Data Warehouse can be defined as the heart of Business Intelligence and it is used to analyze the heterogeneous data from various resources that are stored in it whereas Data Warehousing is the process of building and using the data warehouse. Various historical data are stored in the data warehouse and this data can be analyzed and can also be used to extract various useful insights from it. Data warehouse helps in enhancing the performance of an organization by analyzing the organizations data stored in the data warehouse. The heterogeneous data stored in the data warehouse is fetched from various sources and can be structured, semi-structured or unstructured data. Methods like cleaning, integration and consolidations of data are involved in data warehousing.
At present, Data Warehousing plays a key role for any organizations business. Various global companies like Amazon and Google are integrating cloud services with data warehousing to provide cloud-native data warehouse. These services are providing various business organizations with great manufacturing efficiency and enable them to gain insights from the data easily. As a result of cloud data warehouse services, many companies are switching away from the native, self-managed data warehouses. These cloud data warehouse services take less time and are more accurate with enormous data stored in the data warehouse. These services majorly focus on the analytical insights.
The present comparative study deals with the research on some popular cloud data warehouse services like Amazon’s Redshift, Google’s BigQuery and Snowflake.
The minimum common prerequisite for this research include:
- Basic knowledge about data warehouse, architecture and various terms related to the data warehouse.
- Basic information about data warehouse tools and techniques.
- Basic information about cloud-native data warehouse services like Redshift, BigQuery and Snowflake.
3. Understanding Amazon Redshift, BigQuery and Snowflake
3(a). Amazon Redshift Features
Amazon provides a large number of services under its Amazon Web Services and Amazon Redshift is one of the most popular cloud data warehouse services. It is easy to use and the large-scaled data stored in the data warehouse can be fully managed by it as it provides the services like updating the software and monitoring the data in the data warehouse. It can also handle data on the petabyte scale and run various queries in very less time. It takes complex and large size analytic queries and runs them by optimizing them by using petabytes of structured data.
The data structure provided by Amazon Redshift is columnar in form. A feature named spectrum is present in it and this feature allows the user to query the stored data in various formats available. Various jobs can be scheduled by the use of many AWS components for processing or loading the data as Redshift has the combination of AWS pipeline with itself. For constructing an Extract Transform Load (also known as ETL) pipeline and data warehouse, Redshift offers various features.
The architecture present in Redshift is known as nothing shared architecture and it can handle only 1,600 columns. The pricing plans for the services provided by Amazon’s Redshift are very pliable and there is a requirement of server for using it.
3(b). Google BigQuery Features
Google has its native cloud data warehouse service known as BigQuery. The technology used for building BigQuery was Google’s Dremel technology. BigQuery can provide analysis of massive datasets rapidly and it is capable of running queries with great speed on the data of petabyte-scale. Unlike Amazon’s Redshift, BigQuery is serverless and it does not require management of the cloud instances. Clustering is hidden in BigQuery as it happens in the background and it has a large list of features like interactive analytics, encryption and security, real-time analytics of data, data governance and many more.
The components used in BigQuery include an automated data pipeline for Extract Transform Load (ETL) known as Google Dataflow, Supermetrics for BigQuery a third-party source connector and the storage layer used is Google BigQuery. It also uses machine learning technology like Tensorflow for advance analytics.
BigQuery process the data in the form of read-only. For warehousing and dynamic data storage, BigQuery uses various ingest models. Like Redshift, BigQuery can also be fully managed and it performs optimization on data stored in the data warehouse by finding patterns of use and modifying the data structures for better results.
3(c). Snowflake Features
Snowflake is a data warehouse service which is completely managed. It is capable of scaling the data in the minimum time, uses a hybrid architecture of shared disk and shared-nothing architecture which consists of a storage layer, compute layer and cloud services layer. Snowflake cloud data warehouse requires the minimum need for end-user for running the data warehouse. Snowflake provides support for ANSI SQL and it is provided as Software as a Service (SaaS) to its user. It requires minimum database administration activities for table creation and querying of the data present.
Virtual Warehouse is used for running queries in Snowflake. There are multiple node clusters with memory and CPU present in the cloud provided by Snowflake. Virtual Warehouses have the facility of starting and stopping them whenever required and they can also be set to auto-suspend or auto-resume.
The database management is done by Snowflake itself. The user only needs to create tables, add data in the tables and then query it. There is no need for creating partitions or run vacuum operations that are required in Redshift.
4. Redshift vs BigQuery vs Snowflake: A Comparative Study
Redshift does not separate the storage from the computer resources and it provides local storage. There is no option for its users to independently scale the resources present, resizing of machine types requires reconfiguration of the cluster and this process consumes a lot of time. On the other hand, there is a strong focus on security in Redshift as it requires credentials for sign-in. The storage is not separated in Redshift and if there is a need for more storage space, the user needs to pay for the compute power. It has complexity in integration, requires vacuuming and it is difficult to manage huge tables. The one major drawback of Redshift is that maintenance services like updating the data are to be done by the end-user. It uses pay-as-you-go-pricing for its cloud data warehousing services.
On the other hand, BigQuery has a completely separate storage and a compute. For handling large data loads, there is an automatic and versatile offering for computing resources. It provides the best security for the data warehouse as all the data is encrypted. One of the major advantages of BigQuery is that it requires low maintenance, whereas, major disadvantages include that there are no constraints for columns are present in it. The services provided by BigQuery are fully manageable and backend configuration is managed by Google. Modern pricing models are used by BigQuery and it offers both fixed pricing as well as the option for pay-as-you-go.
Whereas, Snowflake provides separate data processing, data storing and data consumption facilities. It is capable of separating storage efficiently and it can slice trillions of rows very easily, the metadata services can be scaled up and down automatically by Snowflake. The security features are as per industry standards and equitable for a cloud-based data warehouse. It also requires low maintenance and requires zero management from end-users. It also provides a swift and automated supply for large compute resources. Snowflake provides pay what you use facility to its user and storage cost in it is cheap as compared to other providers.
As per the reports of Gartner & Dresner Advisory Services, Snowflake has received the highest rating because of the product, customer support and services provided by it. It has been rated 4.7 out of 5. Whereas BigQuery has been rated and 4.6 out of 5 and Redshift has a rating of 4.5 out of 5 (according to the reviews on Gartner Peer Insights).
These three cloud warehouses provides great performance and they use cost-based query planning and just in time compilation. The best cloud-based data warehouse for a user depends on individual needs. However, Snowflake provides its users with a great experience and services at a very reasonable price and is therefore, mostly preferred. Whereas, BigQuery can handle more than 1,600 columns but it has a delayed query response time and Redshift can handle maximum 1,600 columns and provides expensive services to its user. These reasons rates BigQuery and Redshift as the second choice for the cloud-based data warehouse.