Software Alternatives, Accelerators & Startups
Register | Login

Apache Calcite VS Google BigQuery

Compare Apache Calcite VS Google BigQuery and see what are their differences

ReleasePad
Changelog software that turns GitHub commits into release notes your users actually see — inside your app. Built for founders and teams who ship fast. sponsored
Note: These products don't have any matching categories. If you think this is a mistake, please edit the details of one of the products and suggest appropriate categories.
Contents:
  1. » Base Details
  2. » Videos
  3. » Reviews
  4. » Alternatives

Apache Calcite logo Apache Calcite

Relational Databases

Google BigQuery logo Google BigQuery

A fully managed data warehouse for large-scale data analytics.
  • Apache Calcite Landing page
    Landing page //
    2022-04-30
  • Google BigQuery Landing page
    Landing page //
    2023-10-03

Apache Calcite features and specs

  • Query Optimization
    Calcite provides advanced query planning and optimization features, allowing for efficient execution of SQL queries across different data sources.
  • Extensibility
    The framework is highly extensible, allowing users to add custom rules and support for additional languages and data stores.
  • Support for Multiple Data Sources
    Apache Calcite can integrate with a wide range of data sources, providing a unified query interface where users can query from different systems using standard SQL.
  • Community and Open Source
    As part of the Apache Software Foundation, Calcite benefits from a vibrant open-source community that continuously improves and expands its capabilities.

Possible disadvantages of Apache Calcite

  • Complexity
    The system can be complex to set up and configure, especially for users who are not familiar with query processing infrastructure.
  • Limited Direct Data Storage
    Calcite itself does not store or manage data; it acts as an intermediary layer, which may limit its use for those looking for a standalone database solution.
  • Learning Curve
    The rich set of features and customizations can lead to a steep learning curve, requiring users to invest time to fully understand and utilize its capabilities.
  • Performance Overhead
    Given that Calcite introduces an additional layer between the application and data storage, there might be performance overheads in certain use cases.

Google BigQuery features and specs

  • Scalability
    BigQuery can effortlessly scale to handle large volumes of data due to its serverless architecture, thereby reducing the operational overhead of managing infrastructure.
  • Speed
    It leverages Google's infrastructure to provide high-speed data processing, making it possible to run complex queries on massive datasets in a matter of seconds.
  • Integrations
    BigQuery easily integrates with various Google Cloud Platform services, as well as other popular data tools like Looker, Tableau, and Power BI.
  • Automatic Optimization
    Features like automatic data partitioning and clustering help to optimize query performance without requiring manual tuning.
  • Security
    BigQuery provides robust security features including IAM roles, customer-managed encryption keys, and detailed audit logging.
  • Cost Efficiency
    The pricing model is based on the amount of data processed, which can be cost-effective for many use cases when compared to traditional data warehouses.
  • Managed Service
    Being fully managed, BigQuery takes care of database administration tasks such as scaling, backups, and patch management, allowing users to focus on their data and queries.

Possible disadvantages of Google BigQuery

  • Cost Predictability
    While the pay-per-use model can be cost-efficient, it can also make cost forecasting difficult. Unexpected large queries could lead to higher-than-anticipated costs.
  • Complexity
    The learning curve can be steep for those who are not already familiar with SQL or Google Cloud Platform, potentially requiring training and education.
  • Limited Updates
    BigQuery is optimized for read-heavy operations, and it can be less efficient for scenarios that require frequent updates or deletions of data.
  • Query Pricing
    Costs are based on the amount of data processed by each query, which may not be suitable for use cases that require frequent analysis of large datasets.
  • Data Transfer Costs
    While internal data movement within Google Cloud can be cost-effective, transferring data to or from other services or on-premises systems can incur additional costs.
  • Dependency on Google Cloud
    Organizations heavily invested in multi-cloud or hybrid-cloud strategies may find the dependency on Google Cloud limiting.
  • Cold Data Performance
    Query performance might be slower for so-called 'cold data,' or data that has not been queried recently, affecting the responsiveness for some workloads.

Analysis of Google BigQuery

Overall verdict

  • Google BigQuery is a powerful and flexible data warehouse solution that suits a wide range of data analytics needs. Its ability to handle large volumes of data quickly makes it a preferred choice for organizations looking to leverage their data effectively.

Why this product is good

  • Google BigQuery is a fully-managed data warehouse that simplifies the analysis of large datasets. It is known for its scalability, speed, and integration with other Google Cloud services. It supports standard SQL, has built-in machine learning capabilities, and allows for seamless data integration from various sources. The serverless architecture means that users don't need to worry about infrastructure management, and its pay-as-you-go model provides cost efficiency.

Recommended for

  • Businesses requiring fast processing of large datasets
  • Organizations that already utilize Google Cloud services
  • Companies looking for a cost-effective, scalable analytics solution
  • Teams interested in using SQL for data analysis
  • Data scientists integrating machine learning with their data workflows

Apache Calcite videos

+ Add

The Evolution of Apache Calcite and its Community - A Discussion with Julian Hyde

More videos:

  • Review - Building modern SQL query optimizers with Apache Calcite - Vladimir Ozerov

Google BigQuery videos

+ Add

Cloud Dataprep Tutorial - Getting Started 101

More videos:

  • Review - Advanced Data Cleanup Techniques using Cloud Dataprep (Cloud Next '19)
  • Demo - Google Cloud Dataprep Premium product demo

Category Popularity

0-100% (relative to Apache Calcite and Google BigQuery)

Apache Calcite

Google BigQuery

Databases
100 100%
Databases
0% 0
Data Dashboard
0 0%
Data Dashboard
100% 100
Relational Databases
100 100%
Relational Databases
0% 0
Big Data
0 0%
Big Data
100% 100

User comments

Share your experience with using Apache Calcite and Google BigQuery. For example, how are they different and which one is better?
Log in or Post with

Reviews

These are some of the external sources and on-site user reviews we've used to compare Apache Calcite and Google BigQuery

Apache Calcite Reviews

We have no reviews of Apache Calcite yet.
Be the first one to post

Google BigQuery Reviews

Database for Data Analytics
Processing typeDescriptionUse casesCommon databasesProcessing typesProcesses data in scheduled intervals (hours, days). High-latency but cost-efficient for large datasets.Financial reporting, trend analysis, historical analyticsSnowflake, Amazon Redshift, Google BigQueryContinuously ingests and processes data with minimal latency for real-time decision-making.Fraud...
Source: blog.devart.com
Data Warehouse Tools
Google BigQuery: Similar to Snowflake, BigQuery offers a pay-per-use model with separate charges for storage and queries. Storage costs start around $0.01 per GB per month, while on-demand queries are billed at $5 per TB processed.
Source: peliqan.io
Top 6 Cloud Data Warehouses in 2023
You can also use BigQuery’s columnar and ANSI SQL databases to analyze petabytes of data at a fast speed. Its capabilities extend enough to accommodate spatial analysis using SQL and BigQuery GIS. Also, you can quickly create and run machine learning (ML) models on semi or large-scale structured data using simple SQL and BigQuery ML. Also, enjoy a real-time interactive...
Source: geekflare.com
Top 5 Cloud Data Warehouses in 2023
Google BigQuery is an incredible platform for enterprises that want to run complex analytical queries or “heavy” queries that operate using a large set of data. This means it’s not ideal for running queries that are doing simple filtering or aggregation. So if your cloud data warehousing needs lightning-fast performance on a big set of data, Google BigQuery might be a great...
Source: www.shipyardapp.com
Top 5 BigQuery Alternatives: A Challenge of Complexity
BigQuery's emergence as an attractive analytics and data warehouse platform was a significant win, helping to drive a 45% increase in Google Cloud revenue in the last quarter. The company plans to maintain this momentum by focusing on a multi-cloud future where BigQuery advances the cause of democratized analytics.
Source: blog.panoply.io

Social recommendations and mentions

Based on our record, Google BigQuery should be more popular than Apache Calcite. It has been mentiond 47 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Apache Calcite mentions (12)

  • Data diffs: Algorithms for explaining what changed in a dataset (2022)
    > Make diff work on more than just SQLite. Another way of doing this that I've been wanting to do for a while is to implement the DIFF operator in Apache Calcite[0]. Using Calcite, DIFF could be implemented as rewrite rules to generate the appropriate SQL to be directly executed against the database or the DIFF operator can be implemented outside of the database (which the original paper shows is more efficient).... - Source: Hacker News / about 3 years ago
  • How to manipulate SQL string programmatically?
    Use a SQL Parser like sqlglot or Apache Calcite to compile user's query into an AST. Source: over 3 years ago
  • Parsing SQL
    One parser I think deserves a mention is the one from Apache Calcite[0]. Calcite does more than parsing, there are a number of users who pick up Calcite just for the parser. While the default parser attempts to adhere strictly to the SQL standard, of interest is also the Babel parser, which aims to be as permissive as possible in accepting different dialects of SQL. Disclaimer: I am on the PMC of Apache Calcite,... - Source: Hacker News / about 4 years ago
  • Semantic Diff for SQL
    Apache Calcite can do this, though it's not a beginner-friendly task: https://calcite.apache.org/. - Source: Hacker News / about 4 years ago
  • OctoSQL allows you to join data from different sources using SQL
    You should look at Apache Calcite[0]. Like OctoSQL, you can join data from different data sources. It's also relatively easy to add your own data sources ("adapters" in Calcite lingo) and rules to efficiently query those sources. Calcite already has adapters that do things like read from HTML tables over HTTP, files on your file system, running processes, etc. This is in addition to connecting to a bunch of... - Source: Hacker News / about 4 years ago
View more

Google BigQuery mentions (47)

  • Ruby on Rails Performance: 7 Lessons from Scaling FirstPromoter
    We migrated the analytics layer to Google BigQuery. Same queries that timed out in PostgreSQL now run in under 2 seconds. But not everything belongs in BigQuery — we initially moved too aggressively and actually reverted some queries back when the added complexity wasn't justified. Our rule of thumb: if a query scans hundreds of thousands of rows or involves complex time-series aggregations, BigQuery. Everything... - Source: dev.to / 5 months ago
  • How to Analyze 47 Million Hacker News Posts: A Data Scientist's Dream Dataset Just Got Better
    Google BigQuery - For large-scale data processing and SQL-based analysis. - Source: dev.to / 6 months ago
  • What if ML pipelines had a lock file?
    Data Pipelines usually read from tables that change over time. Most of these tables are stored in a data warehouse like Amazon Redshift or Google BigQuery. Rows are added or removed. Backfills happen. A column gets renamed or its meaning changes. Even when teams snapshot data, those snapshots are often implicit, not recorded as part of the pipeline run itself. - Source: dev.to / 7 months ago
  • Best SQL Courses with Certificates for 2026
    SQL endures because it's the non-negotiable interface for relational data. Enterprise data storage still relies heavily on relational databases despite new alternatives. What makes SQL valuable for learners is transferability—while dialects differ across PostgreSQL, SQL Server, and BigQuery, the fundamentals stay consistent. - Source: dev.to / 9 months ago
  • Why Your Snowflake Bill is High and How to Fix It with a Hybrid Approach
    Within classic cloud data warehouses, Google BigQuery presents a different pricing model. Its on-demand, per-terabyte-scanned pricing can be cost-effective for sporadic forensic queries. But it carries the risk of a runaway query where a single mistake leads to a massive bill. - Source: dev.to / 10 months ago
View more

What are some alternatives?

When comparing Apache Calcite and Google BigQuery, you can also consider the following products

Apache Drill - Schema-Free SQL Query Engine for Hadoop and NoSQL

Databricks - Databricks provides a Unified Analytics Platform that accelerates innovation by unifying data science, engineering and business.‎What is Apache Spark?

Microsoft SQL - Microsoft SQL is a best in class relational database management software that facilitates the database server to provide you a primary function to store and retrieve data.

Looker - Looker makes it easy for analysts to create and curate custom data experiences—so everyone in the business can explore the data that matters to them, in the context that makes it truly meaningful.

MySQL - The world's most popular open source database

Jupyter - Project Jupyter exists to develop open-source software, open-standards, and services for interactive computing across dozens of programming languages. Ready to get started? Try it in your browser Install the Notebook.

Loading...