Cassandra vs Google BigQuery

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Cassandra vs Google BigQuery: What are the differences?

Introduction:

Cassandra and Google BigQuery are both popular database technologies used for storing and managing large-scale data. However, there are several key differences between the two that set them apart. In this article, we will explore the main differences between Cassandra and Google BigQuery.

  1. Data Model: Cassandra is a distributed NoSQL database that follows a wide-column data model, where data is organized into tables with rows and columns. It allows flexible schema design and supports fast read and write operations. On the other hand, Google BigQuery is a fully-managed serverless data warehouse that follows a columnar data model and stores data in a highly compressed and optimized format for analytics. It is primarily built for handling large analytical queries and provides features like partitioning and clustering to optimize query performance.

  2. Storage and Scalability: Cassandra is designed for horizontal scalability and can be easily scaled across multiple commodity servers to handle massive amounts of data. It uses a distributed architecture with a masterless ring design, providing fault tolerance and high availability. In contrast, Google BigQuery is a fully-managed service provided by Google Cloud and can automatically scale to handle petabytes of data without the need for manual configuration. It utilizes Google's extensive infrastructure and resources to provide high-performance data storage and processing.

  3. Query Capabilities: Cassandra supports flexible querying through its query language CQL (Cassandra Query Language). It provides features like filtering, ordering, and aggregation but lacks support for complex analytical queries. Google BigQuery, on the other hand, is specifically designed for handling complex analytical queries on large datasets. It supports standard SQL queries and provides advanced features like window functions, partitioning, and table joins for advanced analytics.

  4. Data Consistency and Transaction Support: Cassandra provides tunable consistency levels for read and write operations, allowing developers to choose between strong or eventual consistency based on their application requirements. It also supports lightweight transactions using compare-and-set (CAS) operations. In contrast, Google BigQuery does not provide support for strong consistency or transactional operations. It is optimized for read-heavy workloads and provides eventual consistency for query results.

  5. Cost and Pricing Model: Cassandra is an open-source database system and can be deployed on-premises or in the cloud, providing flexibility in terms of cost and infrastructure. However, managing and scaling a Cassandra cluster requires expertise and additional operational effort. Google BigQuery follows a pay-per-query pricing model, where users are billed based on the amount of data processed by each query. It offers a serverless architecture, eliminating the need for managing infrastructure, but the cost can significantly increase for large-scale analytical workloads.

  6. Ecosystem and Integration: Cassandra has a rich ecosystem with support for various programming languages and frameworks. It provides client drivers for popular programming languages like Java, Python, and Node.js. It also integrates well with other open-source tools like Apache Spark and Kafka for data processing and streaming. Google BigQuery integrates seamlessly with other services on the Google Cloud Platform (GCP) and provides native connectors for popular data ingestion and visualization tools like Dataflow, Dataproc, and Looker. It also supports data transfer services for seamless data migration from other platforms.

In Summary, Cassandra and Google BigQuery differ in terms of their data model, scalability, query capabilities, consistency support, pricing model, and ecosystem integration. While Cassandra offers flexibility in schema design, scalability, and consistency, Google BigQuery is optimized for complex analytical queries and provides a fully-managed, serverless, and cost-effective solution on the Google Cloud Platform.

Advice on Cassandra and Google BigQuery
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 · 164.9K 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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Decisions about Cassandra and Google BigQuery
Julien Lafont

Cloud Data-warehouse is the centerpiece of modern Data platform. The choice of the most suitable solution is therefore fundamental.

Our benchmark was conducted over BigQuery and Snowflake. These solutions seem to match our goals but they have very different approaches.

BigQuery is notably the only 100% serverless cloud data-warehouse, which requires absolutely NO maintenance: no re-clustering, no compression, no index optimization, no storage management, no performance management. Snowflake requires to set up (paid) reclustering processes, to manage the performance allocated to each profile, etc. We can also mention Redshift, which we have eliminated because this technology requires even more ops operation.

BigQuery can therefore be set up with almost zero cost of human resources. Its on-demand pricing is particularly adapted to small workloads. 0 cost when the solution is not used, only pay for the query you're running. But quickly the use of slots (with monthly or per-minute commitment) will drastically reduce the cost of use. We've reduced by 10 the cost of our nightly batches by using flex slots.

Finally, a major advantage of BigQuery is its almost perfect integration with Google Cloud Platform services: Cloud functions, Dataflow, Data Studio, etc.

BigQuery is still evolving very quickly. The next milestone, BigQuery Omni, will allow to run queries over data stored in an external Cloud platform (Amazon S3 for example). It will be a major breakthrough in the history of cloud data-warehouses. Omni will compensate a weakness of BigQuery: transferring data in near real time from S3 to BQ is not easy today. It was even simpler to implement via Snowflake's Snowpipe solution.

We also plan to use the Machine Learning features built into BigQuery to accelerate our deployment of Data-Science-based projects. An opportunity only offered by the BigQuery solution

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Pros of Cassandra
Pros of Google BigQuery
  • 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
  • 28
    High Performance
  • 25
    Easy to use
  • 22
    Fully managed service
  • 19
    Cheap Pricing
  • 16
    Process hundreds of GB in seconds
  • 12
    Big Data
  • 11
    Full table scans in seconds, no indexes needed
  • 8
    Always on, no per-hour costs
  • 6
    Good combination with fluentd
  • 4
    Machine learning
  • 1
    Easy to manage
  • 0
    Easy to learn

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Cons of Cassandra
Cons of Google BigQuery
  • 3
    Reliability of replication
  • 1
    Size
  • 1
    Updates
  • 1
    You can't unit test changes in BQ data
  • 0
    Sdas

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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 Google BigQuery?

Run super-fast, SQL-like queries against terabytes of data in seconds, using the processing power of Google's infrastructure. Load data with ease. Bulk load your data using Google Cloud Storage or stream it in. Easy access. Access BigQuery by using a browser tool, a command-line tool, or by making calls to the BigQuery REST API with client libraries such as Java, PHP or Python.

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What companies use Cassandra?
What companies use Google BigQuery?
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What are some alternatives to Cassandra and Google BigQuery?
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