The computing world nowadays is growing exponentially with the introduction of big data, machine learning, data analytics, etc.
During these transformations in business intelligence BI and data warehousing, the modern data warehouse has proven to be a reliable and continuous technique for managing integrated data.
A data warehouse is a system that is used for data analysis and reporting which is the core of business intelligence (BI) as all the analytical sources depend on the data warehouse platform. It is a central repository where current, as well as older data, are taken from a variety of sources and are stored in one place.
These reports are helpful for the organizations in understanding or predicting the patterns in their sales and forecasting which helps in formulating market strategies accordingly.
Hence companies nowadays are opting for data warehouse solutions to scale their business capabilities such as Microsoft azure data warehouse.
This Microsoft cloud data warehouse as a service has been proved as one of the most effective and dependable data solutions in recent times.
Data warehouses are located generally on-premises after purchasing the server rooms, some hardware, and specialized people to run it. All kinds of businesses, irrespective of their size, are migrating to the cloud.
A cloud-based data warehouse such as the Azure data warehouse service enables to access of almost unlimited computing power and storage space from the laptop.
Recently organizations are using a Confluent, a streaming data service platform with confluent Kafka cloud, for real-time streams.
One of its surveys shows customers increase speed to market by up to 75%, and reduce total cost of ownership by 60% with Confluent Cloud.
According to the survey, only 21% of companies are planning to stick with a single cloud provider. A hybrid option having on-premises and cloud data warehouses is the business intelligence data warehouse strategy for 57% of companies, while 22% are opting for a multi-cloud data warehousing approach.
Major reasons to get migrated to cloud data warehouse
Some of the reasons to get migrated to the cloud data warehouse and reap the full benefits of the cloud data warehouse are summarised as:
Cost Efficiency
With on-premises data warehouses, scaling the size of your data warehouse or improving would simply mean procuring new hardware, server rooms, and specialized staff to look upon which is quite expensive.
With most cloud DWH such as data warehouse in AWS, you pay only for what you use without any risk of purchasing new hardware components.
Scalability and Elasticity
Cloud data warehouses are elastic which means that you can add or remove computing resources according to your will.
Businesses with cyclical demand get more benefits from this DWH. When demand is high, businesses can add storage.
When demand drops, it’s easy to reduce storage which saves money too.
With cloud-to-cloud migrations, switching between one cloud computing providers to another can occur easily.
Speed and Performance
Running too many queries in a database at one time can quickly reach computing capacity on an on-premises data warehouse.
On the other hand, the cloud data warehouse can handle large numbers of transactions simultaneously.
That means if the analytics teams want to connect with CRM, ERP, and marketing data simultaneously, they can transfer data without sacrificing speed or performance.
This has resulted in building a data warehouse with the most advanced technology so that complex integration can be faster & easier.
Reduced Database Management
Cloud data providers like AWS data warehouse have a high level of reliability and in case of any failure in the data warehouse platform
process; they update and manage the issues on their own.
This is the advantage over an on-premises data warehouse platform where there is some failure; it is your responsibility to fix it.
Disaster Recovery
In a traditional on-premises data warehouse, for data recovery, there is a requirement to invest in “backup” datacenter, which is an additional cost.
Cloud data solutions offer automatic backups, duplicates, and snapshots which makes disaster recovery simpler and cheaper.
Steps to follow while opting cloud data warehouse
The process of migration to a cloud varies from industry to industry based on data volume, compliance requirements, company size, and other factors.
Based on the several cloud migration use cases, some useful guidelines are described below.
- Choosing right architecture
There are two types of architectures for cloud data warehousing systems.
- Cluster-based architectures
- Serverless architecture
Cluster-based architecture is a pool of shared computing resources that are referred to as Nodes. Each node in the cluster consists of RAM, CPU, and storage space.
The number of clusters in cluster-based architecture determines the price and hence the total cost incurred is easily predictable.
In Serverless architecture, the hardware allocation is taken care of by the managed service provider. The queries are executed at a given time once you pay for storage. The price is not easily predictable.
- Create a solid data model and data flow blueprint
The data modelling and data flow blueprint and other critical factors must be taken into consideration while migrating to the cloud.
The data model must always be focused on minimizing the data stored while maximizing the value derived out of it without any compromise.
- Choose ELT over ETL
There are two types of the sequence of the integration process:
- ETL: Extract, Transform, and Load
- ELT: Extract, Load, and Transform
In sequence ELT, firstly data is loaded into the data warehouse and then transformed to the appropriate format. The transformation also happens within the data warehouse, and hence, there is no requirement for a staging environment.
But in ETL, the staging environment is a mandatory requirement since the data transformation occurs outside of the data warehouse before loading it.
The best practice is to choose ELT and ETL in a hybrid model according to the compliance requirements.
- Go agile
The agile methodology is the best for cloud migration projects, especially if the data volume is too high, and there are skilled resource limitations.
This also helps in quickly adaptation to any changes coming during the project.
- Don’t overlook user adoption
Equipping the users with proper knowledge and training in cloud warehouse is very essential.
For example, in a serverless model, a wrongly executed query (use of large space) could even cost $1,000. Thus, it is important to assist users to query the data appropriately so that huge spending can be avoided on a single query.
Explore some technologies/tools in warehouse.
Conclusion
Due to the efficiency of the data warehouse as a service and cloud in the system, several cloud data warehouse providers such as AWS, Microsoft Azure, Google Cloud, Snowflake, and Databricks, are grabbing the market share currently.
But still, there is a need to handle the larger computation that can run complex data warehouse analytics efficiently. This has paved the way for a modern data warehouse with improved infrastructure such as real-time data warehouse. Due to data warehouse modernization, applications based on predictive analytics are being developed without disrupting the system that products and business intelligence rely on. Get the best expert advice and assistance for modernized data warehouse solutions for your business acceleration at CG-VAK.