BigQuery is data warehouse product from Google. We will start with Datawarehouse and it’s importance.
📊 What Is a Data Warehouse and Why It Matters
A data warehouse is a central repository that stores large volumes of data collected from multiple heterogeneous sources — such as transactional databases, logs, APIs, and third-party systems. The purpose of a data warehouse is to support business intelligence (BI), analytics, reporting, and trend analysis over historical data. It’s designed for analytical workloads rather than real-time transactional processing.
But, why do we need a second data storage when we already have Database?
A traditional database that stores current application data and supports real-time transactions. Running heavy analytical queries on a transactional database can degrade performance and slow down the application. That’s why data is often moved to a data warehouse where it can be processed without impacting production systems. A data warehouse holds historical data optimized for analytics.
In a typical data architecture, data flows through an ETL (Extract, Transform, Load) or ELT (Extract, Load, Transform) pipeline to ensure data is cleaned, normalized, and structured for efficient querying. Once in the warehouse, the data is analyzed using OLAP (Online Analytical Processing) tools that support complex queries, aggregations, and multidimensional analysis — ideal for dashboards and trend analytics.
In summary:
• Databases serve real-time operations,
• Data warehouses serve analytical workloads and historical insight.
Google BigQuery is Google Cloud’s fully managed, serverless cloud data warehouse designed for large-scale data analytics. It allows organizations to store and analyze petabytes of data without managing servers, clusters, or infrastructure.
👉 BigQuery supports structured and semi-structured data — not raw unstructured data.
Key Features of BigQuery
Serverless & scalable: No need to provision or manage infrastructure — compute and storage scale independently.
Columnar storage: Data is stored in an optimized columnar format for fast analytical queries, reducing I/O and improving performance on large datasets.
Standard SQL support: BigQuery uses ANSI-compliant SQL, familiar to data analysts and engineers.
Massive parallel processing: Queries are executed across thousands of nodes using Google’s distributed systems. BigQuery separates storage from computing; that’s how it is superfast with any queries run for deriving insights.
Built-in machine learning: BigQuery ML lets users build and deploy machine learning models using SQL — eliminating the need to export data to external ML tools.
Flexible data ingestion: Supports structured and semi-structured formats such as CSV, JSON, Parquet, and Avro, and ingest data via batch or streaming pipelines.
Working principle of BigQuery on High Level:
- Load Data from Cloud Storage, Cloud SQL or external sources (CSV, JSON, etc).
- Big Query stores this data in tables inside datasets.
- Perform SQL operations. BigQuery executes it on Google’s infrastructure.
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