In simple terms, data warehousing is a process used to collect and analyze data from multiple sources. The importance of data warehouses has been boosted with the availability of large volumes of business data that is now available for analysis and reporting among business users.
However, data warehousing is not a recent development and has been in the making since the late 1980s. Before the evolution of cloud technology, traditional data warehouses were operated on in-house infrastructure which was more expensive.
Additionally, these data warehouses contained data from selected sources including relational database tables and data files. Today, data warehouses include these data sources and additional sources including CRM & ERP systems, social media platforms, IoT devices, big data, and much more.
So, how has data warehousing for the enterprise evolved since the 80s? How does a data warehouse on the cloud compared with the traditional model? Let’s examine this and much more in the remaining sections.
What is an Enterprise Data Warehouse?
An Enterprise Data Warehouse (or EDW) is a centralized database (or a collection of databases) that stores business data from multiple data sources. This business data is then made available for data analytics and for deriving valuable insights.
In today’s age of big data, an enterprise warehouse is a central repository that collects data from multiple sources (as illustrated in the figure). This includes structured, unstructured, and partially structured data that goes through an ETL (extraction, transformation, and loading) process before being stored in the data warehouse.
According to Oracle, the enterprise data warehouse is a data management system that has been designed for performing business intelligence or BI.
With the availability of all the business data in a centralized warehouse, the data can now be analyzed using data analysis or BI tools for more accurate insights. For example, a data warehouse in an airline company can be used to determine the current crew assignment, frequent flyer programs, and air route analysis.
In recent years, companies have moved their enterprise data warehouse architecture from traditional in-house warehousing to cloud-based architecture. An enterprise cloud data warehouse provides the following business benefits as compared to the traditional warehousing model:
- Eliminates the need to purchase any in-house hardware for data warehousing.
- Offer lower upfront costs as compared to traditional warehouses.
- It offers higher scalability with an increase in available data.
- Faster processing of analytics and queries thanks to its massively parallel processing (or MPP) capabilities.
Next, we shall discuss the Enterprise data warehouse architecture along with its key components.
Enterprise Data Warehouse – Architecture & Key Components
First, we shall discuss the key components of the enterprise data warehouse architecture.
Typically, a three-tier enterprise data warehouse architecture consists of the following components, namely:
- Data sources or the databases that are storing the business data.
- Extract, Transform, Load (ETL) or Extract, Load, Transform (ELT), referred to as the ETL or ELT tools that are used for data extraction, transformation, and loading into the EDW. The main difference between ETL and ELT is that the ETL process includes a staging area where the data transformation is executed.
- Staging area component that is included only when ETL tools are deployed. This is the component where the extracted data is loaded and transformed before being stored in the data warehouse.
- Data warehousing database where the extracted data is loaded and transformed (only in the case of ELT) into the storage space.
- OLAP Server component used for online analytical processing of the data. This component allows business users to slice & dice the generated data for compiling business reports.
- Reporting components or tools that provide users with the BI interface for visualizing the data and report generation.
As a technological innovation, an enterprise data warehouse can support many workloads in any business enterprise, including manufacturing, financial reporting, supply chains, and customer satisfaction analysis.
Among the primary reason to migrate the data enterprise to the cloud, data scalability can be a major challenge as organizations keep adding new or more complex workloads. This growth can create multiple workloads including analytical and operational workloads.
For proper planning, businesses must consider the following key categories of EDW workloads:
- Auditable workloads, that include business legal requirements and operations for any company. These workloads include data for business compliance, risk, and government regulations.
- Sustained workloads, that are typically used by the management team to check key measures such as customer satisfaction, CRM, and other business-driving metrics.
- Variable workloads, that include any financial and operational reporting data that do not use EDW on a continuous basis. Ideal for cloud-powered EDW, these workloads are used for sales planning and compensation.
- Organizational reporting workloads, that comprise of metrics and KPIs, which measure business performance. This workload is ideal for an enterprise data warehouse in the cloud platform.
- Business unit-specific workloads, that are department-specific data and are among the first workloads that can be safely migrated to the cloud.
Now that we have an understanding of the EDW components and workload categories, let’s move on to the process of implement the EDW on a cloud platform.
How to Implement EDW on the Cloud
With the emergence of cloud-powered technologies, business enterprises are implementing (or migrating) their enterprise data warehouse in cloud platforms. Cloud-based data warehousing services are offered on technologies such as Amazon Redshift, Google BigQuery, and IBM DB2.
Before migrating your EDW to a cloud platform, any business must assess a few migration considerations. This is crucial to ensure that the cloud-based data warehousing workloads are integrated seamlessly with the in-premise workloads, thus minimizing any downtime.
Each of the following considerations can be used to determine the priority of the workloads to be moved to the cloud:
Define the required data sets and data volumes
For large data volumes, migrating the data warehouse from the premise to the cloud can be a long and time-consuming process. Hence, you must define the data sets early in the process. This ensures smooth connectivity for data movement and the design of a project schedule for a time-bound data migration process.
Evaluate your ETL tools for cloud operation
A cloud-powered EDW requires all your ETL processes and data to be enabled beyond in-premise warehousing. As a consideration, evaluate if your existing ETL tools are valid for cloud operation and can be integrated with cloud-based EDW technologies.
Assess your development team’s expertise in cloud technologies
Cloud-powered EDWs offer capabilities that can be a challenge for developers who are used to working on in-premise warehouses. Through proper training and learning, ensure that your project developers gain the necessary expertise and resources required to adopt a cloud EDW.
Estimate the overall costs of data movement
Business costs of moving data from an on-premise warehouse to the cloud can overshoot in the event of an inefficient migration process. As a business, evaluate the overall costs of migration before proceeding with the same.
Implementing data warehousing for enterprise on the cloud requires a phase-wise process comprising of new deployments as well as the migration of the existing workloads to the cloud.
Let’s discuss each of these phases in detail:
- Workshop phase
This is the initial phase where you can define the business metrics and KPIs for measuring the EDW project deliverables. For defining target workloads, engage and brainstorm with your team members including business analysts, BI programmers, and database administrators. You can also identify the use cases for the cloud-powered EDW.
- Assessment and planning phase
During the assessment phase, list and map out your workloads to data sets, database tables, and other structures. For better governance and compliance, define the necessary security controls. Additionally, you can define the roadmap for a minimum viable cloud (MVC) as well as the staffing for executing the MVC build and other EDW operations.
- MVC Build phase
In the third phase, design and implement the MVC build including elements such as connectivity, routing, access controls, and other deployment tools. Additionally, you should configure separate environments for development, testing, and production activities. Along with building database instances, you can configure the staging environment for data replication and transformation.
- Migration phase
Finally, we come to the migration phase where you can move your datasets to the cloud EDW platform. Additionally, you can implement the ETL processes during this phase to ensure integrity between the on-premise and cloud premise workloads. Identify the individual workloads (for development, testing, production) and migrate them along with the data workflows. At last, reconfigure your data analytical models for execution on the cloud.
- Operation phase
The final phase is the operational phase for executing an operational and automated response to any system events. During this phase, identify the operation team personnel and if necessary, train them to identify and respond to system events.
With this phase-wise implementation, you can complete the migration of your EDW system to the cloud. Next, we shall look at how a cloud-powered enterprise data warehouse benefits any organization.
Business Benefits of EDW on the Cloud
Cloud-hosted data warehousing for enterprise offer multiple business benefits including the following:
Lower operational costs
Any on-premise infrastructure involves multiple cost avenues including license costs, hardware, manpower, and even deployment and maintenance-related costs. For cloud-powered EDW, you only pay for what you consume. Additionally, cloud platforms free up your internal resources for other work which would otherwise, have been allocated to managing your data warehousing activities.
Higher business agility
Capabilities like effective data analytics and business intelligence are no longer just an IT domain but are now used by business users and departments. In this environment, it’s no longer feasible for business functions to wait for IT personnel to set up a data warehouse for them.
On the other hand, cloud deployments offer the advantage of faster and agile data warehousing for the enterprise and business users.
Enhanced security and protection
Cloud deployments have often raised business concerns about data security and privacy. This has been addressed by cloud-powered tools like Microsoft Azure and Google Cloud Storage that place a high value on their security measures. For example, measures like data encryption on the Google Cloud Platform has improved data security.
For any industry or business domain, data warehousing for an enterprise has its share of benefits and challenges. To simplify decisions, you must first understand your data-related needs and the feasibility of migrating your data warehouse to the cloud.
With its customized services in cloud engineering and analytics, Countants has enabled its global clients to move to the cloud platform and reduce their operational costs.
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