Snowflake vs BigQuery

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Cloud data warehouse-an indispensable need of today's era

Cloud data warehouse-an indispensable need of today’s era Data warehouse systems are the foundation of business analysis and help in the consolidated historical data analysis for improved performance and growth. The notion is to create a permanent secured storage space for data preparation, exchange and processing. These constraints include excessive time consumption that leads to performance deterioration.

What is Google BigQuery

It is a Google Cloud Platform to an enterprise data warehouse for analytics. It is good for analyzing the huge amount of data to meet big data processing requirements. The provided data is encrypted, durable, and highly available. It offers Exabyte-scale storage and petabyte-scale SQL queries. With the growth of business managing data becomes a tough task. This focus can be shifted to analyze business-critical data. Dremel is a powerful query engine developed by Google that is used to execute queries in BigQuery.

Benefits of Google BigQuery

  • It has very balanced storage. It offers durable and persistent storage.
  • Their cloud-powered massively parallel query service Can read about 100,000 disks using thousands of CPUs in parallel.
  • It supports a variety of formats for data ingestion like Avro, Parquet/ORC.
  • It simplifies queries and enables them to store semi-structured data naturally.
  • It has strong AI/ML capabilities and supports broad analytical use cases using AutoML Tables for best-in-class accuracy and BigQuery ML for problems that require fast experimentation and development time.

What is Snowflake

Snowflake is a data warehouse solution that is offered as a SaaS platform. It can be hosted on either of two public cloud services like Amazon Web Services or Microsoft Azure. It uses existing solutions such as a new SQL database engine with a cloud-optimized architecture. It enables separation between data warehouses to compute and storage requirements.

Benefits of Snowflake

  • It has a multi-cluster, shared data architecture that separates its storage and compute layer.
  • It features micro-partitioning which means it can manage semi-structured and structured data.
  • It is a complete ANSI SQL database warehouse. It has good compatibility for multi-statement transactions.
  • It quickly allows us to scale-up, down elastically.
  • It offers high-level queries on semi-structured data.
  • It provides economic per-second computing price and cost-effective compressed data storage pricing.

Quality comparison Snowflake vs Google BigQuery

  • Highlights
  • Core Competencies
  • Integration
  • Sharing
Features Snowflake Google BigQuery Analysis
Time-based pricing model
Query-based pricing
BigQuery is cheaper
Advanced features
Handles everything
Removes manual scaling.
BigQuery is better
Ability to autoscale.
Perform well under load levels
Ability to Autoscale.
Perform well under load levels
According to independent Third-party bench-mark Snowflake is better
Ease to use
High on the usability scale
Easy but may face technical issues due to automated process
Snowflake is easier to use
Use AES encryption
Federated user access via Okta, ADFS
Use AES encryption.
Federated user access via Microsoft Active Dictionary.
Both are equally secure
Keeps Compute, Storage, and Cloud services separate to optimize
Takes into account Computation and Storage.
Snowflake has better architecture.
The Administrator manages user roles.
It is “serverless”.
Compute and storage resources are handled automatically.
BigQuery is better.
Data protection
Lacks built-in virtual private networking
Protects through Google Cloud Platform's Virtual Private Cloud Service Controls.
BigQuery has an upper hand
Complance & Governance
Fulfills compliance requirements of HIPPAA, ISO, 27001, PCI DSS, SOC 1 Type II, AND SOC 2 Type II
Fulfills compliance requirements of HIPPAA, ISO, 27001, PCI DSS, SOC 1 Type II, AND SOC 2 Type I
Both offer excellent performance
Core Competencies Snowflake Google BigQuery
Data Integrations
ETL/ELT concept in data integration.
Read data using streaming mode or batch mode.
Data Compression
Gzip compression efficiency.
In parallel, data is compressed before transfer while for CSV and JSON, it loads uncompressed files.
Data Quality
With tools like Talend provide data management with real time speed.
Advanced data quality with SQL.
Built-In Data Analytics
A single platform that creates cloud.
Fully manages enterprise data for large scale data analytics.
In-Database Machine Learning
SQL dialect like
‘Intelligent Miner’ and ‘Oracle’ is being used.
Bigquery ML let you create and execute machine. learning models using SQL queries.
Data Lake Analytics
Global snowflake turns data lake into data ocean.
Uses Identity and Access Management (IAM) manage access to resources to analyse data.
Integration Snowflake Google BigQuery
AI/ ML Integration
Driveless A1
Automated machine learning inflows.
Use bigquery ML to evaluate ML models.
BI Tool Integration
Built -for -cloud warehouse deliver efficient BI solution.
BI is responsible for (RLS) Row Level Security and applying user permissions.
Data lake Integration
It is a modern data lake.
Data like API system use Google Cloud composer to schedule Bigquery Processing.
Sharing Snowflake Google BigQuery
Enables sharing through shares between read-only.
Securely access and share analytical insights in few clicks.
Data Governance
Data governance experts like Talend provides perfect data governace.
Using google cloud that allows customers to abide by GDPR , CCTA and over regulations.
Data Security
Role Based Access Control (RBAC) authorization.
Security model based on Google Clouds. IAM capability.Column level security.
Data Storage
Uses new SQL database.
Nearline storage
Backup & recovery
Does with virtual warehouse and querying from clone.
Automatically backed up.


Want more information about how to solve your biggest data warehousing challenges? Visit our resource center to explore all of our informative and educational ebooks, case studies, white papers, videos and much more.


BigQuery and Snowflake are both feature-rich warehouses. These solutions have helped companies of all sizes to improve their BI and analytics workflows. Both the warehouses have explicit features to provide better performance and output.

There is a need to test our data to determine and decide on the warehouse that suits our needs.

Why is Lyftrondata the best choice?

Lyftrondata delivers a data management platform that combines a modern data pipeline with agility for rapid data preparation. Lyftrondata supports you with 300+ data integrations such as ServiceNowZendeskShopifyPaylocity, etc. to software as a service SaaS platforms. Lyftrondata connectors automatically convert any source data into the normalized, ready-to-query relational format and provide search capability on your enterprise data catalog. It eliminates traditional ETL/ELT bottlenecks with automatic data pipelines and makes data instantly accessible to BI users with the modern cloud compute of Spark & Snowflake.

It helps migrate data from any source easily to cloud data warehouses. If you have ever experienced a lack of data you needed, time consuming report generation or long queue to your BI expert, consider Lyftrondata.

How Lyftrondata boosts BigQuery

Lyftrondata Data Pipeline manages connections to data sources and loads data to BigQuery. All transformations are defined in standard SQL and pushed down to data sources and BigQuery.

  • Incorporates and Assembles:

    All your information, every one of your bits of knowledge and all your security that you never thought conceivable at a centralized spot.

  • Secured Access:

    Keep up resilience against consistent digital dangers through our secured Lyftron engineering.

  • Comprehensive Analytics:

    Access progressed reports for better experiences on your Vertica Database Warehouse information. Get knowledge across items, channels, client lifetime worth, etc.

  • 360-degree Customer View:

    Know who your clients are and what they purchased.

  • Real-Dime Data Integration:

    Survey, improve, dispatch and smooth out constant information assortment from different streams and drive instant actionable insight.

How Lyftrondata is meant for Snowflake

Lyftrondata is a future-proof solution for rapid growth in data volume. It is amazing for data synchronization. It has a caliber to revolutionize the concept of dealing with data.

Snowflake Processing For Faster Query Performance:

Lyftrondata empowers organizations to effortlessly handle information and changes from various sources to Snowflake, empowers with continuous sync and information security through cutting edge encryption with the most cost effective licensing model.

Secured and Seamless Data Sharing:

Lyftrondata and Snowflake follow top-tier, standard-based practices to guarantee your information and information distribution center security.

Lower Infrastructure Costs:

Lyftrondata's architecture brings down foundation costs and reduces load by wiping out the requirement for independent arranging framework or workers.

Codeless Development Environment And Integrated Metadata Views:

Lyftrondata is outfitted with the most natural and easy-to-use interface. Within two clicks, you can load, move, and imitate information to any stage with no issue.

Boundless Cloud Compute:

Lyftrondata and Snowflake columnar architecture consequently scale to help any measure of information, remaining burdens and simultaneous clients and applications without requiring information development, information bazaars or information duplicates.

Data Integration:

Associate 130+ on-premise and cloud data sources and set up ceaseless information synchronization of chosen data.

Lyftrondata use cases

  • Data Lake:

    Lyftrondata combines the power of high-level performance and cloud data warehousing to build a modern, enterprise-ready data lake.

  • Data Migration:

    Lyftrondata allows you to migrate a legacy data warehouse either as a single LIFT-SHIFT-MODERNIZE operation or as a staged approach.

  • BI Acceleration:

    Scales your BI limitlessly. Query any amount of data from any source and drive valuable insights for critical decision making and business growth.

  • Master Data Management:

    Lyftrondata enables you to work with chosen web service platforms and manage large data volumes at an unprecedented low cost and effort.

  • Application Acceleration:

    Application acceleration With Lyftrondata you can boost the performance of your application at an unprecedented speed, high security, and substantially lower costs.

  • IoT:

    IoT robust frame-work of Lyftrondata and scalable architecture of Amazon Redshift allow us to process and transform the structured and semi-structured IoT data.

  • Data Governance:

    With Lyftrondata, you get a well-versed data governance framework to gain full control of your data, better data availability and enhanced security.


Schedule a free, no-strings-attached demo to discover how Lyftrondata can radically simplify data lake ETL in your organization.

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