Last updated on Nov 07, 2023
Snowflake is a cloud-based Software-as-a-service(SAAS) solution which allows data storage, preprocessing and analysis. It provides an all-in-one software package to support data collection from a variety of sources as well as storage, processing and analysis solutions. Snowflake does not employ any past or existing database technology. Still, its architecture, as well as query engines, are designed from the beginning, specifically to fit the public cloud infrastructure behind it.
All the data of Snowflake is stored on Amazon S3 using a central repository that can be accessible to all the computation instances; it is similar to the shared disk architecture. For its analytical computations, Snowflake uses virtual compute instances where massive parallel compute clusters process user clusters and handle user queries that, in turn, is the same as the share-nothing architecture. Snowflake, therefore, brings together the best of the two approaches to offer speed, flexibility, scalability, and adaptability.
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Oracle is a slightly more traditional database management system that began with relational RDBMS technologies and was improvised with object-relational and multi-model databases. From the past few years, Oracle has shifted its offerings to the Cloud to leverage the benefits of cloud technology. It also presented some of the modern SAAS offerings such as ERPs/CRMs/SCMs/IoT on the Cloud.
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Now let us go through the architectural difference between Oracle and Snowflake:
Oracle has been a leading databasd for running online transaction processing and analytical databases across the industry for more than 40 years. There are three main structures of the Oracle data server. They are Storage structure, process structure and memory structure. The objective of the storage structure is just to keep the data in the databases, and it has a more complex storage architecture. The process is divided into oracle database processes (for main compute), client processes (for user execution) and Oracle Demons, as well as application processes (for the background). The memory stores the shared data as well as the program code, and in the backend engine, there are System Global Areas(SGA) and Program Global Areas (PGA). Basically, PGA is a client-specific memory allocation space dedicated to the users, and SGA is a shared memory pool space connected to all the users.
Snowflake is a database technology newly brought into the Cloud. The basic design of Snowflake consists of three main components: Services, storage, and compute. Snowflake storage is the same as Oracle storage in which it stores data, which includes relational as well as semi-structured data, in databases. All data is stored in a single layer, so updates are made only once. Compute is the design component which runs the query computations. The dynamic computing infrastructure of Snowflake makes it possible for users to submit a large number of queries that Snowflake assigns automatically to the virtual warehouses, which are capable of accessing storage space without any restraint. The Services will coordinate the execution of all processes in Snowflake, including user verification and security, metadata handling and maintenance of optimized performance. The ability of Snowflake to manage a wide variety of usage scenarios with high performance offers the flexibility and availability needed for the increasing variety of data.
When we compare Oracle and Snowflake architecture, Oracle data servers have three main structures: Storage structure(to store the data), process structure(divided into oracle database processes for main compute, client processes and Oracle Demons and application processes) and memory structure(to store the shared data and program code). It also consists of a client-specific memory allocation space (PGA) and a shared memory pool space(SGA) connected to all the users. While Snowflake architecture includes the components Services, storage, and compute. Snowflake storage is the same as Oracle storage in which it stores data, Compute is to run the computations and services are to coordinate the execution of all processes.
Oracle Core Storage Units are referred to as data blocks which store persistent pieces of data which will be connected with each other. While the storage of Snowflake is arranged in micro-partitions rooted in a columnar format. Using this structure, the systems that run Snowflake do not need to read useless data but can identify which columns or rows are required by micro partitioning. By default, Oracle needs processing exponential amounts of unwanted data because it must read through complete rows. Many times, users are required to access information only in some column that Snowflake can retrieve easily because of its columnar format, whereas Oracle does not manage to do this effectively because it has to read a whole row before finding that column.
Now let us compare how Oracle and Snowflake differ technically and in terms of features, performance, and more.
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Following are some of the benefits of Snowflake:
Following are some of the drawbacks of Snowflake:
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Following are some of the benefits of Oracle:
Following are some of the drawbacks of Oracle:
In comparing Oracle with Snowflake, both are Scalable, and the best results are observed with the right size of the hardware. There are also higher costs associated with assigning more hardware on both platforms in terms of pricing. But it's also very tough to compare prices directly. Oracle charges the prices by OCPU and disk space, while Snowflake costs for operating warehouses, according to size. Since these warehouses are normally suspended when queries are not running, the calculation of the price is different from an Oracle ADW. But when the question "Which among Oracle and Snowflake databases is better?" then the answer will be "It depends on the needs of the organization".
In this blog, we have seen what Oracle and Snowflake are, their architecture, compared their features, and learned their pros and cons. We hope now you have enough knowledge of Snowflake and Oracle databases and their differences. For more blogs like this, Stay tuned to HKR Trainings.
As a senior Technical Content Writer for HKR Trainings, Gayathri has a good comprehension of the present technical innovations, which incorporates perspectives like Business Intelligence and Analytics. She conveys advanced technical ideas precisely and vividly, as conceivable to the target group, guaranteeing that the content is available to clients. She writes qualitative content in the field of Data Warehousing & ETL, Big Data Analytics, and ERP Tools. Connect me on LinkedIn.
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