What is a Data Warehouse?
As businesses grow, they need increasingly robust analytics to evaluate their performance. Although some organizations attempt to use databases for their analytics, a data warehouse is better suited to take up the task. Why?
What is a Data Warehouse and How Does It Work?
A data warehouse is a large collection of business data used to help an organization make decisions. It is the foundational component of business intelligence efforts.
The concept of the data warehouse has existed since the 1980s, when it was developed to help with the transition of information from operations to decision support systems. These systems were needed to analyze large amounts of data coming from different places over varying periods of time.
A data warehouse can be formatted to serve the unique needs of any organization, but the general process remains the same.
The data warehouse periodically pulls data from one or many sources. Then, the data goes through formatting and import processes to match the data already in the warehouse. The data warehouse stores this processed data so it’s ready for decision makers to access.
How frequently data pulls occur, how data is formatted, etc., varies depending on the needs of the organization. Data coming into a data warehouse is transformed from its source, based on the analytical needs of the company. It is stored in the warehouse so it can be easily accessed via query, aiding in business decisions in near-real time. Business data warehouses, therefore, can support future decision making – and many major corporations use these systems for that purpose.
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Data Warehouse vs. Database
It’s easy to confuse a data warehouse with a database , as both concepts are similar at a high level. The primary difference, however, comes into effect when a business needs to perform analytics on a large data collection. Data warehouses are made to handle this type of task, while databases are not. Here’s how to tell the difference between the two:
What it is
Data collected for multiple transactional purposes. Optimized for read/write access.
Aggregated transactional data , transformed and stored for analytical purposes. Optimized for aggregation and retrieval of large data sets.
How it’s used
Databases are made to quickly record and retrieve information.
Stores data from multiple databases and makes it easier to analyze.
Databases are used in data warehousing. However, the term usually refers to an online, transactional processing database. There are other types as well, including csv, html, and Excel spreadsheets used for database purposes.
An analytical database that layers on top of transactional databases to allow for analytics.
Data Warehouse vs Data Mart
Data warehouses are also sometimes confused with data marts . But data warehouses are generally much bigger and contain a greater variety of data, while data marts are limited in their application.
Data marts are often subsets of a warehouse, designed to easily deliver specific data to a specific user, for a specific application. In the simplest terms, data marts can be thought of as single-subject, while data warehouses cover multiple subjects.
Benefits of a Data Warehouse
Organizations that use a data warehouse to assist their analytics and business intelligence see a number of substantial benefits compared to those that try to rely on databases:
Better data - Adding data sources to a data warehouse enables organizations to ensure that they are collecting consistent data from that source. They don’t need to wonder whether the data will be accessible or outdated as it comes in to the system. This ensures higher data quality and data integrity for sound decision making.
Better decisions - Decision makers who are accustomed to having quick access to data may be used to making decisions based on hunches and high-level, incomplete data. Data warehouses provide the analytical power and a more complete dataset to base decisions on hard facts.
Faster results - Data in a warehouse is ready to be analyzed, which helps organizations make good decisions faster. Manually extracting, formatting, sorting, combining, and/or analyzing information from multiple databases almost immediately puts an organization behind the curve.
In the healthcare industry , for example, a hospital might collect EHRs to report on statistics such as hospital admissions or clinical gap closures. However, these reports by themselves have limited analytical potential. With a data warehouse, aggregated EHRs can also reveal reasons for hospital readmissions and factors leading to clinical gap closures. These kinds of analytical connections can give organizations a significant leg up.
Or in retail, a sales manager can track trends in sales performance over time using simple databases. With a data warehouse, however, that manager can quickly find connections in data that factor into sales performance (perhaps seasonality or particular promotions), helping the team achieve greater success in the future.
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A Data Warehouse Example
As a leading provider of fitness, nutrition, and weight-loss programs, Beachbody needed big data integration to better serve customer needs. Better targeting and personalizing to customers means not only better business performance for Beachbody, but better health outcomes for clients.
The company revamped its analytics architecture by adding a Hadoop-based cloud data lake on AWS, powered by Talend Real-Time Big Data . This platform has allowed Beachbody to reduce data acquisition time by five times, while improving the accuracy of the database for marketing campaigns.
For more examples of how data warehouses benefit businesses, visit the Talend customers page.
Data Warehouse Architecture
The basic data warehouse structure includes several architecture components. There are data sources and targets; a framework for extraction, transformation, and loading ; and an application layer for access.
An additional component of many data warehouses is the staging area. This is a workspace where data is transformed and enriched before it comes into the warehouse. This provides an important step for many businesses in ensuring that data is as comprehensive and actionable as possible.
Some data warehouses also include one or many data marts, as discussed above.
3 Approaches to Data Warehousing
Data warehouse designers don’t follow a single approach to creating warehouses. Instead, there are three general approaches that guide the process.
Bottom-up design begins with the data mart as the basic unit of reporting capability. Data marts grow and combine to gradually form an integrated data warehouse. The integration of these data marts relies on data warehouse bus architecture. The data marts in a bottom-up design are generally consistent and quick to access. They also can be easily extended.
Top-down design takes the reverse approach, beginning with the data warehouse and building out data marts from the larger warehouse. This can provide much more consistent dimensional views across the warehouse, and be more adaptive to business changes. It can also be less flexible and more expensive.
Some organizations also build out data warehouses from both directions, integrating data marts into a larger data warehouse in a hybrid approach.
Cloud and Open Source Data Warehouse Tools
Moving data to the cloud offers many businesses advantages that help them stay competitive in today’s markets. The flexibility, collaboration, and anywhere-access can give the workforce a major boost.
As businesses make the move to the cloud, so too do their databases and data warehousing tools. Popular tools like Amazon Redshift , Google BigQuery , and Microsoft Azure SQL Data Warehouse have all offered businesses simple ways to warehouse and analyze their cloud data.
Meanwhile, open-source tools have provided a foundation for businesses to implement solutions that are efficient and affordable:
Data warehouse tools for integration and ETL — Talend Open Studio for Data Integration is a powerful, easy-to-use data integration and ETL tool that's been downloaded millions of times since it was first launched in 2006, and has hundreds of thousands of active users.
With an Eclipse-based graphical development environment, more than 900 components and built-in data connectors, a unified metadata repository, automated generation of Java code, and robust ETL testing functionality, Talend Open Studio for Data Integration dramatically boosts developer productivity and reduces time-to-value for ETL data warehouse projects.
Data warehouse tools for data management and MDM — Talend Open Studio for Data Integration and Talend Data Integration are feature-rich data warehouse tools that support the design, development, and deployment of data extraction, data conversion, and data loading operations, as well as related integration processes like data replication and data synchronization.
Talend Data Management Platform makes it easy to enhance the quality of data before loading it into your data warehouse, with support for normalization, de-duplication, and enrichment by reference to internal or third party standards (such as census data or international postal standards).
Talend MDM Platform provides comprehensive support for master data management and enterprise data governance.
Modern businesses of all sizes have significant data analysis needs, which makes cloud-native and open source data warehouse tools indispensable.
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