nodejs-bigquery
MongoDB
nodejs-bigquery | MongoDB | |
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43 | 250 | |
457 | 25,453 | |
0.9% | 0.6% | |
8.0 | 10.0 | |
3 days ago | 9 days ago | |
TypeScript | C++ | |
Apache License 2.0 | GNU General Public License v3.0 or later |
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
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Wrangling BigQuery at Reddit
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.
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Building a dev.to analytics dashboard using OpenSearch
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.
- Como evitar SQL Injection utilizando client do BigQuery
- Learning Excel. Is there a resource for fake data sets like retail and wholesale inventories and sales histories etc for testing and practice?
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How to Totally Fubar Your Cloud Infrastructure Costs
First, in one of our recent projects, we helped our client to run the cloud-based infrastructure of their entirely automated, real-time SEO platform. The solution rested in the safe familiarity of Google’s popular cloud-based data centres (i.e. Google Cloud Platform), whilst also making use of BigQuery — a serverless, multi-cloud data warehouse.
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Data Analytics at Potloc I: Making data integrity your priority with Elementary & Meltano
Bigquery as our data warehouse
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I've tried really hard but need some help please. Bigquery not returning data after 2019.
This post in github thinks it may be an error in bigquery's backend.
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Deploying a Data Warehouse with Pulumi and Amazon Redshift
A data warehouse is a specialized database that's purpose built for gathering and analyzing data. Unlike general-purpose databases like MySQL or PostgreSQL, which are designed to meet the real-time performance and transactional needs of applications, a data warehouse is designed to collect and process the data produced by those applications, collectively and over time, to help you gain insight from it. Examples of data-warehouse products include Snowflake, Google BigQuery, Azure Synapse Analytics, and Amazon Redshift — all of which, incidentally, are easily managed with Pulumi.
- [Question] Which GCP tool should I use to build a Business decisional dashboard?
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Designing a Video Streaming Platform 📹
Google BigQuery
MongoDB
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What do you want to watch next? This is why I built GoodWatch.
Data Handling: Utilizes Windmill for data pipelines, with a primary database powered by PostgreSQL. Auxiliary data storage is handled by MongoDB, with Redis for caching to optimize performance
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System Design: Databases and DBMS
MongoDB
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From Zero to CRUD Hero: Building Your First Backend API in JavaScript
First, visit MongoDB Atlas and create an account, or sign in if you already have one. This article will guide you through the process of creating a MongoDB account. You should be redirected to your dashboard once you have completed the process. Locate the Connect button and click it.
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Understanding SQL vs. NoSQL Databases: A Beginner's Guide
On the other hand, NoSQL databases are non-relational databases. They store data in flexible, JSON-like documents, key-value pairs, or wide-column stores. Examples include MongoDB, Couchbase, and Cassandra.
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Building Llama as a Service (LaaS)
I built each API with Node.js, Express, and Docker. Services connected to a NoSQL MongoDB database.
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Time Series Blob Data: ReductStore vs. MongoDB
In edge computing, managing time series blob data efficiently is critical for performance-sensitive applications. This blog post will compare ReductStore, a specialized time series database for unstructured data, and MongoDB, a widely-used NoSQL database.
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Build Your Own Uptime Monitor with MeteorJS + Fetch + Plotly.js ☄️🔭
MongoDB to store our data as documents, close to JS objects
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How to choose the right type of database
MongoDB: Known for its ease of development and strong community support, MongoDB is effective in scenarios where flexible schema and rapid iteration are more critical than strict ACID compliance.
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How to create a dynamic AI Discord bot with TypeScript
MongoDB
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Mastering Microservices: A Hands-On Tutorial with Node.js, RabbitMQ, Nginx, and Docker
Ensure you have MongoDB installed for data storage. You can download MongoDB Community Server from MongoDB's official website or use the cloud cluster.
What are some alternatives?
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.
mongo-express - Web-based MongoDB admin interface, written with Node.js and express
dbt-core - dbt enables data analysts and engineers to transform their data using the same practices that software engineers use to build applications.
Marten - .NET Transactional Document DB and Event Store on PostgreSQL
dagster - An orchestration platform for the development, production, and observation of data assets.
LiteDB - LiteDB - A .NET NoSQL Document Store in a single data file
rudderstack-docs - Documentation repository for RudderStack - the Customer Data Platform for Developers.
LevelDB - LevelDB is a fast key-value storage library written at Google that provides an ordered mapping from string keys to string values.
dbt - dbt enables data analysts and engineers to transform their data using the same practices that software engineers use to build applications. [Moved to: https://github.com/dbt-labs/dbt-core]
SQLAlchemy - The Database Toolkit for Python
streamlit - Streamlit — A faster way to build and share data apps.
Apache Ignite - Apache Ignite