Snowflake vs Redshift vs
Google BigQuery
What is BigQuery?
It is an enterprise data warehouse for analytics powered by the Google Cloud Platform. It works well for large-scale data analysis needed to satisfy big data processing demands. The supplied data is extremely available, robust, and encrypted. It provides petabyte-scale SQL queries and storage on an exabyte scale. For organizations, managing ever-increasing data is challenging. 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.
What is Redshift?
A petabyte-scale, cloud-ready data warehouse solution that is fully managed and can be easily linked with business intelligence tools is what Redshift is. To make business smarter, extract, transform, and load must be completed. The Redshift 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 Snowflake?
A strong relational database management system is Snowflake. It is an analytical data warehouse that uses the SaaS paradigm to store both structured and semi-structured data. Compared to traditional warehouses, it offers greater flexibility and is faster and easier to utilize. It makes use of a SQL database engine with a special architecture made for cloud computing.
Comparision between Amazon Redshift, BigQuery and Snowflake
| Attributes | Google BigQuery | Amazon Redshift | Snowflake |
|---|---|---|---|
| G2 Rating |  |
 |
|
| Maintenance | 1-More time in optimizing queries. 2-Partitions and sorts data in a background. |
1- Some operations need to be manual. 2-Query planner still relies on table statistics for updating the stats. |
1-Little to maintain. 2-It supports automatic clustering. 3-It has depressed manual clustering. |
| Data Sources | 1-One can set up a connection to some external data storage like Cloud SQL | 1- We can connect to data sitting on S3. 2-Act as an intermediate compute layer. |
1-The data has to be stored within Snowflake. 2-Provides extra table functionality. |
| Streaming | Native streaming | No native streaming | No native streaming. Microbatching via Snowpipe from data sitting on Google Cloud. |
| Caching | Caches queries and has an adjustable intermediate cache. | Caches queries and results depending on the node type. | Hot and warm query caches in intermediate storage. |
| Materialise View | Currently in GA. | Has good support for materialized view. | Full support for the materialized view. |
| UDF's | Supports writing UDF's in SQL and Javascript. | UDFs can be written in SQL and PYTHON | Support for functions in SQL and Javascript. |
| Query Language | Offers two main dialect Legacy SQL Standard SQL | The SQL syntax is also an ANSI complaint. | ANSI complaint. Simple to use. |
| Encryption | Encrypted using Google-managed encryption key management system(KMS) | End-to-End encrypted by default | Encrypted at rest using the AWS management system. |
| Scalability | Only charged for queries you run | Recently induced RA3 node offers both elastic resize or classic resize | Pause, resume semantics both manual and automated based on workload. |
| Pricing | 2 cents per GB for warm data, 1 cent per GB for colder data | Node type (ds2/dc2/RA3, avoid d*1 node type. The Use of Redshift spectrum may incur additional charges. | With the number and size of warehouses, you will get cost per credit. |
| Cloud Deployment | Multicloud analytic solution. It is Google Cloud fully managed ware house. | Fully managed petabyte scale data ware house service in Cloud. | With it’s Cloud data platform live data can be shared. |
| Compression | Proprietary compression that is opaque to the user. | Transparent compression by implementing open algorithms. | It provides its compression layer that is opaque to the user. |
| Core Competencies | Google BigQuery | Amazon Redshift | Snowflake |
|---|---|---|---|
| Data Integrations | Read data using streaming mode or batch mode. | Advanced ETL tool helps you effortlessly by collecting data. | ETL/ELT concept in data integration. |
| Data Compression | In parallel, data is compressed before transfer while for CSV and JSON, it loads uncompressed files. | Columnar compression | Gzip compression efficiency. |
| Data Quality | Advanced data quality with SQL. | Python data quality for amazon shift. | With tools like Talend provide data management with real time speed. |
| Built-In Data Analytics | Fully manages enterprise data for large scale data analytics. | Know is a BI tool used for Amazon Redshift. | A single platform that creates cloud. |
| 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. | SQL dialect like ‘Intelligent Miner’ and ‘Oracle’ is being used. |
| 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. | Global snowflake turns data lake into data ocean. |
| Integration | Google BigQuery | Amazon Redshift | Snowflake |
|---|---|---|---|
| AI/ ML Integration | Use bigquery ML to evaluate ML models. | Create data source wizard in (Amazon ML). | Driveless A1 Automated machine learning inflows. |
| BI Tool Integration | BI is responsible for (RLS) Row Level Security and applying user permissions. | Know is a BI tool used in Redshift. | Built -for -cloud warehouse deliver efficient BI solution. |
| Data lake Integration | Data like API system use Google Cloud composer to schedule Bigquery Processing. | Integrated with data lake to offer 3x performance. | It is a modern data lake. |
| Sharing | Google BigQuery | Amazon Redshift | Snowflake |
|---|---|---|---|
| Sharing | Securely access and share analytical insights in few clicks. | Share data in Apache Parquet Format. | Enables sharing through shares between read-only. |
| Data Governance | Using google cloud that allows customers to abide by GDPR , CCTA and over regulations. | Data Lienage using Token. | Data governance experts like Talend provides perfect data governace. |
| 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. | Role Based Access Control (RBAC) authorization. |
| Data Storage | Nearline storage | Columnar storage | Uses new SQL database. |
| Backup & recovery | Automatically backed up. | Automatically backed up. | Does with virtual warehouse and querying from clone. |
How Lyftrondata helps
- Lyftrondata provides cumulative data from a different source and brings it down to the data pipeline..
- It works on the pain-points of data preparation, thus avoiding project delays.
- It also converts the complex data into the normalized one.
- It eliminates traditional bottlenecks related to data.
- It works at solving problems such as huge time consumption to generate reports, waiting to get new reports, real-time data, and data inconsistency.
- It democratizes data management.
- It helps in combining other data sources to the target data warehouse.
- It perfectly integrates the data and enables data masking and encryption to handle sensitive data.
- It provides a data management platform for rapid data preparation with agility, combining it with the modern data pipeline.
- It empowers business users to solve data-driven business problems.
- It reduces the workload of prototyping tools while optimizing offload 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:
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.
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 easily migrate data from any source to cloud data warehouses. If you have ever experienced a lack of needed data, your report generation is too time consuming, and the queue for your BI expert is too long, then consider Lyftrondata.
Are you unsure about the best option for setting up your data infrastructure?
