Google BigQuery vs Amazon Redshift: Learn Key Differences
What is Redshift?
Redshift is a fully managed petabyte-scale cloud-ready data warehouse solution that connects easily with business intelligence tools. To make business more intelligent, extraction, transformation, and loading must be completed. The Red Shift cluster is a collection of nodes that must be launched in order to launch a cloud data warehouse. The benefits of quick query performance are available for all data sizes.
What is Google BigQuery?
It is an enterprise data warehouse for analytics powered by the Google Cloud Platform. It works well for evaluating massive amounts of data to satisfy the demands of big data processing. The supplied data is extremely available, robust, and encrypted. It provides petabyte-scale SQL queries and storage on an exabyte scale. As businesses expand, handling data gets increasingly difficult. Analyzing data that is essential to business can take up this focus again. BigQuery queries are executed using Dremel, a potent query engine created by Google.
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?
With the help of agility and a contemporary data pipeline, Lyftrondata offers a data management platform that facilitates quick data preparation. With over 300 data connectors to software as a service (SaaS) platforms, like ServiceNow, Zendesk, Shopify, Paylocity, and others, Lyftrondata can help you. Lyftrondata connections give your company data catalog search capabilities by automatically converting any source data into a normalized, query-ready relational format. With automated data pipelines, it removes the typical ETL/ELT bottlenecks, and with Spark & Snowflake's cutting-edge cloud compute, data is instantaneously available to business intelligence consumers.
It facilitates the simple migration of data to cloud data warehouses from any source. Think about Lyftrondata if you have ever encountered a lack of data that you required, time-consuming report development, or a lengthy wait for your BI specialist.
How Lyftrondata boosts BigQuery
Data is loaded into BigQuery and links to data sources are managed via Lyftrondata Data Pipeline. Standard SQL is used to define each transformation, which is then pushed down to BigQuery and data sources.
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
When Lyftrondata is integrated with Amazon Redshift, the outcomes are remarkable. It brings down to the data pipeline and gives cumulative data from several sources.
- 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?
