What Is a Data Warehouse?

In the present era of digital transformation, businesses encounter obstacles to analyze the huge amount of customer data that they accumulate. Traditional systems struggle with the weight of data deluge and look for advance solutions like Data warehouse for enhancing data management.

In fact, according to research, 76% of companies are now investing more in analytics infrastructure like data warehouses. The global data warehouse market is projected to reach $7.69 billion  by 2028, expanding at a blistering 24.5% annual clip.

But what exactly are these vital systems, and how do they work their magic behind the scenes? Read on to know more.

What is a data warehouse and why is it beneficial?

According to Databricks, a data warehouse is a data management system that stores current and historical data from multiple sources in a business-friendly manner for easier insights and reporting.

Data warehouses, crucial for data governance, establish a structured environment to manage data flow effectively. Through operational applications like ERP and CRM systems, separate databases, and external sources such as partner networks, IoT sensors, weather APIs, or social media, data continuously streams into the warehouse. This steady influx adheres to a regular schedule, ensuring the warehouse remains updated with the latest information.

As data storage evolves, from its origins in local servers to its current sprawl across on-premises infrastructure, private cloud environments, and public clouds, significant changes in storage methods emerge.

Source: jazz.net

Without a data warehouse, teams would face challenges integrating data from diverse sources, formatting it for analysis, or maintaining comprehensive views of current and historical data. The warehouse serves as the single source of truth, streamlining data management and analysis processes efficiently.

Components and structure of data warehouse

Before understanding the architecture, it is essential to look at the components of a data warehouse. 

• ETL: The Extract, Transform, Load (ETL) process takes data from various source systems and prepares it for storage in the data warehouse. ETL converts the data into a consistent, readable format so it can be easily analyzed.
• Metadata: Metadata provides essential context about the data. It describes the schema, or structure, of the data warehouse including attributes like data elements, relationships, access rules, and data quality.
• SQL: It is the standard language for querying deriving analytical insights. The data warehouse has optimized SQL server processing that allows for fast, complex analyses of large datasets. Powerful computing resources access the stored data quickly to generate reports and dashboards.
• Data layer: Also known as data mart, this partitions the data warehouse into logical subsets accessible by different user groups. Data mart enhances data security by restricting access and protecting sensitive information on a need-to-know basis.
• Governance: Strong governance and security policies are paramount as data from across the organization is centralized. Features like authentication, authorization, and logging aim to preserve privacy and regulatory compliance. Governance ensures consistent data quality as volumes increase over time.
• Access tools: Data warehouses require user-friendly tools for accessing and analyzing information. These tools include querying applications, reporting tools, development environments, data mining software, and OLAP systems. 

Let us now understand the architecture of data warehouse.

The architecture typically falls into three main categories namely, single-tier, two-tier, and three-tier. Among these, the three-tier architecture, featuring separate databases or servers for each layer, stands out as the most prevalent due to its superior scalability and performance.

The overarching aim of a well-designed data warehouse architecture is to seamlessly integrate data from diverse sources, ensuring effective cleansing and structuring within the storage layer. Ultimately, this architecture should facilitate intuitive access for users, enabling thorough analysis and actionable insights.

In dissecting data warehouse architecture, there are three primary layers:

• Data source layer: This bottom layer contains the raw data sources like operational databases, files, streaming data, etc. Data is extracted from these sources and transformed.
• Storage layer: The storage layer contains the data warehouse where the cleaned and transformed data from various sources is stored. It is structured for querying and analysis rather than transactions. Common structures include a star schema or snowflake schema.
• Access layer: The access layer contains the tools that business analysts and others use to access, analyze, and report on the data in the storage layer. Common tools include OLAP tools, BI tools, data mining tools, and APIs.

In addition, some architectures also include a staging area or data staging layer between the source and storage layers. This is where raw source data is cleaned and transformed before loading it into the data warehouse.

Data lake vs. data warehouse

The confusion between data lakes and data warehouses often arises due to their shared objective of storing and managing data for analysis and decision-making purposes. However, they differ significantly in their architecture, functionality, and use cases.

A data lake takes a very open-ended approach by allowing you to store huge volumes of raw data in its original format, without imposing much structure upfront. This makes it simple to collect and access vast amounts of various data types, whether structured, semi-structured, or unstructured.

A data warehouse, on the other hand, is intended specifically for business intelligence and analytics use cases. Multiple data sources are cleaned, normalized, and loaded into a structured repository with a predefined schema. This optimizes the data for queries, reporting, and other analysis activities.

Here are the main points of comparison:

What are the benefits of data warehousing?

Data warehouses provide many benefits to organizations by helping them consolidate, cleanse, and analyze data from multiple sources. Here are the top advantages:

• Centralized place for all data: Having a data warehouse means all your business data is stored in one centralized place. This is convenient because employees, analysts, and other users only need to check one system instead of multiple databases or data sources.
• Historical insights: A data warehouse collects data over time so you can analyze trends and patterns of customers. You'll have a historical record to look back on to see how metrics changed month to month or year to year.
• Dedicated resources for analytics: Separating analytics and reporting from transactional systems enhances the performance of both components. Data warehouse handles all the data pulls, integrations, and analytics processing so operational database isn't bogged down. Frontline systems stay quick while analytics run smoothly.
• Consistent, trustworthy data: Information in a data warehouse goes through data quality checks like normalization, deduplication, and validation. This refining process maintains uniform naming, formatting, and coding across all data.
• Maintain data reliability: Issues like duplicate records, incompatible formats, and outdated values decrease data reliability. A warehouse's ETL process cleans and standardizes incoming data to keep the retrieved information timely, accurate, and unambiguous. It is critical for effective decision-making.

What’s Next?

Altudo recognizes the critical role of having a data warehouse to curate personalized CX. We’re proud to be a Databricks partner one of the leaders in data lakehouse analytics. Databricks' unified data platform allows enterprises to unify data engineering and business analytics across data lakes and warehouses. Altudo provides Databricks consultation, implementation, and support services for every business.

If you are ready to unlock the potential of your data and drive business success through informed, data-driven decisions by leveraging a robust data warehouse, get in touch with us.