Google BigQuery vs Amazon Redshift: Learn Key Differences
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
Highlights
| # | Features | Google BigQuery | Amazon Redshift |
|---|---|---|---|
| 1 | G2 Rating |  |
 |
| 2 | Pricing | Query-based pricing. | Attractive pricing at certain level usage. |
| 3 | Scalability | Handles everything, Removes manual scaling. | Not as instant as Google BigQuery. It can take a few minutes to some hours. |
| 4 | Performance | Ability to autoscale. Perform well under load levels. | Average in performance. |
| 5 | Security | Use AES encryption. Federated user access via Microsoft Active Dictionary. MFA. | Uses end-to-end encryption. |
| 6 | Maintenance | It is “serverless”. Compute and storage resources are handled automatically. | Manual maintenance i.e Vacuuming by an administrator. |
| 7 | Integration | Protects 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
| # | Features | Google BigQuery | Amazon Redshift |
|---|---|---|---|
| 1 | Data Integration | Read data using streaming mode or batch mode. | Advanced ETL tool helps you effortlessly by collecting data. |
| 2 | 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. |
| 3 | Data Quality | Advanced data quality with SQL. | Python data quality for amazon shift. |
| 4 | Built-In Data Analytics | Fully manages enterprise data for large scale data analytics. | Know is a BI tool used for Amazon Redshift. |
| 5 | In Database Machine Learning | Bigquery ML lets 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. |
| 6 | Data Lake Analytics | Uses Identity and Access Management (IAM) to manage access to resources and analyze data. | Uses Amazon S3. It is cost efficient and stores unlimited data. |
Deployment
| # | On-Premise | Google BigQuery | Amazon Redshift |
|---|---|---|---|
| 1 | Cloud | Multicloud analytic solution. It is Google Cloud fully managed warehouse. | Fully managed petabyte scale data warehouse service in Cloud. |
Performance
| # | Performance | Google BigQuery | Amazon Redshift |
|---|---|---|---|
| 1 | Scalability | Scalable, it scales as needs change. | Unlimited scalability. |
Sharing
| # | Features | Google BigQuery | Amazon Redshift |
|---|---|---|---|
| 1 | Sharing | Securely access and share analytical insights in a few clicks. | Share data in Apache Parquet Format. |
| 2 | Data Security | Security 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. |
| 3 | Data Governance | Using google cloud that allows customers to abide by GDPR , CCTA and over regulations. | Data Lineage using Tokens |
| 4 | Data Storage | Nearline storage. | Columnar storage. |
| 5 | Backup and Recovery | Automatically backed up. | Automatically backed up. |
Why Lyftrondata is your 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 ServiceNow, Zendesk, Shopify, Paylocity, 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.
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.
Enterprise grade data platform for Google BigQuery
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.
Are you unsure about the best option for setting up your data infrastructure?
