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Cassandra vs Snowflake: What are the differences?

Introduction

Cassandra and Snowflake are both popular databases used for storing and processing data, but they have some key differences in their architecture and use cases.

  1. Data Model: Cassandra is a NoSQL database that uses a columnar data model, allowing for flexible schema and efficient write operations. On the other hand, Snowflake is a relational database that follows the traditional relational model with tables, rows, and columns.

  2. Scalability: Cassandra is designed for high scalability and distributed architecture, making it suitable for handling large amounts of data and high write and read loads. Snowflake, on the other hand, provides elasticity by automatically scaling up or down compute resources as needed, which is more suitable for ad-hoc querying and analytics workloads.

  3. Data Processing: Cassandra is optimized for fast write operations and can handle real-time data ingestion and high-speed data writes. It is well-suited for use cases requiring low-latency data updates. Snowflake, on the other hand, excels in complex analytics and reporting scenarios, providing advanced SQL querying capabilities and support for joining and aggregating large datasets.

  4. Data Consistency: Cassandra offers tunable consistency, allowing users to choose between eventual consistency or strong consistency levels based on their requirements. Snowflake provides strong consistency guarantees, ensuring that all queries see the most recent data.

  5. Query Language: Cassandra uses CQL (Cassandra Query Language), which is a SQL-like language. It also provides a limited set of predefined functions and does not support complex joins or transactions. Snowflake uses standard SQL for querying data and supports advanced SQL features like window functions, subqueries, and complex joins.

  6. Data Storage: Cassandra stores data in a distributed fashion across multiple nodes, ensuring high availability and fault tolerance. It uses a peer-to-peer gossip protocol for communication between nodes. Snowflake, on the other hand, uses a shared virtual warehouse architecture and separates storage from compute, allowing for independent scaling of storage and compute resources.

In Summary, Cassandra is a scalable NoSQL database optimized for fast writes and low-latency data updates, while Snowflake is a relational database designed for complex analytics and reporting workloads with automatic scaling capabilities.

Advice on Cassandra and Snowflake
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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Pankaj Soni
Chief Technical Officer at Software Joint · | 2 upvotes · 162K 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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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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Pros of Cassandra
Pros of Snowflake
  • 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
  • 7
    Public and Private Data Sharing
  • 4
    Multicloud
  • 4
    Good Performance
  • 4
    User Friendly
  • 3
    Great Documentation
  • 2
    Serverless
  • 1
    Economical
  • 1
    Usage based billing
  • 1
    Innovative

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Cons of Cassandra
Cons of Snowflake
  • 3
    Reliability of replication
  • 1
    Size
  • 1
    Updates
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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 Snowflake?

    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.

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    What companies use Cassandra?
    What companies use Snowflake?
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    Jul 2 2019 at 9:34PM

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    What are some alternatives to Cassandra and Snowflake?
    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