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Enterprise Data Warehouse (EDW) Solutions
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What is an Enterprise Data Warehouse?
An enterprise data warehouse (EDW), is unlike a traditional data warehouse which might only contain data for a specific department. An EDW is designed to be a single repository where all of an organization’s data is collected and stored. Data within an EDW may be further divided into “data marts” to allow for quick access to relevant data at a departmental level.
One of the key strengths of an EDW over a data lake (which can hold both unstructured and processed data), is that all of the data has already been cleaned and formatted. This allows queries to run in seconds rather than minutes or hours.
Enterprise data warehousing has been adopted across countless industries as a way to extract the most value from the ever-growing amount of data that modern companies now have at their disposal.
Explore the Core Components of an EDW
Data Integration
A standard EDW collects business data from a vast array of sources such as CRM (customer relationship management), ERP (enterprise resource planning), and finance. For this data to be quickly accessible, it needs to be cleaned and formatted into a common EDW schema.
The best EDWs offer seamless data integration solutions with near real-time CDC (change data capture) and ETL (extract, transform, and load), transforming any raw data into a rich vein of actionable insights.
What is the difference between ETL and ELT?
Both ETL and ELT are common data management techniques that follow the same basic steps:
Extract: Pulling source data from original databases.
Transform: Reformatting and cleaning of data.
Load: Depositing the data into a data storage system.
The only difference between ETL and ELT is the order in which they carry out these steps. ETL transforms data first in a staging area before loading it into the database. ELT loads the unstructured data first, ready to be transformed later.
ELT might be a suitable approach when there is no need to query vast and diverse data formats. However, for a truly fast enterprise data warehouse, the greatest advantage of ETL is that, with all data already structured, queries can be run in a fraction of the time.
Data Modeling
Data modeling analyzes individual sets of data and seeks to define the relationships between them, organizing clusters of information into corresponding silos. There are numerous ways to structure your data such as relational, hierarchical, and network models.
The goal of data modeling is to make it easier to build meaningful, data-driven insights. For example, querying year-on-year sales might offer a basic overview of a company’s growth, but how can it be used to drive growth?
If that data can then be queried in real-time based on demographics, regional variations, product performance, etc. then those insights become actionable. That’s the power of data modeling.
Data Governance
Data governance ensures the quality and security standards of an enterprise data warehouse.
- Data quality management: Ensuring that data is accurate and reliable. This includes appropriate data cleaning at the integration stage and ongoing quality validation.
- Data security: As any EDW is likely to contain sensitive data, it’s essential to use the latest security measures. Physical and digital security, as well as industry-specific standards, should be applied.
Presentation Layer
The presentation layer is the point at which the individual interacts with the EDW. Powerful BI tools, query options, and reporting allow you to transform that wealth of information into detailed insights and actionable ideas.
Exasol is ushering in the future of data analytics with the integration of the advanced AI program, Veezoo. Enabling users, regardless of technical ability, to query data using plain language.
Understanding the Different Types of EDWs
Choosing between a traditional on-premises data warehouse, a cloud-based solution, or a hybrid of both depends on what your company needs regarding control, scalability, and accessibility:
A virtual warehouse connects multiple databases to create a single system that can then be queried.
Setting up a virtual EDW can be beneficial as it requires minimal change to the underlying infrastructure. However, query time, maintenance costs, and system complexity are all likely to be higher.
A virtual warehouse connects multiple databases to create a single system that can then be queried.
Setting up a virtual EDW can be beneficial as it requires minimal change to the underlying infrastructure. However, query time, maintenance costs, and system complexity are all likely to be higher.
An enterprise cloud data warehouse can be run through your existing public cloud account. This allows data to be accessed from anywhere in the world, whenever it is needed.
A hybrid approach between on-premises and the cloud can combine the best of both worlds: the control of an on-site solution with the accessibility of the cloud.
Adopting a SaaS model is one of the quickest and most straightforward ways of streamlining your enterprise data management.
With a simple setup, instant scalability and flexible payment structures, SaaS enterprise data warehousing is a great option for any business that wants to hit the ground running and avoid any future roadblocks to growth.
Benefits
Key Benefits of EDWs at a Glance
Enables greater data-informed decision-making
Instant insights based on real-time data
Enhanced security against unauthorized access and cyber threats
Homogenized data from across an entire company
Democratizes data and makes it accessible to all
Reduce data storage and management overheads
Unlocking Data Power
Financial Services
For more than three decades, the biggest retail and e-commerce companies have depended on streamlined data integration to drive growth through detailed customer insights.
Healthcare
Healthcare providers use the invaluable insights offered by EDWs to reduce patient harm and improve overall satisfaction.
Retail and eCommerce
For more than three decades, the biggest retail and e-commerce companies have depended on streamlined data integration to drive growth through detailed customer insights.
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