RST Software
Editorial Team
Magdalena Jackiewicz
Reviewed by a tech expert

Introduction to data warehouses: use cases, design and more

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Have you ever felt like you're drowning in data but starving for insights? As organizations collect more and more data from various systems and sources, making sense of it all to drive better business decisions is a key challenge. All those numbers, metrics, and transactions flying around your business hold keys that can unlock big-time insights, if only you could collect it all in one place and organize it.

That's why smart companies build data warehouses – central repositories updated on a regular basis, collecting data scattered across departments and databases. With the right data foundation powering detailed analytics, powerful data science and business intelligence efforts, companies can shift from reactive responses to proactive, insight-led strategies that create competitive advantages and shareholder value.

In this article, we give you insights into building a data warehouse that transforms a digital deluge into strategic insights that propel business growth. The era of data warehousing isn't on the horizon; it's already here. And for businesses aiming to thrive in this new landscape, the message is clear: the time to embrace data warehousing is now.

What is a data warehouse?

What is data warehousing, and why is it critical in today’s businesses? Simply put, a data warehouse is a specialized database optimized for analytical queries rather than transaction processing. It serves as a central store for historical and current data from multiple sources, structured for querying and analysis. It is a treasure chest for business analysts, data engineers, data scientists, and decision-makers who rely on business intelligence tools, SQL clients, and analytics applications to extract meaningful insights.

Some key characteristics that distinguish a data warehouse:

  • Integrated data from multiple systems and sources – e.g. combining sales data from CRM systems, web analytics data, and inventory data from ERP systems,
  • Current and historical data retains time-variant data over extended periods to enable trend analysis, for example, maintaining 5+ years of historical data even when source systems purge older data,
  • Read-only – data is loaded and stored but not updated or deleted within the warehouse ensuring reliability as a consistent analytic data source,
  • Organized by relevant subjects – and focused on business analysis rather than operations, such as sales, inventory or production analytics, or similar.

In short, a data warehouse powers business intelligence by enabling users to easily query large volumes of high quality, integrated data for reporting and advanced analytics.

What is a cloud data warehouse?

Exploring what a data warehouse is reveals its role as a fully managed service offered in the cloud, known for their scalability and flexibility. One of the leading options is AWS Amazon Redshift, which offers extensive data warehousing and analytical capabilities. 

Source: Amazon Redshift

Advantages of a cloud data warehouse vs an on-premise data warehouse

What does data warehousing allow organization to achieve? Migrating your on-premise data warehouse to the cloud offers several compelling benefits including:

  • Elastic scalability – scale storage and compute up and down as needed, from a few terabytes to petabytes and back again, without any hardware capacity planning,
  • No infrastructure to setup and manage – fully managed service without need for database admin and ops teams to install, maintain and tune infrastructure,
  • Usage-based pricing – pay only for storage, compute and services used per hour or month rather than upfront hardware costs,
  • Faster time to value – get up and running quickly without lengthy on-prem hardware procurement cycles.

For example, with a cloud data warehouse like Amazon Redshift, ad-hoc analysis of billions of rows of data can be supported instantly with no upfront hardware investments required.

While on-premise data warehouses provide more control and security, they often struggle with complexity and scalability. For businesses requiring rapid scaling, cloud data warehouses can be more responsive to these needs.

How does a data warehouse work?

Behind the scenes, a data warehouse is composed of an automated data integration architecture for loading and organizing data, and a structured database optimized for analytical performance. Data within a warehouse is methodically organized into databases, tables, and columns, facilitating efficient data management. 

Data warehouse architecture

Cloud-native data warehouse platforms that handle the infrastructure, management, and scaling of analytical data workloads in the cloud typically provide data warehouse architecture that consists of:

Staging layer

The staging layer acts as a temporary landing zone where raw data gets extracted from source systems before loading it into the data warehouse. It handles essential data integration tasks like validation, quality checks, transformation, and data conversion to prepare the source data for analytic use.

For example, log files or database backups may get staged here for processing.

Core data warehouse

This analytical database is where clean, integrated enterprise data is stored in an analytics-optimized structure designed for flexibility, scalability and fast query performance. Various storage schema designs of data warehouse systems can be utilized to structure the data, like star or snowflake schemas separating business facts from dimensional attributes. Columnar storage, compression, partitioning, caching and query optimization help provide fast analysis over huge data volumes, even with petabytes of historic data.

Access layer

The presentation layer consisting of BI tools, SQL clients, analytics dashboards and other applications that analysts, data scientists and business users leverage to access and analyze the data. This abstraction layer hides complex underlying data structures while exposing intuitive business entities. Security integration and access controls also happen here.

Together these cloud data warehouse layers allow scalable and governed pipelines to move raw data from on-prem systems into a cloud analytics-ready state where it fuels advanced analysis and business insights.

This architecture provides flexible and secure data warehouse management, and can be customized based on organizational requirements. From simple structures to complex models with special staging areas, these architectures allow businesses to adapt their data warehouses to specific operational needs.

How do databases, data warehouses, and data lakes work together?

When further customization is required, modern analytics architectures offer a powerful combination of transactional source systems, data lakes, and warehouses. Together, they form a comprehensive ecosystem for managing diverse data requirements:

  • Transactional databases. Databases usually handle real-time operational data. They are systems of record capturing business events and transactions (e.g. a retail bank's core banking system tracking deposits, payments, and account updates),
  • Data warehouses. In the context of big data, what is a data warehouse becomes crucial as it serves as a central integrated data repository specialized for analytics (e.g. an aggregate view of retail banking transactions, balances, and customer details),
  • Data lakes. Scalable “landing pads” for vast amounts of raw data both structured and unstructured (e.g. clickstream events from a bank's website funneling into a Hadoop data lake), acting as an expansive repository storing data in its original format.

When considering upgrades to our data systems, it's essential to revisit what is a data warehouse, database and lake, and how its latest advancements can benefit our analytics. Together these systems enable real-time operations to feed raw data efficiently into long-term storage, where ETL processes (Extract, Transform, and Load) structure and prepare data for analysis within the optimized data warehouse where it unlocks value.

For example, a business may use a database for daily operations, a data lake for storing raw data from transactional systems, and a data warehouse for in-depth analysis of data reshaped, cleaned, and integrated in batch ETL processes.

What are the benefits of a data warehouse?

Now that we understand what data warehousing entails and how it works, let’s highlight some of the tangible benefits:

  • Deeper business insights – by structuring integrated enterprise data focused specifically around business analysis, hidden insights can be uncovered to inform strategic decisions,

For example, analyzing multidimensional sales trends by region, product, sales rep can result in more granular understanding of a particular market.

  • Improved analytics performance – orders of magnitude faster query speeds compared to transaction databases, e.g. sub-second response for ad-hoc queries involving billions of rows, to enable interactive analysis without high latency impacting production systems.
  • Data governance and QA – data warehousing ETL processes improve data quality, consistency, accuracy, and governance enforced on source data before final storage,

Validating records, transforming disparate coding, flagging outliers or errors are standard procedures that can be performed while using data warehouse.

  • Historical trend analysis – retains extensive time series data beyond what even source systems store for longitudinal and historical analysis,

For instance, analyzing account growth and retention cohorts over a 5+ year period can translate to more informed customer engagement strategies, providing insights into customer behavior and helping to tailor marketing efforts for improved loyalty and retention.

  • Self-service BI and agility – broad user access and flexible tools empower various teams to gain insights from data independently without being gated by IT or engineering resources.

The ultimate end goal of data warehouse investment is empowering more data-driven decision making at an organizational level based on accurate analytics leveraging your full domain of enterprise data.

Data warehouse use cases

Nearly any business looking to unlock value through deeper analysis of its data is a candidate for data warehousing. Some examples where data warehousing delivers immense value include:

  • Sales analytics – companies can leverage data warehousing and business intelligence capabilities for deeper sales analytics to uncover trends, optimize operations, and improve forecasting. By integrating data from CRM systems, financial records, and other sources, sales leaders can track KPIs like pipeline trends, win/loss metrics, deal velocity, and forecast accuracy down to granular segment and account levels. This enables data-driven decisions around resource allocation, quota setting, and sales process optimization,
  • Customer analytics – data warehousing provides a 360-degree customer intelligence capability by consolidating data from across disparate channels and systems. Analytics use cases span behavioral segmentation for precision targeting, analyzing customer lifetime value trends, building predictive models to estimate renewal and churn risks, tracking multi-touch attribution, and optimizing customer marketing through campaign measurement and ROI analysis. These data-driven insights support strategic decision making around resource allocation, pricing optimization, product enhancement priorities and customer experience management,
  • Human resources – HR teams can leverage analytics use cases such as advanced workforce planning leveraging historical hiring and attrition models, analyzing training program efficacy, ensuring pay equity across segments, and building predictive models for talent retention risks. By understanding key workforce metrics and trends better, HR leaders are empowered to optimize hiring and talent management programs, minimize regrettable turnover in key roles, and align learning & development investments to drive productivity,
  • Financial planning – data warehousing enables finance teams to consolidate data from operations, sales, HR and other functions for deeper analysis in support of long term planning. Use cases include driver-based budget forecasting by business unit, dynamic what-if modeling and scenario analysis, analyzing true profitability by customer segment, product line or region, and optimizing cost structure through granular cost & profitability reporting. The insights help guide operational planning, investment decisions and growth strategy.

Having explored common business analytics use cases powered by data warehousing, let's shift to some leading practices for architecting your own data warehouse solution tailored to your organization's needs. While specific components depend heavily on your industry, data systems landscape and expected query patterns, these general guidelines help structure tactical design decisions.

How to design a data warehouse for your specific use case, step-by-step

Key considerations when architecting a data warehouse for a specific use case include:

Step 1: Integrate key data sources

A fundamental step is identifying critical systems of record across your organization to integrate into the data warehouse, such as your CRM and ERP databases, web or mobile analytics tools, IoT data streams, and other transactional and operational systems.

For instance, AWS offers native connectivity from Redshift to common data sources like S3, DynamoDB, RDS, Salesforce etc. Ingesting this raw data becomes the foundation for transformed datasets serving various analytics use cases.

Step 2: Enable self-service access

Structure curated data sets tailored for business teams containing clean, business-friendly views they can self-serve for analysis without deep technical skills.

For example, Redshift Spectrum allows querying exabytes of unstructured S3 data, while Redshift fuctions help present calculated metrics. Combine disparate data into reusable semantic models using AWS services like Quicksight, Sagemaker or third-party BI tools.

Step 3: Ensure scalability

Data warehouses deliver petabyte-scale storage capacity, easily scaling on demand. The distributed query processing architecture optimizes performance across compute clusters spanning dozens of nodes. Moreover, you pay only for the managed infrastructure used per hour with no upfront costs.

Step 4: Combine batch & streaming

Modern data warehouses can integrate both batch and real-time streaming data sources. Batch data pulled from systems of record on periodic extracts provides in-depth historic context. Integrating streaming data from message buses, IOT devices or transactional logs adds a real-time dimension enabling up-to-the minute operational insights. Your warehouse architecture should support ingesting high volume event streams as well as retaining large historic datasets, under one analytics foundation.

For example, Redshift integrates batch historic data from S3 and databases with streaming sources like Kafka and Kinesis using services like Firehose, enabling real-time dashboards over fresh data while retaining large historical datasets for flexible ad-hoc analysis.

Step 5: Facilitate data science

Plan to enable more advanced data manipulation, statistical analysis and machine learning model development against warehouse data. Provide tools and access for data scientists to transform raw data into features, build training datasets, even build and execute machine learning models with native scoring integration. Optimized data types for complex analytics functions processing large data volumes with high cardinality can enable sophisticated enterprise AI/ML without requiring data to move elsewhere.

If you decide to use Redshift, it is good to know that it integrates smoothly with Sagemaker allowing data scientists to manipulate datasets for advanced analytics at scale. ML models trained in Sagemaker can also be operationalized within Redshift SQL queries. Optimized types like hyperloglog algorithms enable complex analytics functions over extremely large data.

The key is flexible data warehousing architectures to facilitate varied analytics use cases – from parameterized dashboards to canned reports and ad-hoc analysis to advanced modeling. Different query patterns should be supported for business analysts, data engineers, data scientists and application developers against a consolidated 360-degree data view.

How can RST Software support your data warehouse efforts

Businesses often ask what a data warehouse is to understand how it can enhance their data analysis and reporting capabilities. As data management experts supporting clients across industries in leveraging AWS, RST Software is uniquely equipped to advise, architect, and implement your cloud data warehousing initiatives. Our team can help at any point along your data journey:

  • Advisory on leading data warehousing approaches and best practices,
  • Technical architecture and design services,
  • Build and deployment services on AWS cloud platforms.

Whether you are looking to set up your first cloud data warehouse or migrate from legacy data warehouses, reach out to us and let's discuss how RST can help to transform your enterprise data into business value.

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