BigQuery vs Azure Synapse
Data - the currency of the 21st century
Data is quite aptly called the currency of the future. It is something that is driving everything in the world and is one of the most important indispensable commodities. An explosion of data has enabled companies to deal with customers effectively.
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 have to be done to make business smarter. To launch a cloud data warehouse, a set of nodes has to be launched called the Redshift cluster. Regardless of the size of data, one can take advantage of fast query performance.
What is Snowflake?
Snowflake is considered a powerful relational database management system. It’s an analytical data warehouse for both structured and semi-structured data that follows the SaaS model. It is fast, user-friendly, and offers more flexibility than a traditional warehouse. It uses a SQL database engine with a unique architecture specifically designed for clouds.
Comparison between Snowflake and Redshift
Highlights
| # | Attributes | Snowflake | Amazon Redshift |
|---|---|---|---|
| 1 | Scaling | Auto concurrency provides instant scaling without redistribution. | Not as instant as Snowflake. It can take a few minutes to some hours. |
| 2 | Maintenance | Completely automated. No maintenance is required. | Manual maintenance i.e Vacuuming by an administrator. |
| 3 | Performance | High performance. | Average in performance. |
| 4 | Data Replication | Uses COPY command. | Uses COPY INTO command. |
| 5 | Security | Uses Always-on-encryption. | Uses end-to-end encryption. |
| 6 | Pricing | The policy of pay as you use is attractive for users. | Attractive pricing at certain level usage. |
| 7 | Automation | Fully automated. | Manual effort. |
| 8 | Packages | Tier based packages. | More unified offer package. |
| 9 | Engine | Unique architecture design to scale on the web. | Machine learning engine. |
| 10 | Integration | 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. | Snowflake does not have equivalent integrations which makes it more difficult for customers to use tools like Kinesis, Glue, Athena, etc. when trying to integrate their data warehouse with their data lake architecture. Snowflake does, however, offer a few other interesting integration points including IBM Cognos, Informatica, Power BI, Qlik, Apache Spark, Tableau and a few others. |
Core Competencies
| # | Core Competencies | Snowflake | Amazon Redshift |
|---|---|---|---|
| 1 | Data Integration | ETL/ELT concept in data integration. | Advanced ETL tool helps you effortlessly by collecting data. |
| 2 | Data Compression | Gzip compression efficiency. | Columnar compression. |
| 3 | Data Quality | With tools like Talend provide data management with real time speed. | Python data quality for Amazon Redshift. |
| 4 | Built-In Data Analytics | A single platform that creates cloud. | Know is a BI tool used for Amazon Redshift. |
| 5 | In Database Machine Learning | SQL dialect like ‘Intelligent Miner’ and ‘Oracle’ is being used. |
Create data source wizard is used in Amazon Machine Learning to create data source object. |
| 6 | Data Lake Analytics | Global snowflake turns data lake into data ocean. | Uses amazon S3. It is cost efficient and stores unlimited data. |
Integration
| # | Integration | Snowflake | Amazon Redshift |
|---|---|---|---|
| 1 | AI/ML Integration | Driveless A1 Automated machine learning inflows. |
Create data source wizard in (Amazon ML). |
| 2 | BI Tool Integration | Built -for -cloud warehouse deliver efficient BI solution. | Know is a BI tool used in Redshift. |
| 3 | Data Lake Integration | It is a modern data lake. | Integrated with data lake to offer 3x performance. |
Sharing
| # | Sharing | Snowflake | Amazon Redshift |
|---|---|---|---|
| 1 | Sharing | Enables sharing through shares between read-only. | Share data in Apache Parquet Format. |
| 2 | Data Governance | Data governance experts like Talend provides perfect data governace. | Data Lienage using Token. |
| 3 | Data Security | Role Based Access Control (RBAC) authorization. | Network isolation to control access to data warehouse cluster. SSL and AES 256 encryption end – to – end encryption. |
| 4 | Data Storage | Uses new SQL database. | Columnar storage. |
| 5 | Backup and Recovery | Does with virtual warehouse and querying from clone. | 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 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.
How Lyftrondata helps
- Lyftrondata provides cumulative data from a different source and brings down to the data pipeline.
- It works on the pain-points of preparation of data avoiding delays in projects.
- It also converts the complex data into the normalized one.
- It eliminates traditional bottlenecks related to data.
- It works at solving problems like 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.
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?
