Google BigQuery vs Pandasql

Overview

Google BigQuery

Stacks1.8K

Followers1.5K

Votes152

Pandasql

Stacks11

Followers51

Votes1

GitHub Stars1.4K

Forks187

Google BigQuery vs Pandasql: What are the differences?

Introduction: When comparing Google BigQuery and Pandasql, it is essential to understand the key differences to determine which tool best suits your data analysis needs.

Querying Method: Google BigQuery is a cloud-based data warehouse that allows users to run SQL-like queries on massive datasets. On the other hand, Pandasql is a Python library that enables users to write SQL queries on pandas DataFrames. The key difference lies in the fact that while Google BigQuery is designed for working with large-scale data in the cloud, Pandasql is more suited for users who prefer working within the Python ecosystem with pandas DataFrames.
Scalability: Google BigQuery is highly scalable and can handle petabyte-scale datasets with ease by leveraging Google's infrastructure. In contrast, Pandasql is limited by the memory and processing power available on the local machine. Thus, BigQuery is better suited for handling massive datasets and complex queries that exceed the capabilities of traditional databases and tools like Pandasql.
Storage: Google BigQuery stores data in a distributed, columnar format that is optimized for analytical queries. In comparison, Pandasql operates on in-memory pandas DataFrames, which are stored locally on the user's machine. This difference in storage mechanisms impacts the performance and scalability of the tools, with BigQuery offering more efficient data processing capabilities for large datasets.
Collaboration and Sharing: Google BigQuery is designed for collaborative work environments, allowing multiple users to access and query datasets concurrently. It also integrates seamlessly with other Google Cloud services for data analysis and visualization. Pandasql, on the other hand, is more suited for individual users or small teams working within the Python environment, limiting collaboration and sharing capabilities compared to BigQuery.
Cost: Google BigQuery is a cloud-based service that charges users based on the amount of data processed by queries, storage usage, and other factors. In contrast, Pandasql is an open-source library that can be used free of charge within the Python ecosystem. The difference in cost structure makes BigQuery more suitable for organizations that require cost-effective solutions for processing and analyzing large volumes of data.

In Summary, understanding the differences between Google BigQuery and Pandasql can help users choose the appropriate tool for their data analysis requirements.

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Advice on Google BigQuery, Pandasql

Julien

CTO at Hawk

Sep 19, 2020

Decided

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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Detailed Comparison

Google BigQuery	Pandasql
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.	pandasql allows you to query pandas DataFrames using SQL syntax. It works similarly to sqldf in R. pandasql seeks to provide a more familiar way of manipulating and cleaning data for people new to Python or pandas.
All behind the scenes- Your queries can execute asynchronously in the background, and can be polled for status.;Import data with ease- Bulk load your data using Google Cloud Storage or stream it in bursts of up to 1,000 rows per second.;Affordable big data- The first Terabyte of data processed each month is free.;The right interface- Separate interfaces for administration and developers will make sure that you have access to the tools you need.	-
Statistics
GitHub Stars -	GitHub Stars 1.4K
GitHub Forks -	GitHub Forks 187
Stacks 1.8K	Stacks 11
Followers 1.5K	Followers 51
Votes 152	Votes 1
Pros & Cons
Pros 28 High Performance 25 Easy to use 22 Fully managed service 19 Cheap Pricing 16 Process hundreds of GB in seconds Cons 1 You can't unit test changes in BQ data 0 Sdas	Pros 1 Super fast to handel df by sql syntax Cons 1 Its cant output boolean
Integrations
Xplenty Fluentd Looker Chartio Treasure Data	No integrations available

What are some alternatives to Google BigQuery, Pandasql?

dbForge Studio for MySQL

It is the universal MySQL and MariaDB client for database management, administration and development. With the help of this intelligent MySQL client the work with data and code has become easier and more convenient. This tool provides utilities to compare, synchronize, and backup MySQL databases with scheduling, and gives possibility to analyze and report MySQL tables data.

dbForge Studio for Oracle

It is a powerful integrated development environment (IDE) which helps Oracle SQL developers to increase PL/SQL coding speed, provides versatile data editing tools for managing in-database and external data.

dbForge Studio for PostgreSQL

It is a GUI tool for database development and management. The IDE for PostgreSQL allows users to create, develop, and execute queries, edit and adjust the code to their requirements in a convenient and user-friendly interface.

dbForge Studio for SQL Server

It is a powerful IDE for SQL Server management, administration, development, data reporting and analysis. The tool will help SQL developers to manage databases, version-control database changes in popular source control systems, speed up routine tasks, as well, as to make complex database changes.

Amazon Redshift

It is optimized for data sets ranging from a few hundred gigabytes to a petabyte or more and costs less than $1,000 per terabyte per year, a tenth the cost of most traditional data warehousing solutions.

Liquibase

Liquibase is th leading open-source tool for database schema change management. Liquibase helps teams track, version, and deploy database schema and logic changes so they can automate their database code process with their app code process.

Sequel Pro

Sequel Pro is a fast, easy-to-use Mac database management application for working with MySQL databases.

DBeaver

It is a free multi-platform database tool for developers, SQL programmers, database administrators and analysts. Supports all popular databases: MySQL, PostgreSQL, SQLite, Oracle, DB2, SQL Server, Sybase, Teradata, MongoDB, Cassandra, Redis, etc.

Qubole

Qubole is a cloud based service that makes big data easy for analysts and data engineers.

dbForge SQL Complete

It is an IntelliSense add-in for SQL Server Management Studio, designed to provide the fastest T-SQL query typing ever possible.

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Google BigQuery vs Pandasql: What are the differences?

Introduction: When comparing Google BigQuery and Pandasql, it is essential to understand the key differences to determine which tool best suits your data analysis needs.

Querying Method: Google BigQuery is a cloud-based data warehouse that allows users to run SQL-like queries on massive datasets. On the other hand, Pandasql is a Python library that enables users to write SQL queries on pandas DataFrames. The key difference lies in the fact that while Google BigQuery is designed for working with large-scale data in the cloud, Pandasql is more suited for users who prefer working within the Python ecosystem with pandas DataFrames.
Scalability: Google BigQuery is highly scalable and can handle petabyte-scale datasets with ease by leveraging Google's infrastructure. In contrast, Pandasql is limited by the memory and processing power available on the local machine. Thus, BigQuery is better suited for handling massive datasets and complex queries that exceed the capabilities of traditional databases and tools like Pandasql.
Storage: Google BigQuery stores data in a distributed, columnar format that is optimized for analytical queries. In comparison, Pandasql operates on in-memory pandas DataFrames, which are stored locally on the user's machine. This difference in storage mechanisms impacts the performance and scalability of the tools, with BigQuery offering more efficient data processing capabilities for large datasets.
Collaboration and Sharing: Google BigQuery is designed for collaborative work environments, allowing multiple users to access and query datasets concurrently. It also integrates seamlessly with other Google Cloud services for data analysis and visualization. Pandasql, on the other hand, is more suited for individual users or small teams working within the Python environment, limiting collaboration and sharing capabilities compared to BigQuery.
Cost: Google BigQuery is a cloud-based service that charges users based on the amount of data processed by queries, storage usage, and other factors. In contrast, Pandasql is an open-source library that can be used free of charge within the Python ecosystem. The difference in cost structure makes BigQuery more suitable for organizations that require cost-effective solutions for processing and analyzing large volumes of data.

In Summary, understanding the differences between Google BigQuery and Pandasql can help users choose the appropriate tool for their data analysis requirements.