nodejs-bigquery VS dbt-core

Compare nodejs-bigquery vs dbt-core and see what are their differences.

nodejs-bigquery

Node.js client for Google Cloud BigQuery: A fast, economical and fully-managed enterprise data warehouse for large-scale data analytics. (by googleapis)

dbt-core

dbt enables data analysts and engineers to transform their data using the same practices that software engineers use to build applications. (by dbt-labs)
Our great sponsors
  • SurveyJS - Open-Source JSON Form Builder to Create Dynamic Forms Right in Your App
  • InfluxDB - Power Real-Time Data Analytics at Scale
  • WorkOS - The modern identity platform for B2B SaaS
nodejs-bigquery dbt-core
43 86
451 8,718
0.7% 6.1%
7.9 9.7
10 days ago 4 days ago
TypeScript Python
Apache License 2.0 Apache License 2.0
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

nodejs-bigquery

Posts with mentions or reviews of nodejs-bigquery. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-05-15.
  • Wrangling BigQuery at Reddit
    2 projects | /r/RedditEng | 15 May 2023
    If you've ever wondered what it's like to manage a BigQuery instance at Reddit scale, know that it's exactly like smaller systems just with much, much bigger numbers in the logs. Database management fundamentals are eerily similar regardless of scale or platform; BigQuery handles just about anything we throw at it, and we do indeed throw it the whole book. Our BigQuery platform is more than 100 petabytes of data that supports data science, machine learning, and analytics workloads that drive experiments, analytics, advertising, revenue, safety, and more. As Reddit grew, so did the workload velocity and complexity within BigQuery and thus the need for more elegant and fine-tuned workload management.
  • Building a dev.to analytics dashboard using OpenSearch
    6 projects | dev.to | 25 Mar 2023
    Now I know I've got some data I could use, I now need to find a platform that I can use to analyse the data coming from the Forem API. I did consider some other pieces of software, such as Google BigQuery (with looker studio) and ElasticSearch (with Kibana), I ultimately went with OpenSearch which is essentially a forked version of ElasticSearch maintained by AWS. The main reasons are that I could host it locally for free (unlike BigQuery). I do have some prior experience with both elastic (back when it was called ELK) and OpenSearch, but my work with OpenSearch was far more recent, so I decided to go with that.
  • Learning Excel. Is there a resource for fake data sets like retail and wholesale inventories and sales histories etc for testing and practice?
    2 projects | /r/excel | 7 Mar 2023
  • Data Analytics at Potloc I: Making data integrity your priority with Elementary & Meltano
    4 projects | dev.to | 5 Jan 2023
    Bigquery as our data warehouse
  • Designing a Video Streaming Platform 📹
    7 projects | dev.to | 13 Nov 2022
    Google BigQuery
  • What is data integration?
    6 projects | dev.to | 9 Nov 2022
    You build a data integration between all the ad service providers (e.g. Google Ads, Facebook Ads, etc.), ingesting data from those APIs and storing it in your BigQuery data warehouse.
  • What are Firebase Extensions? How can they speed up your app development?
    3 projects | dev.to | 7 Nov 2022
    It also includes some extensions that integrate Firebase with Google Cloud Platform services such as BigQuery.
  • Evolutionary Data Infrastructure
    5 projects | dev.to | 26 Sep 2022
    In addition, batch tasks require knowledge of the data schema of each service in order to get the data correctly and save it to the corresponding warehouse table. Assuming our data warehouse is GCP BigQuery, the schema in the warehouse table also needs to be created and modified manually.
  • Moving to Google Cloud managed services, from a FinOps point of view
    2 projects | dev.to | 20 Sep 2022
    BigQuery has a pricing model close to Pub/Sub : you pay for what you insert on the database (in streaming) and the storage of these data. The main difference is on what you can do with these data. BigQuery is not a message queuing service, this is a data warehouse service. It proposes a query service to exploit these data and you pay for these queries. Actually, not on the query itself but on the quantity of data manipulated for producing the results of the query. This means that you do not directly pay for a power capacity on query but on data transfer to produce the result which is very different from a none managed database perspective such SQL databases where the main model pricing is the node size to store and query data.
  • Apache Kafka Use Cases: When To Use It & When Not To
    4 projects | dev.to | 6 Sep 2022
    A Kafka-based data integration platform will be a good fit here. The services can add events to different topics in a broker whenever there is a data update. Kafka consumers corresponding to each of the services can monitor these topics and make updates to the data in real-time. It is also possible to create a unified data store through the same integration platform. Developers can implement a unified store either using an open source data warehouse like Apache Kylin or use a cloud-based one like Redshift or Snowflake. In this instance, the organization uses BigQuery. Data to this warehouse can be loaded through a separate Kafka topic. The below diagram summarizes the complete architecture.

dbt-core

Posts with mentions or reviews of dbt-core. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-09-16.

What are some alternatives?

When comparing nodejs-bigquery and dbt-core you can also consider the following projects:

airbyte - The leading data integration platform for ETL / ELT data pipelines from APIs, databases & files to data warehouses, data lakes & data lakehouses. Both self-hosted and Cloud-hosted.

metricflow - MetricFlow allows you to define, build, and maintain metrics in code.

n8n - Free and source-available fair-code licensed workflow automation tool. Easily automate tasks across different services.

Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows

citus - Distributed PostgreSQL as an extension

dagster - An orchestration platform for the development, production, and observation of data assets.

argo-navis - Argo Navis repository for research, docs and misc items

streamlit - Streamlit — A faster way to build and share data apps.

targets - Function-oriented Make-like declarative workflows for R

great_expectations - Always know what to expect from your data.

nbdev - Create delightful software with Jupyter Notebooks