PostgreSQL and Snowflake are data storage platforms that are suitable for software applications of any size. But selecting the one between them for your use case is critical. This PostgreSQL vs. Snowflake blog will provide an overview of their architecture, core concepts, pricing, and features so you can make a wise decision.
What is PostgreSQL?
PostgreSQL is a trustworthy database management system with an ardent emphasis on SQL extensibility and compliance. It is suitable for several use cases and industries like finance and web apps. It endorses a high level of performance optimization and modern data types.
Advantages of PostgreSQL
Features of PostgreSQL are as follows:
1) Freeware License: The code of PostgreSQL is available under the freeware license. This gives you the freedom to change, utilize, and implement it as you need, free of charge.
2) Standards and Reliability Compliance: PostgreSQL is a highly fault-tolerant database because of its write-head logging functionality. It has a great community of freeware contributors that serves as a force behind the community support network.
3) Features and Extensions
PostgreSQL enhances various features, like granular access controls, nested transactions, point-in-time recovery, asynchronous replication, and multi-version concurrency Control.
PostgreSQL core concepts
1) TOAST: Oversized-Attribute Storage methodology is a mechanism to store the large data values in a different table for reducing the disk space consumption of the primary table.
2) MVCC: The Multi-Version Concurrency Control is the methodology PostgreSQL utilizes to enable multiple transactions to be implemented parallelly without locking or conflicts.
3) WAL: The Write-Ahead Logging is the technique used for ensuring data durability by logging the changes to the journal before they are written to primary data files.
PostgreSQL Use Cases
1) GIS Applications
Using the PostGIS extension, We can use PostgreSQL to store and analyze the geospatial data in applications like routing, mapping, and geocoding.
2) Enterprise Applications
PostgreSQL is the best choice for high-scale enterprises because of its performance, feature set, and reliability.
3) OLTP workloads
As the relational database, PostgreSQL will be suitable for any application that includes transactional workloads.
What is Snowflake?
Snowflake is a completely managed service that offers customers the near-infinity extensibility of simultaneous workloads to integrate, load, analyze, and securely send their data. The common applications of Snowflake include data engineering, data lakes, data science, and data application development.
The distinct architecture of Snowflake integrates with storage and computing. It allows you to substantially allow your data and user workloads for accessing a single copy of data without any adverse impact on the performance. Using Snowflake, we can continuously run the data solutions throughout multiple clouds and regions for a constant experience.
Snowflake Features
Features of Snwoflake are as follows:
1) Enhanced Data-Driven Decision Making: Snowflake enables us to decompose data soils and offer access to understandable insights throughout the organization. This is the key step for enhancing partner relationships, decreasing operational costs, maximizing pricing, and driving sales efficiency.
2) Expedite Quality of Speed and Analytics: Snowflake equips the analytics pipeline by moving from nightly batch loads to real-world data streams. We can improve the analytic quality at the workplace by giving governed, concurrent, and secure access to our data warehouses throughout the enterprise.
3) Personalized Data Exchange: Snowflake enables us to develop the data exchange, which allows us to securely share live and governed data. It also offers the impulse to develop the best data relationships throughout our business units and with your customers and partners.
4) Enhanced User Experiences and Product Offerings: With Snowflake training, we can better learn product usage and user behavior. We can also utilize the full scope of the data for delivering customer success and supporting data science innovation.
5) Strong Security: We can adjust the secure data lake as a single location for all the cybersecurity and compliance data. Snowflake data lakes assure a rapid incident response. This enables us to understand the full picture of the incident by combining high-volume data in a single location and effectively analyzing years of log data.
Snowflake core concepts
1) Micro-Partition: It is a storage unit in Snowflake that includes the subset of the data in the table. Micro-Partitions are optimized for effective querying.
2) Virtual Warehouse: In Snowflake, Virtual Warehouse is a compute resource that executes the queries and carries out data unloading and loading. Virtual warehouses are separately scaled up or down as per the demand.
3) Data Sharing: The capability to securely share the data between Snowflake accounts without copying or transferring the data.
4) Time Travel: In Snowflake, it is a feature that enables us to query the historical data at particular points in time or within a particular time range.
Snowflake use cases
1) Data Lake
Snowflake is the data lake to ingest and store massive volumes of unprocessed and raw data, which can be later analyzed and transformed as required.
2) Data Warehouse
Snowflake offers a secure and scalable managed data warehouse solution, making it ideal for enterprises that have to store and analyze massive volumes of semi-structured and structured data.
3) ETL and Data Integration
Snowflake supports SQL and several unloading and data-loading options, which make it suitable for ETL and data integration.
Snowflake vs. PostgreSQL
1) Functionality
Snowflake is a cloud-oriented data warehouse service that can work with structured and semi-structured data. PostgreSQL is the freeware RDBMS that was designed to assist the general-purpose entity. Both Snowflake and PostgreSQL are ACID flexible.
2) Support
Regarding support, PostgreSQL and Snowflake have committed communities of developers providing tutorials, articles, videos, and other assets to assist you in your journey.
3) Scalability
Both PostgreSQL and Snowflake include scalability. Snowflake enables us to run huge parallel workloads in a single system at scale. The cluster-based solution of PostgreSQL enables immense expansion.
4) Architecture
We can deploy Snowflake throughout multiple cloud providers like Azure, AWS, and Google Cloud Platform. We can deploy PostgreSQL on several platforms, like on-site, in virtual machines, or as a managed cloud service, like Google Cloud SQL, Amazon RDS, and Azure Database for PostgreSQL.
5) Time Series Data
We can use PostgreSQL for time series data analysis and storage. With its plenty of data types, windows functions, and indexing options, PostgreSQL will handle time series data. Snowflake can efficiently store, analyze, and process time series data because of its flexible and scalable architecture.
6) Pricing Model
PostgreSQL is a freeware software, and there are no licensing fees associated with it. But, costs can emerge from the hosting and operational expenses while deploying the self-managed PostgreSQL server. Snowflake provides pay-as-you-go pricing. It Charges separately for compute and storage resources. It is charged on a per-terabyte and per-month basis.
Conclusion
This PostgreSQL vs. Snowflake discusses key differences between PostgreSQL and Snowflake. It also includes core concepts, features, and advantages of PostgreSQL and Snowflake. Thus, it will help you make a conscious decision about Snowflake and PostgreSQL as per your needs.
Author Bio:
Vinod Kasipuri is a seasoned expert in data analytics, holding a master’s degree in the field. With a passion for sharing knowledge, he leverages his extensive expertise to craft enlightening articles. Vinod’s insightful writings empower readers to delve into the world of data analytics, demystifying complex concepts and offering valuable insights. Through his articles, he invites users to embark on a journey of discovery, equipping them with the skills and knowledge to excel in the realm of data analysis. Reach Vinod at LinkedIn.