Overview

Google BigQuery

Stacks1.7K

Followers1.5K

Votes152

Knex.js

Stacks184

Followers406

Votes49

Google BigQuery vs Knex.js: What are the differences?

Introduction: Google BigQuery and Knex.js are two popular database tools that serve different purposes in the development process. Below are key differences between the two.

Purpose: Google BigQuery is a fully managed, serverless data warehouse offering powerful data analysis and SQL queries at blazing speed, ideal for analyzing large datasets and performing complex queries. On the other hand, Knex.js is a SQL query builder for Node.js, used to facilitate the creation and execution of SQL queries across a variety of databases, making it more suitable for smaller scale applications and projects.
Scalability: Google BigQuery is designed to handle massive amounts of data efficiently and can easily scale to petabytes of data, making it suitable for enterprise-level solutions requiring high scalability and performance. Knex.js, on the other hand, may struggle to handle extremely large datasets or complex queries as efficiently as Google BigQuery due to its primary focus on simplifying SQL query building tasks for smaller applications.
Managed vs Local: Google BigQuery is a fully managed service provided by Google Cloud Platform, taking care of infrastructure, updates, and maintenance, allowing users to focus solely on their data analysis tasks. In contrast, Knex.js needs to be set up and managed within the application itself, requiring developers to handle database connections, query executions, and performance optimizations manually.
SQL Syntax: While both Google BigQuery and Knex.js support SQL queries, the syntax and capabilities may vary. Google BigQuery supports standard SQL syntax along with some extensions for working with large datasets, whereas Knex.js provides a fluent interface for building SQL queries in JavaScript, which may differ slightly from traditional SQL syntax.
Cost: Google BigQuery is a paid service, and users are billed based on the amount of data processed and storage used, making it more suitable for businesses with a budget for data analysis and storage. Knex.js, being an open-source library, incurs no additional costs for usage, except for the time and resources needed for implementation and maintenance.
Integration: Google BigQuery seamlessly integrates with various Google Cloud Platform services, allowing for easy data storage, analysis, and visualization within the GCP ecosystem. On the other hand, Knex.js can be integrated with multiple databases and platforms, providing more flexibility in the choice of database technology and hosting environment.

In Summary, Google BigQuery is ideal for handling large-scale data analytics and complex queries with high scalability within a managed environment, while Knex.js is better suited for smaller applications needing SQL query building capabilities without the need for external database services.

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Advice on Google BigQuery, Knex.js

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	Knex.js
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.	Knex.js is a "batteries included" SQL query builder for Postgres, MySQL, MariaDB, SQLite3, and Oracle designed to be flexible, portable, and fun to use. It features both traditional node style callbacks as well as a promise interface for cleaner async flow control, a stream interface, full featured query and schema builders, transaction support (with savepoints), connection pooling and standardized responses between different query clients and dialects.
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.	SQL query builder for Postgres, MySQL, MariaDB, SQLite3, and Oracle
Statistics
Stacks 1.7K	Stacks 184
Followers 1.5K	Followers 406
Votes 152	Votes 49
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 11 Write once and then connect to almost any sql engine 10 Faster 8 Nice api, Migrations/Seeds 7 Free 7 Flexibility in what engine you choose
Integrations
Xplenty Fluentd Looker Chartio Treasure Data	PostgreSQL Oracle MySQL SQLite

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dbForge Studio for Oracle

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