Google BigQuery vs Amazon Redshift: Learn Key Differences

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What is Redshift?

Redshift can be described as a fully-managed cloud-ready petabyte-scale data warehouse service that can be seamlessly integrated with business intelligence tools. Extraction, transformation, and load has to be done to make business smarter. To launch a cloud data warehouse, a set of nodes have to be launched called the Red Shift cluster. Regardless of the size of data, one can take advantage of fast query performance.

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 reshifted to analyze business-critical data. Dremel is a powerful query engine developed by Google that is used to execute queries in BigQuery.

Comparision between BigQuery and Amazon Redshift

AttributesGoogle BigQueryRedshift
G2 Rating
ScalingHandles everything, Removes manual scaling.Not as instant as Google BigQuery. It can take a few minutes to some hours.
MaintenanceIt is “serverless”. Compute and storage resources are handled automatically.Manual maintenance i.e Vacuuming by an administrator.
PerformanceAbility to autoscale. Perform well under load levels.Average in performance.

Use AES encryption. Federated user access via

Microsoft Active Dictionary. MFA.

Uses end-to-end encryption.
PricingQuery-based pricing.Attractive pricing at certain level usage.
IntegrationProtects through Google Cloud Platform's Virtual Private Cloud Service Controls. Fulfills compliance requirements of HIPPAA, ISO, 27001, PCI DSS, SOC 1 Type II, AND SOC 2 Type II.Redshift integrates with a variety of AWS services such as Kinesis Data Firehose, SageMaker, EMR, Glue, DynamoDB, Athena, Database Migration Service (DMS), Schema Conversion Tools (SCT), CloudWatch, etc.

Core Competencies Google BigQuery Redshift
Data Integrations Read data using streaming mode or batch mode. Advanced ETL tool helps you effortlessly by collecting data.
Data Compression Data is compressed before transfer while for CSV and JSON, it loads uncompressed files. Data is compressed before transfer while for CSV and JSON, it loads uncompressed files.
Data Quality Advanced data quality with SQL. Python data quality for amazon shift.
Built-In Data Analytics Fully manages enterprise data for large scale data analytics. Know is a BI tool used for Amazon Redshift.
In-Database Machine Learning Bigquery ML let you create and execute machine. learning models using SQL queries. Create data source wizard is used in Amazon Machine Learning to create data source object.
Data Lake Analytics Uses Identity and Access Management (IAM) manage access to resources to analyse data. Uses Amazon S3. It is cost efficient and stores unlimited data.

On-Premise Google BigQuery Redshift
Cloud Multicloud analytic solution. It is Google Cloud fully managed warehouse. Fully managed petabyte scale data warehouse service in Cloud.

PerformanceGoogle BigQueryRedshift
ScalabilityScalable, it scales as needs change.Unlimited scalability.

SharingGoogle BigQueryRedshift
SharingSecurely access and share analytical insights in a few clicks.Share data in Apache Parquet Format.
Data GovernanceUsing google cloud that allows customers to abide by GDPR , CCTA and over regulations.Data Lineage using Tokens.
Data SecuritySecurity model based on Google Clouds. IAM capability.Column level security.Network isolation to control access to data warehouse cluster. SSL and AES 256 encryption end – to – end encryption.
Data StorageNearline storage.Columnar storage.
Backup & recoveryAutomatically backed up.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.

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 to consuming report generation or long queue to your BI expert, consider Lyftrondata.

Read More: Comparsion between Snowflake vs Redshift


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 Lyftrondata 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, what they purchase, and where they please your store in a flawlessly planned dashboard.

  • Real-Dime Data Integration

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

How Lyftrondata modernizes Redshift

The results are astounding when Amazon Redshift is combined with Lyftrondata. It provides cumulative data from a different source and brings down to the data pipeline.

  • Easy Data extraction.
  • It provides massively parallel processing (MPP).
  • Shortens data preparation.
  • Provides columnar data storage.
  • Avoids delay in the projects.
  • Converts the complex data into the normalized.
  • Eliminates problems related to real-time data, and data inconsistency.

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:

    Scale 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:

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

  • IoT:

    Powerful analytics and decision making at the scale of IoT. Drive instant insights and value from all the data that IoT devices generate.

  • 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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