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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.
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.
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.
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.
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.
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.
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.
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.
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
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.
i love syclla for pet projects however it's license which is based on server model is an issue. thus i recommend cassandra
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.
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
Pros of Cassandra
- Distributed119
- High performance98
- High availability81
- Easy scalability74
- Replication53
- Reliable26
- Multi datacenter deployments26
- Schema optional10
- OLTP9
- Open source8
- Workload separation (via MDC)2
- Fast1
Pros of Google BigQuery
- High Performance28
- Easy to use25
- Fully managed service22
- Cheap Pricing19
- Process hundreds of GB in seconds16
- Big Data12
- Full table scans in seconds, no indexes needed11
- Always on, no per-hour costs8
- Good combination with fluentd6
- Machine learning4
- Easy to manage1
- Easy to learn0
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Cons of Cassandra
- Reliability of replication3
- Size1
- Updates1
Cons of Google BigQuery
- You can't unit test changes in BQ data1
- Sdas0