
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    What tools integrate with Vertica?

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    What are some alternatives to Cassandra and Vertica?
    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.
    Google Cloud Bigtable
    Google Cloud Bigtable offers you a fast, fully managed, massively scalable NoSQL database service that's ideal for web, mobile, and Internet of Things applications requiring terabytes to petabytes of data. Unlike comparable market offerings, Cloud Bigtable doesn't require you to sacrifice speed, scale, or cost efficiency when your applications grow. Cloud Bigtable has been battle-tested at Google for more than 10 years—it's the database driving major applications such as Google Analytics and Gmail.
    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.
    Redis
    Redis is an open source (BSD licensed), in-memory data structure store, used as a database, cache, and message broker. Redis provides data structures such as strings, hashes, lists, sets, sorted sets with range queries, bitmaps, hyperloglogs, geospatial indexes, and streams.
    Couchbase
    Developed as an alternative to traditionally inflexible SQL databases, the Couchbase NoSQL database is built on an open source foundation and architected to help developers solve real-world problems and meet high scalability demands.
    See all alternatives