Data Warehouse: Stepping into the Future of Cloud and Automation

The future of data warehouse is anticipated to be in the emergence of cloud and automation

Companies are increasingly valuing data more than anything else. Businesses are considering data as a priceless asset they own. However, even though data plays a big role in decision-making, it gets its value when data is stored, aggregated and analyzed. Raw data is as useless as immovable property. This is where data warehouse gets streamlined. The future of data warehouse is anticipated to be in the emergence of cloud and automation.

What is data warehouse?

A data warehouse is a type of data management system that is designed to enable and support business intelligence (BI) activities, especially, analytics. Data warehouses contain large amounts of historical data in which it performs queries and analysis. The data stored in a data warehouse usually come from a wide range of sources like application log files and transaction applications. Remarkably, data warehouse centralizes and consolidates large amounts of data from multiple sources. It helps improvise the decision-making process by deriving valuable business insights from raw data. Over time, it builds a historical record that can be invaluable to data scientists and business analysts. Besides, data warehousing is the process of constructing and using a data warehouse. Data warehousing involves data cleaning, data integration and data consolidation. Some of the benefits of using data warehouse are listed below,

• The root of the collected data is very important. Data warehouse provides the facility to add the data source in its base. This ensures that companies are collecting consistent and relevant data from the source.
• Businesses don't have to worry about the quality of data as they are accessible or inconsistent as it comes into the system. This ensures higher data quality and data integrity for enhanced decision-making.
• The consistent format of the data warehouse accelerates data to be analyzed anytime. It also leverages the analytical power and a more complete dataset to base decisions on hard facts. Henceforth, decision-makers no longer can keep away from relying on hunches, incomplete data, or poor quality data and risk delivering slow and inaccurate results.
• At data warehouse, data is copied, processed, integrated, annotated, summarized and restructured in a semantic data store in advance, making the analytics process easy.
• Besides, data warehouse allows organizations to analyze large amounts of variant data and extract significant value from it in four particular ways namely subject-oriented, integrated, non-volatile and time-variant.

History of data warehouse

Even though the concept of data warehouse came to light in 1980, its footprints are seen way back in 1960 when Dartmouth and General Mills developed the term dimension and facts in a joint research project. In 1970, Nielsen and IRI introduced dimensional data marts for retail sales. Tera Data Corporation launched a database management system that is specifically designed for decision support. The first implementation of a data warehouse came into existence in the 1980s when IBM worker Paul Murphy and Barry Devlin developed the Business Data Warehouse. However, as Inmon Bill uncovered the real concept of the system, he is called the 'father of data warehouse.' He had written about a variety of topics for building, usage and maintenance of the warehouse and the corporate information factory.

Types of data warehouse

Three main types of data warehouse make a cut in the business process today.

 Enterprise Data Warehouse (EDW): An Enterprise Data Warehouse (EDW) is a relational data warehouse containing a company's business data, including information about its customers. An EDW enables data analytics, which can inform actionable insights. Besides, it offers a unified approach for organizing and representing data.

Operational Data Store (ODS): An Operational Data Store (ODS) is a central database that provides a snapshot of the latest data from multiple transactional systems for operational reporting. It enables organizations to combine data in its original format from various sources into a single destination to make it available for business reporting.

Data Mart: A data mart is focused on a single functional area of an organization and contains a subset of data stored in a data warehouse. Data mart is a condensed version of data warehouse and is designed for use by a specific department, unit or set of users in an organization.

Moving data warehouse into cloud

We can confidently blame the Covid-19 pandemic for making data warehouse move into cloud platforms. On-premise data warehouses come with a lot of benefits like improved governance, security and speed. However, they are not elastic and require complex forecasting to determine how to scale the data warehouse for future needs. During the lockdown, the whole workforce was shifted to cloud and data warehouse is one among them. Even large enterprise data warehouses that many people thought would never leave the on-premise data centres are moving to the cloud to take advantage of today's cloud technologies. Some of the advantages of cloud data warehouse are,

• It leverages elasticity with separate compute and storage
• Cloud data warehouse scales-out abilities to handle compute or storage requirements
• They are easy to use, have versatile management and are cost-effective.

The future: Automation of data warehouse

Some of the issues that a data warehouse face are data integration, data views, data quality, optimization, competing methodologies, etc. Fortunately, data warehouse automation can flip this scenario completely on its head. In data warehouse automation, a data warehouse uses a next-generation technology for automation that relies on advanced design patterns and processes to automate the planning, modelling and integration steps of the entire lifecycle. It provides an efficient alternative to traditional data warehousing design by reducing time-intensive tasks such as generating and deploying ETL codes to a database server.