Today, organizations worldwide use cloud data warehouses as the foundation for effective data analytics and business intelligence. Cloud data warehouses eliminate the need to maintain fixed hardware and infrastructure. Some well-known cloud data warehouses are Snowflake, Microsoft Azure SQL, Google BigQuery, and Amazon Redshift.
The choice of a cloud data warehouse is influenced by several factors, making it difficult for organizations to make the right decision. It is essential to comprehend the fundamental characteristics of cloud data warehouses and the criteria against which they should be assessed.
The Best Cloud Data Warehouse Providers
Several reputable cloud data warehouses can meet an organization's big data and cloud computing needs.
Snowflakes
Snowflake can run on Google Cloud Platform, Microsoft Azure, Amazon Web Services, and more. Snowflake offers flexible pricing, separate processing and storage fees, and different storage resources.
Snowflake is a plug-and-play tool. It automatically triggers various security measures and automates numerous routine data management tasks, including metadata cleaning and updating.
Microsoft Azure SQL
Microsoft is best known for its SQL Server database, but it also offers a competing cloud storage platform. With its standardized syntax and interfaces, Azure SQL Data Warehouse provides a scalable and cost-effective way to process large databases. Compared to competitors, Azure Data Warehouse offers better control over indexing. Optimizing the underlying data structures of the Azure platform requires a thorough technical understanding. Thus, teams with good SQL skills can effectively manage data-driven systems.
Google BigQuery
BigQuery is a cost-effective data warehouse solution with machine learning capabilities. Combined with TensorFlow and Cloud ML, it can be used to create advanced artificial intelligence models. It also enables the retrieval of petabytes of data in seconds for real-time analysis.
This cloud-based data warehouse also supports spatial analysis. So, you can discover new business opportunities or analyze data based on location. With BigQuery, you can decouple data volume from data storage. So, you can tailor storage capacity and computing power to your business needs.
Amazon Redshift
Amazon's cloud data warehouse product is called Amazon Redshift. This is an excellent option for organizations that need continuous data storage and have already invested in AWS (Amazon Web Services). Redshift is scalable and offers web-based solutions, cloud delivery, and software as a service (SaaS). Redshift is the best choice for organizations operating in the AWS ecosystem as it is integrated with the full range of AWS services.
SAP HANA
SAP HANA is a cloud-based tool with in-memory caching. It supports enterprise-level data analytics and fast, real-time transaction processing. It also provides a single central platform for virtualization, integration, and data access.
With data federation, you can query external databases without migrating data. SAP Adaptive Server Enterprise (SAP ASE) and Hadoop are examples of such data sources. SAP HANA enables intelligent applications, as well as text analytics and predictive analytics.
Steps to Choosing the Right Cloud Data Warehouse
Knowing the details of a cloud data warehouse is essential, but to choose the best cloud data warehouse, consider the following factors:
Consider Your Business Requirements
Cloud data warehousing can be utilized across various business segments and domains. Still, you need to think carefully about how you plan to use the data warehouse, as vendor benchmarks may vary depending on usage scenarios and organizational characteristics.
For example, since Snowflake supports JSON storage and queries, an organization that wants to use JSON in its data warehouse may choose Snowflake instead of Redshift. Redshift requires a lot of configuration and administration, so organizations without a data warehouse manager should look for another data warehouse.
Internal Technical Data
Cloud data warehouses have different assumptions and data requirements. For example, Redshift does not allow semi-structured data, but Snowflake does. Therefore, Redshift (because it supports structured data) makes additional assumptions about the data structure that affect the aggregation schema. However, since Redshift needs to consider unnormalized data structures, it may take longer to search the data.
It is, therefore, essential to be aware of the level of customization and the technological components required by the organization. If an organization needs to store semi-structured data, Snowflake's more scalable structure may be more appropriate.
Security
Choose a data warehouse that meets your organization's security requirements. Most data warehouse providers address vulnerabilities in their systems and update them regularly. However, you may be concerned about the default configuration of your system. For example, Google BigQuery encrypts data in transit and at rest by default. In contrast, Amazon Redshift requires that database encryption be explicitly enabled.
Scalability
Scalability and flexibility regarding storage and computing resources are two essential aspects of the cloud. Leading cloud data warehouse services are based on massively parallel processing (MPP) systems that enable scalability in periods of high compute resource demand and cost reduction in periods of low demand. Leading cloud data warehouse providers offer manual or automatic scaling options.
An Explanation of The Combined Approach
Each option for building a cloud data platform must be cost-effective and align with specific business objectives. Each option must fit into the overall concept. Assessing design and business constraints, such as financial, time, and organizational resources, ensures that the plan gives you confidence in the project's success. Ensure that stakeholders are involved in the planning and implementation process.
Plan-Driven Implementation
Harnessing the benefits of the cloud is the most efficient way to reap the benefits while reducing risks. As cloud applications evolve, the plan must ensure that more value is created. This way, operational and financial benefits can be demonstrated to fund further migration projects. Additional changes can be easily made once critical workloads have been moved to the cloud. With a well-thought-out plan, migration to the cloud is entirely possible and does not need to be tied to a single platform and technology. It is essential to be decisive and purposeful, especially if you have doubts about the stability and maturity of your existing cloud computing platforms.
Conclusion
Choosing the ideal cloud data warehouse solution for your organization can take time, as several factors affect the quality of system performance. However, an organization can assess the relevant variables and select the best data warehouse for its needs, considering its planned usage scenarios and workflows.
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