Data storage is a constant necessity for any business, especially as the data being accumulated by businesses has grown exponentially, especially given that data continues to evolve and businesses need to continuously use data to stay competitive.
But while there are numerous data storage solutions readily available, from cloud services to databases, many of these solutions are not built to handle billions of rows and take anywhere from 30 minutes to a few hours to return the results from a single query. The cost of running these queries is very high due to the lack of query optimization.
This is where data warehousing comes in.
But what exactly is a data warehouse?
Definition
A data warehouse is a data management system that stores large amounts of data from multiple sources within a company’s ecosystem, including relational databases or transactional systems and acts as a single source of truth for analytical reporting tasks, on-time decision-making, and other business intelligence tasks being done by organizations.
With a data warehouse, users can perform queries and look at historical data over time to improve decision-making. The main people in a company who will use data warehouses are data scientists and business analysts.
Fundamentals
A functioning and efficient data warehouse offers more than just data storage. In particular, three critical elements help make for a functioning data warehouse.
Integration – It acts as an endpoint for data from various sources such as APIs and databases. As such, it needs to be tightly integrated with all popular third-party data-generating tools. Most data warehouses either have native connectors with third-party tools or rely on ETL players to extract data from these sources and insert them into the data warehouse.
Cleaning – Data from various sources is cleaned prior to consolidation to ensure data reliability. Data engineers can run data quality checks using certain keywords; if there are any outliers they are replaced with the appropriate value, ultimately to prevent bad data from landing in the warehouse.
Consolidation – Clean data from various sources then get combined and distilled to extract valuable information. SQL queries are written for such tasks. Transformation tools like dbt are also very popular low-code, no-code alternatives to build data models and consolidate data so that it is ready to be consumed.
Functions
Given the presence of these aforementioned elements, the data warehouse should provide the following functions other than just providing storage:
Loading: Data is loaded using a loading wizard, cloud storage, programmatically via REST API, and third-party integrators. Data can be loaded in batches or can be streamed in near real-time, regardless of structure.
Transformation: Raw data ingested into a data warehouse may not be suitable for analysis. They need to be transformed. Data engineers use SQL, or tools like dbt, to transform data within the data warehouse.
Security: Access to a data warehouse does not guarantee access to all contents within. Every user is tied to a role and every role only has needs-based access. Sometimes access can go as granular as column-level masking.
Benefits
Data warehousing provides additional support for data as data warehouses are designed to monitor, manage, and analyze information to deliver a more actionable resource. They provide three key benefits for users:
Improved business intelligence
Data warehousing provides better access to information as businesses can make decisions based on data-driven information that is supported by additional information gathered by the company over a period of time. It becomes easy to retrieve the information based on the needs of the company and the improved business intelligence can be, thus, applied to core business processes including market segmentation, financial management, inventory management, etc.
Quick and timely access to data
Users can access data from multiple sources and quickly retrieve the required data. By employing analysis tools and data queries within the data warehouse, it allows companies to spend less time on the collection of data and devote more time towards conducting data analysis.
Ensures data quality and consistency
The conversion capabilities of the data warehouse ensure that data from different departments along with the processes are consistent and standardized. This allows departments to function efficiently by referring to a single source of data and getting results in accordance with the needs of the enterprise.
Data warehousing is critical for every organization in today’s competitive as it enables businesses to be able to deliver viable business solutions and effectively assess market trends to ensure their growth.