Google big query vs Redshift vs snowflakes

Overview :
Data warehouse architecture is rapidly evolving as the market for cloud storage grows. Because of their improved connectivity, integration, and low cost of ownership, businesses are migrating to cloud-based data-warehouses such as big query, redshift, or snowflake. Since many users who are putting together their data analytics stack ask us which data warehouse is best for their data-driven digital transformation: Google Big Query, Snowflake, or Redshift, we’ve put together a list of the best data warehouses. Despite the fact that Redshift, Big Query, and Snowflake are all very close. There are a few differences, however, that an individual should be aware of when selecting a method. In this blog, we are going to answer this question by providing differences between “Redshift vs Big Query vs Snowflake”.

Prerequisites –

• Before diving deep into cloud computing-based data-warehouses like redshift, big query, and snowflake and the nuanced difference that exists between them one must know thoroughly about data warehouses.
• The data warehouses are central repositories of integrated data that are typically used to connect, analyze and report business data from different sources within an organization. DWs store historical information about a business to analyze and extract insights from it.
• Some advantages of a data warehouse include Easy Error identification and correction, consistency of Data, and Faster Analysis.

Amazon Redshift :
Amazon Redshift is an Amazon web service cloud-based data warehouse that is managed and scales to petabytes. It is designed such that it is capable of handling a wide range of data storage and performing large-scale database migration. In this, troubleshooting, updating software, etc are not a matter of concern for end-users. It is an efficient solution for collecting data that can be analyzed to provide meaningful business insights. Irrespective of the size of the data, Redshift offers fast query performance. Redshift Architecture consists of nodes and clusters. Each cluster has a single leader node and multiple compute nodes. The receiving, parsing, and developing query execution plans are done by the leader node. The type and number of compute nodes depend upon many factors including the size of your data, the number of queries to be executed, and execution performance.

Advantages of Redshift :

1. High performance –
   Redshift provides high performance due to several factors such as Massively parallel processing, Columnar storage, good data compression, and query optimization. MPP enables redshift to fast execute complex queries. Moreover, the data stored in redshift uses a columnar storage arrangement that reduces overall input-output disk requirement which is responsible for optimizing analytic query performance. Data compression, on the other hand, increases query capacity by lowering storage requirements. All these factors help to improve the overall performance.
   
2. Extremely Fast –
   Redshift is lightning-fast when it comes to loading and querying data for analysis and reporting. It allows Massively parallel processing which helps to load data at a very high speed.
   
3. Huge storage capacity –
   Redshift being a data warehouse provides large storage capacity ranging from gigabytes to petabytes and more.
   
4. Security –
   Redshift offers a high degree of security. It has several features, including data encryption and access control options. In redshift, we can encrypt data in multiple locations. It allows encryption of data from data stored in the cluster to data in transit.

Snowflake :
Snowflake is a cloud-based completely managed Data warehouse that allows the building of a scalable, highly flexible cloud environment.  Snowflake can be used on AWS, Azure, and the Google Cloud Platform, and therefore it is considered as a multi-cloud data platform. Snowflakes can be used as both a data warehouse and as a SQL Data Lake because of their large data managing capability. Snowflake doesn’t require any hardware and software to install, configure, or manage. Moreover, all the Ongoing maintenance, management, and upgrades are managed by Snowflake itself, therefore it is considered a true SaaS offering. Snowflake can’t operate on private cloud infrastructures. Instead, all of its service components run on public cloud infrastructures. Snowflake’s cloud is powered on an advanced platform that is built by combining a new SQL query engine with an inventive architecture. Snowflake works as a combination of “shared-disk” and “shared-nothing” architectures. It processes queries with massively parallel processing compute clusters, similar to “shared-nothing” database architectures, and uses a centralized data repository for persisted data, similar to Shared disk database architectures.

Advantages of snowflake :

1. High-performance Queries –
   Snowflake allows enterprises to have speedy access to AVRO, JSON, ORC, and Parquet data and therefore providing a full view of your business and customers for better insights.
   
2. Limitless query concurrency –
   Snowflakes provide easy and flexible scaling of data as per requirement. As the demand rises data can be scaled up similarly it can be scaled down when there is no demand. It also allows the user to have simultaneous access to all the data.
   
3. Snowflake is a multi-cloud data platform –
   Snowflake allows its user to access 3 different clouds with high availability and secure data. Snowflake can be used on AWS, Azure, and the Google Cloud Platform.

Google Big Query :
Google Big Query is a fully managed, server-less data warehouse that is used for the analysis of over petabytes of data. It is a very efficient cloud-computing based data warehouse for analyzing large amounts of data to provide meaningful business insights. Google used this technology internally for over a decade for the analysis and reporting of data. Big Query’s data is secured, long-lasting, and highly available. With Google Big Query, you may acquire insights through real-time and predictive analysis. It also consists of machine learning capabilities. Google Big Query is a query engine that runs on Google’s Cloud Platform(GCP). GCP consists of projects to manage resources.  Once the Big Query API is set, the data can be stored in the Big query’s table. Big Query divides the data tables into smaller components known as datasets. Google’s Cloud Platform has a storage service called Google Cloud Storage (GCS). The Source data is loaded in GCS by default after every five minutes by the pipeline, which is then loaded into Big query using Big Query’s Batch Load feature.

Advantages of Google Big Query :

1. Big query allows testing of machine learning models using SQL queries –
   You can create, run and test machine learning models using standard SQL queries with the help of the Big Query ML feature. Big Query ML can be accessed through the user interface and REST API.
   
2. Scalability and cost-efficiency –
   Since Big Query is a “pay-as-you-go” cost model, for storage and querying, The cost of usage is variable which means the bill will be as per the usage per month. However, it also offers free, both storage and queries for the first 1TB usage. Moreover, it also has lots of free operations that do not incur costs such as Loading data into Big Query, etc.
   
3. Services provided by the big query is managed and maintained –
   All Big Query updates are supplied instantly to your systems, and there is no infrastructure to manage on your end.

Redshift vs Snowflakes vs Big Query :

1. Pricing –
   In terms of which data warehouse is the best, is concerned, money is the hardest to gauge. As far as Redshift is concerned, the size of the cluster needs to be predetermined. This means whether you query your data or not, you will be charged on hourly usage of the cluster. This makes Redshift more costly when there are low queries. But on the other hand, if the queries are uniformly distributed and are in large volume. Redshift could end up being a lot cheaper and hence it is predictable. In snowflake, Billing is based on the amount of data you store and the time spent, and therefore it is easily measurable and predictable. On the other hand, Google big Query charges as per the usage i.e. cost of usage are not fixed. Billing is done on the basis of the amount of data processed. Big Query may seem to be cheaper, but it can turn out to be expensive with the large volume of queries since Big Query is harder to predict.
   
2. Scalability –
   Redshift has a local storage configuration, and it requires cluster reconfiguration for resizing or changing machine instance type which requires a lot of time. In Google big query and snowflake both storage and compute are separated. As a result, more efforts have to be spent in redshift as compared to the other two cloud computing data warehouses since storage and compute are not separated in redshift.
   
3. Security –
   One of the most important aspects of buying Data warehouses is security. It’s important to realize that the data should never be exchanged with malicious third parties. All three data warehouse technology has security measures to protect your data. When you talk about Redshift, It provides various security features including load data encryption, database security, SSL connection, and many more Google’s Big Query also takes security as its prime concern. In this, all the data is encrypted and in transit by default. In a much similar way, Snowflake also provides tight security based on the cloud’s provider feature.

Conclusion

Finally, in the field of cloud-based data centers, Redshift, Big Query, and Snowflake are similar in that they provide cloud-based scale and cost savings. The biggest difference you’ll want to think about is how the services are billed, especially in terms of how this billing style can fit into your workflow. If you have a lot of data but a sporadic workload (i.e., you run a lot of queries sometimes with a lot of idle time), Big Query would probably be cheaper and simpler. Snowflakes can be more cost-effective if you have a more consistent, more continuous use pattern. When it comes to queries and the data you’re working with, you’ll be able to squeeze more queries into the hours you’re paying for. Redshift could just give you the flexibility to tune the infrastructure according to your needs if you have machine engineers.