June 20, 2023
An enterprise data warehouse (EDW) is a repository that centralizes enterprise-wide data coming from heterogeneous sources to prepare it for further analysis. An enterprise DWH breaks down data silos and makes corporate information accessible for companies to make timely data-driven decisions.
the projected enterprise data warehouse market size by 2030
Market Research Future
Scheme title: The most important data warehousing-related trends
Data source: yellowbrick.com — Key Trends in Hybrid, Multicloud and Distributed Cloud for 2021
What are the most important data warehousing-related trends for your company this year? Select all that apply.
Scheme title: Companies’ investments in analytics infrastructure
Data source: yellowbrick.com — Key Trends in Hybrid, Multicloud and Distributed Cloud for 2021
In the current climate of uncertainty, our investment in analytics infrastructure such as data platforms, data warehouses, etc. is:
An enterprise data warehouse is more than a repository connected to your data sources (CRM, IoT devices, SaaS apps, etc.) on one end and to BI and analytics software on the other. In truth, an enterprise data warehousing solution is a comprehensive data processing and storage environment that consists of the following components:
Extract, transform, load (ETL) or extract, load, and transform (ELT) tools ingest information from the source systems and process it until it’s suitable for permanent storage. It’s one of the core elements for enterprise-grade analytics because companies have to rely on numerous data sources with different data types, models, and information generation speeds.
A staging area is a temporary raw data repository between data sources and its permanent storage that hosts the data during the transformation stage. This element is typical for solutions built with the ETL approach but can be omitted if the transformations are performed in the data warehouse database.
Traditionally, an enterprise data warehouse database is a relational database where the integrated and subject-oriented business information is loaded into data models for analytical querying. This component also includes a metadata repository where an enterprise stores a map of its data for easy access and handling.
Dimensional data marts are built to satisfy the analytics needs of specific user groups and departments, for example, sales and marketing, production, supply chain management, and finance. Data marts facilitate easier and quicker data access and analysis as they handle smaller, more applicable datasets.
Deploying multidimensional OLAP cubes that store data in the pre-aggregated form helps overcome the limitation of relational databases and streamline data analysis. The data in OLAP cubes can be sliced and diced, drilled down, rolled up, and pivoted to handle various analytics requests of business users.
The data governance component defines processes and policies for managing data quality and security, data modeling, metadata, data retention and backup, data usage, and user activity.
Traditional enterprise data warehouse solutions are built according to the three-tier architecture, which includes:
However, the architectural approach should still be dictated by the company’s needs, so other design methods (e.g. a one-tier or a two-tier architecture) can prove more suitable in some cases.
An enterprise data warehouse is not a specific software type but an environment combining multiple technologies. Together, they enable the following functionality:
To serve the needs of various users across the company, the enterprise data warehouse should integrate data from all sources defined by the established analytics objectives at the required granularity level. Among the most commonly integrated data sources are:
When setting up an enterprise data warehouse, businesses have to choose between a cloud, on-premises, or hybrid environment.
On-premises
Cloud
Hybrid
An in-house or outsourced IT team on-premises deploys DWH on the local server
A cloud data warehouse is hosted and managed in the cloud. All hardware-related costs, software setup, infrastructure audits, and maintenance are the provider’s responsibility (if a DWH is delivered as a managed server).
A hybrid data warehouse is distributed across both cloud and on-premises environments
On-premises
Cloud
Hybrid
Comprehensive control over the data warehouse hardware and software infrastructure High availability and security Compliance with data regulations, which require keeping data onsite
Quick deployment and fast and cost-effective storage and computational resources scaling up and out Minimized upfront costs due to a pay-as-you-go model High fault tolerance and disaster recovery due to the distributed nature of the cloud data warehouses
Efficient operation in the cloud while meeting the strictest regulatory requirements and addressing data latency issues
On-premises
Cloud
Hybrid
Heavy upfront investments for hardware acquisition, software licenses, IT resources, etc. Requires comprehensive capacity planning due to complicated scaling Requires an experienced IT team to keep the system running efficiently
Failure to meet compliance requirements prohibiting cloud data storage Lack of pricing transparency and complicated pricing structures (e.g., egress fees, extra pay for hot data storage, excess compute, geo-redundancy)
High price due to purchasing hardware and software and paying for the cloud resources Requires solid expertise in development and maintenance
We recommend starting the data warehouse selection process by reviewing the solutions that are recognized leaders in the recent Forrester Wave and Gartner Magic Quadrant reports.
As the field of data analytics matures, the diversity of data warehouse software technologies can become overwhelming. So when assessing enterprise data warehouse technologies, a company should consider multiple factors to select the right tech stack.
Enterprise data warehouse costs include various categories of implementation and maintenance costs, such as:
Data volume, its type, complexity, and quality
The number of data sources and their disparity
The number of data flows and data modeling complexity
Type of workload to support
Data cleansing and transformation complexity
Data security and compliance requirements
Platform scalability, fault tolerance, and velocity
Real-time reporting requirements
Software license fees
Solution management
Integrating enterprise-wide information typically scattered across multiple systems, companies can perform analysis of cross-functional historical data and carry out business performance assessment, risk analytics, or strategic planning.
An enterprise data warehouse serves as a solid and well-governed foundation for new analytics initiatives – big data analytics, predictive analytics, self-service BI, ML, and AI.
As companies across industries try hard to embed data-driven insights into every business decision, customer interaction, and business process, data warehousing is gaining traction as a key enabler. There has never been a better time to get more value out of data, with the growing information volume, increasing computing powers, and more advanced and affordable technology. Since implementing an enterprise data warehousing solution requires solid expertise, consider bringing a trustworthy DWH consultant to your project. At Itransition, we offer a full range of DWH services to help companies design and introduce a scalable and future-proof data warehouse and use it to accelerate decision-making and gain a competitive advantage.