Google BigQuery vs Sequelize

Overview

Google BigQuery

Stacks1.8K

Followers1.5K

Votes152

Sequelize

Stacks1.0K

Followers1.4K

Votes143

GitHub Stars30.2K

Forks4.3K

Google BigQuery vs Sequelize: What are the differences?

Introduction

In this article, we will explore the key differences between Google BigQuery and Sequelize, focusing on their distinct features and capabilities.

Scalability: Google BigQuery is a massively scalable cloud-based data warehouse, designed to handle and analyze enormous datasets quickly. It offers a managed infrastructure that automatically scales resources based on demand, allowing users to process large volumes of data with ease. On the other hand, Sequelize is an Object-Relational Mapping (ORM) library for Node.js, which primarily focuses on simplifying database operations and interactions within the application code. While Sequelize can handle smaller datasets, it may not have the same level of scalability as BigQuery when dealing with massive data volumes.
Storage Model: Google BigQuery follows a columnar storage model, where data is stored in columnar format rather than rows. This enables efficient compression and optimized query performance for analytical workloads. In contrast, Sequelize works with relational databases that typically use a row storage model. While row-based storage is suitable for transactional workloads, it may not perform as well as columnar storage in analytical scenarios involving complex queries and aggregations.
Query Language: Google BigQuery uses a variant of SQL known as BigQuery SQL, which includes additional features specific to BigQuery's capabilities. It supports standard SQL syntax and provides extensions for working with nested and repeated data structures, as well as advanced analytical functions. Sequelize, on the other hand, supports multiple SQL dialects (such as MySQL, PostgreSQL, SQLite, etc.) based on the database engine being used. It provides a JavaScript-based API for constructing queries and interacting with the database in a consistent and ORM-like manner.
Data Processing and Analytics: Google BigQuery natively integrates with other tools and services in the Google Cloud ecosystem, such as Dataflow for batch/streaming data processing and Data Studio for visualization and reporting. It also supports real-time analytics through features like BigQuery Streaming and BigQuery BI Engine. In contrast, Sequelize primarily focuses on data manipulation and retrieval within the application code, without offering extensive built-in data processing or analytics capabilities. Additional tools and frameworks may need to be integrated for advanced analytics and visualization.
Cost Structure: Google BigQuery follows a pay-as-you-go pricing model, where users are billed based on the amount of data processed and storage utilized. It offers flexible pricing tiers and options for optimizing costs, such as partitioning and clustering data. Sequelize, being a library for application development and database interactions, does not have a separate cost structure. However, the underlying database system used with Sequelize may have its own pricing model based on licensing, cloud usage, or hardware requirements.

In summary, Google BigQuery is a scalable cloud-based data warehouse with advanced analytics capabilities, columnar storage, and a specialized variant of SQL. It is well-suited for processing large datasets and performing complex analytical queries. On the other hand, Sequelize is an ORM library for Node.js that simplifies database operations within application code. It focuses on relational databases, provides a JavaScript-based API, and may not have the same level of scalability or analytical capabilities as BigQuery.

Share your Stack

Help developers discover the tools you use. Get visibility for your team's tech choices and contribute to the community's knowledge.

View Docs

CLI (Node.js)

Manual

Advice on Google BigQuery, Sequelize

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

193k views193k

Comments

Detailed Comparison

Google BigQuery	Sequelize
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.	Sequelize is a promise-based ORM for Node.js and io.js. It supports the dialects PostgreSQL, MySQL, MariaDB, SQLite and MSSQL and features solid transaction support, relations, read replication and more.
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 30.2K
GitHub Forks -	GitHub Forks 4.3K
Stacks 1.8K	Stacks 1.0K
Followers 1.5K	Followers 1.4K
Votes 152	Votes 143
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 42 Good ORM for node.js 31 Easy setup 21 Support MySQL & MariaDB, PostgreSQL, MSSQL, Sqlite 14 Open source 13 Free Cons 30 Docs are awful 10 Relations can be confusing
Integrations
Xplenty Fluentd Looker Chartio Treasure Data	SQLite Microsoft SQL Server Node.js PostgreSQL MySQL MariaDB io.js

What are some alternatives to Google BigQuery, Sequelize?

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.

Qubole

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

Prisma

Prisma is an open-source database toolkit. It replaces traditional ORMs and makes database access easy with an auto-generated query builder for TypeScript & Node.js.

Amazon EMR

It is used in a variety of applications, including log analysis, data warehousing, machine learning, financial analysis, scientific simulation, and bioinformatics.

Hibernate

Hibernate is a suite of open source projects around domain models. The flagship project is Hibernate ORM, the Object Relational Mapper.

Doctrine 2

Doctrine 2 sits on top of a powerful database abstraction layer (DBAL). One of its key features is the option to write database queries in a proprietary object oriented SQL dialect called Doctrine Query Language (DQL), inspired by Hibernates HQL.

Altiscale

we run Apache Hadoop for you. We not only deploy Hadoop, we monitor, manage, fix, and update it for you. Then we take it a step further: We monitor your jobs, notify you when something’s wrong with them, and can help with tuning.

Snowflake

Snowflake eliminates the administration and management demands of traditional data warehouses and big data platforms. Snowflake is a true data warehouse as a service running on Amazon Web Services (AWS)—no infrastructure to manage and no knobs to turn.

MikroORM

TypeScript ORM for Node.js based on Data Mapper, Unit of Work and Identity Map patterns. Supports MongoDB, MySQL, MariaDB, PostgreSQL and SQLite databases.

Entity Framework

It is an object-relational mapper that enables .NET developers to work with relational data using domain-specific objects. It eliminates the need for most of the data-access code that developers usually need to write.

Related Comparisons

Bootstrap vs Materialize

Django vs Laravel vs Node.js

Bootstrap vs Foundation vs Material UI

Node.js vs Spring-Boot

Flyway vs Liquibase