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May 22, 2024 - HTML
Google BigQuery enables companies to handle large amounts of data without having to manage infrastructure. Google’s documentation describes it as a « serverless architecture (that) lets you use SQL queries to answer your organization's biggest questions with zero infrastructure management. BigQuery's scalable, distributed analysis engine lets you query terabytes in seconds and petabytes in minutes. » Its client libraries allow the use of widely known languages such as Python, Java, JavaScript, and Go. Federated queries are also supported, making it flexible to read data from external sources.
📖 A highly rated canonical book on it is « Google BigQuery: The Definitive Guide », a comprehensive reference.
Another enriching read on the subject is the inside story told in the article by the founding product manager of BigQuery celebrating its 10th anniversary.
The dbt-native data observability solution for data & analytics engineers. Monitor your data pipelines in minutes. Available as self-hosted or cloud service with premium features.
HTTP Archive's annual "State of the Web" report made by the web community
Metrics Observability & Troubleshooting
BigQuery Manager
LABS Final Project: Yelp_GoogleMaps_Reviews - Roles: Data Engineer, Data Analyst, Machine Learning Engineer, Data Scientist | Bootcamp Henry: Data Science Career | DataFT Cohort 17
This ETL (Extract, Transform, Load) project employs several Python libraries, including Airflow, Soda, Polars, YData Profiling, DuckDB, Requests, Loguru, and Google Cloud to streamline the extraction, transformation, and loading of CSV datasets from the U.S. government's data repository at https://catalog.data.gov.
This is the sentiment analysis on the #VisitRwanda on twitter, this is a campaign of eco tourism in Rwanda that promotes touristic places attraction in Rwanda.
Spark Read/Write data from/to Multi Cloud utils (GCP, Azure and AWS)
Asynchronous big query ready functions to control your BigQuery execution flow. Pretty neat, right?
An analysis of the USDA NASS Agriculture Dataset using BigQuery, Geopandas, etc.
Build a model that can predict customers' Long Term Value (LTV).
A repro with reports of the assignments of the master degree in data science
Released May 19, 2010