What is SnowFlake?
Snowflake is an analytical data warehouse provided as Software-as-a-Service (SaaS). Snowflake provides the user with a data warehouse that is quicker, easier to use, and far more flexible than any other traditional data warehouses. Basically, the Snowflake’s data warehouse structuring is not built on an existing database or big data software platform such as Hadoop. The Snowflake data warehouse uses a new SQL database engine with a unique architecture designed only for the cloud. To the user, Snowflake has many similarities to other enterprise data warehouses but also has many unique functionaries and capabilities
The Snowflake brings to you an architecture that is hybrid of traditional shared-disk database architectures and shared-nothing database architectures. Similar to shared-disk architectures, It uses a central data repository for existing data that is accessible from all nodes in the data warehouse. But it is similar to shared-nothing architectures, Snowflake processes the queries using MPP (massively parallel processing) compute clusters where each and every node in the cluster stores a portion of the entire data set locally. This kind of approach offers the data management advantages of a shared-disk architecture, but with the higher performance and all the benefits of a shared-nothing architecture.
Benefits of using SnowFlake:
- A Multi-cluster Shared Data Architecture across any Cloud
- Most secure Data-sharing and Collaboration
- PaaS (Platform as a service) with Zero Maintainance
What is Hadoop?
Hadoop is an open-source framework developed by Doug Cutting at Yahoo and it was made open source in 2012. Hadoop allows companies to implement a distributed processing of large data sets across clusters of computers using some simple programming models.
The idea behind Hadoop was enabling companies to scale up from single servers to thousands of machines offering local computation and storage. That way, businesses could solve problems that involve massive amounts of data and computation. No wonder that since 2012, Hadoop gained considerable traction as a possible replacement for data warehouse applications running on costly MPP appliances.
Benefits of Hadoop
- Open Source: Its Source code is available, you can modify, change as per your requirements.
- Meant for Big Data Analytics: It can handle Volume, Variety, Velocity & Value. Hadoop is a concept of handling Big Data, & it handles it with the help of the Ecosystem Approach.
- Ecosystem Approach: (Acquire, Arrange, Process, Analyze, Visualize ) Hadoop is not just for storage & Processing, Hadoop is an ecosystem, that is the main feature of Hadoop. It can acquire the data from RDBMS, then arrange it on the Cluster with the help of HDFS, after then it cleans the data & makes it eligible for analyzing by using processing techniques with the help of MPP(Massive Parallel Processing) which shared-nothing architecture.
- Shared Nothing Architecture: Hadoop is a shared-nothing architecture, which means Hadoop is a cluster with independent machines. (Cluster with Nodes), that every node performs its job by using its own resources.
- Distributed File System: Data is Distributed on Multiple Machines as a cluster & Data can stripe & mirror automatically without the use of any third-party tools. It has a built-in capability to stripe & mirror data.
Key comparison metrics
| Hadoop | SnowFlake | |
| Definition | Open-source Framework | Data Warehouse |
| Where can it be placed | On-Premise | Cloud-Based |
| Features | Hadoop offers no ACID compliance: It writes immutable files without allowing any updates or changes. To change a file, users need to read it in and write it out with the applied changes. | SnowFlake supports multiple concurrent read-consistent reads. It also supports updates in compliance with ACID |
| Data Storage | Hadoop breaks down data into fixed-size blocks replicated across three nodes. Which is not a good solution for small data files under 1GB, where the entire data is usually set on a single node | Snowflake can scale up from a small to large data warehouse within seconds, and also the other way round. |
| Pricing | Traditional, Mainly requires capital expense on-premise or software deployment and management in the cloud | Completely Pay-as-you-go model |
| Maximum number of nodes | 1000 or more is possible, 100s are typical | 4XL*10 which equals 1028 nodes per virtual warehouse |
| Minimum Data size | Data under 1GB should be avoided, Hadoop doesn’t work well with small data | Snowflake supports data of all sizes from Kilobytes to petabytes |
| Tools supported | Mainly open-source with some support for 3rd party tools using ODBC and JDBC | An extensive array of data management and Business Intelligence tools, with various dedicated interfaces |
| Deployment complexity | Extremely high, needs highly skilled professional support and system managements | Simple, zero expertise needed to deploy Snowflake |
| Data velocity | Batch or Real-Time | Batch or Real-Time |
Conclusion:
Hadoop is expensive to deploy and manage and offers pretty poor support for low latency queries many Business Intelligence users may need. Hadoop is a good solution for a data lake, an immutable data store of raw business data. However, Snowflake is an excellent data lake platform as well, thanks to its support for real-time data ingestion and JSON. Snowflake offers high performance, query optimization, and low latency to stand out as one of the best data warehousing platforms on the market today. Although using it comes at a price, the deployment and maintenance are easier than with Hadoop.
