As organizations navigate the complexities of data protection, many are plagued with uncertainty about their current measures, leaving them exposed to potential risks. A startling 55% of organizations lack full confidence in meeting their backup and recovery service level objectives (SLOs), and an even higher 69% fear the possibility of a disruptive event within the next year.

In response to these challenges, building a data Warehouse emerges as a crucial solution. Data warehouses provide organizations with the necessary tools and structure to effectively collect, organize, and analyze data, bolstering their backup and recovery systems.

Throughout this article, we’ll delve into how to create a data warehouse and discuss important factors to consider during the process. No need for the post? Consider hiring data management system developers instead.

What Does an Enterprise Data Warehouse (EDW) Entail?

An Enterprise Data Warehouse (EDW) is a large-scale, centralized data storage solution that consolidates and manages data from various sources across an organization. It provides a unified and consistent view of an organization’s data, allowing for more effective decision-making, reporting, and data analysis.

But How Does an EDW Differ from a Traditional Data Warehouse?

While both EDWs and traditional data warehouses store data from multiple sources, an EDW is specifically designed to accommodate the extensive and complex data requirements of large organizations. An EDW is typically more scalable and robust, capable of handling vast volumes of structured and unstructured data while ensuring data quality, consistency, and security.

Data Warehouse Outcomes (Benefits Explained)

Businesses utilize data warehouses to analyze and report data, ultimately making well-informed decisions. The primary users of data warehouses within a company are data scientists and business analysts. Here’s how data management systems support organizations and the advantages they derive from them.

Gaining a Competitive Edge

By analyzing data from various sources in a data warehouse, businesses can discover valuable insights that help them outperform their competitors. Take a fast-food chain that uses data to optimize its menu and promotions based on customer preferences and buying patterns.

Streamlining Data Access and Analysis

In a survey conducted among data and analytics leaders in Europe and the United States, participants were asked to identify the main challenges they faced when utilizing data to generate business value. As of 2021, the most significant challenge, cited by 41 percent of respondents, was the insufficient analytical skills among employees. Additional obstacles included data democratization and the presence of organizational silos.

Since data warehouses consolidate and organize data, analysts can swiftly retrieve and examine the information they need, enhancing their productivity and effectiveness.

Accommodating Growth and Change

As enterprises evolve and expand, data warehouses can scale to meet increasing data demands, ensuring a smooth transition for organizations experiencing rapid growth.

Seamless Collaboration with BI Tools

The compatibility of data warehouses with a variety of business intelligence tools enables organizations to generate actionable insights through reports, dashboards, and visualizations that facilitate data-driven decision-making.

Uncovering Hidden Patterns

Storing historical data allows organizations to detect trends and patterns that may have been overlooked. For instance, an insurance company may find correlations between policyholder behaviors and claim frequency, helping them to adjust pricing and risk models.

Better Risk Management

Organizations can leverage data warehouses to identify potential risks and vulnerabilities by analyzing historical data, trends, and patterns, enabling proactive risk mitigation strategies and more effective decision-making.

Delineating Data Warehouse Terminology

It is imperative to distinguish data warehouses from analogous concepts such as “data lake,” “database,” or “data mart,” as these entities exhibit divergent scopes and structures.

Data Lake Versus Data Warehouse

Data lakes function as repositories for raw, unadulterated data originating from a multitude of sources, whereas data warehouses prioritize the analysis and processing of contextualized, collated data.

Data Mart Versus Data Warehouse

Data marts represent more narrowly focused subsets of a data warehouse, specifically designed to furnish particular users or applications with relevant data. Conversely, data warehouses encompass a broader range of subjects.

Database Versus Data Warehouse

Databases primarily serve to record and retrieve data, while data warehouses specialize in the analysis of extensive datasets. Although these constructs exhibit similarities, their underlying purposes diverge. Data warehouses often assimilate data from an array of databases.

Architecting a Data Warehouse

Data warehouse architecture is the blueprint that outlines the organization, presentation, and communication of data within the system. It typically consists of three main components:

Data Sources

These are the various systems and applications within an organization that generate and store data. Examples include CRM systems, ERP systems, and other operational databases.

Data Integration and Transformation

This component is responsible for extracting data from the sources, transforming it into a consistent format, and loading it into the data warehouse. This process is commonly known as ETL (Extract, Transform, Load).

Data Storage and Presentation

The final component is the data warehouse itself, which stores the transformed data in a structured format optimized for efficient querying and reporting. Data is usually organized into subject-specific areas called “dimensions” and “facts” to facilitate easy analysis.

Data warehouse architecture can be designed in various ways, such as with a simple structure:

You can also incorporate a staging area.

Or combine a staging area and data marts.

In the first approach, the data warehouse collects the data, and users can then perform reporting and analysis. Alternatively, the data can be organized into data marts before users carry out analysis and reporting.

Staging areas, often seen in visual representations, serve to cleanse and process data before integrating it into the warehouse. This streamlines the data preparation process. 

Inspiring Data Warehouse Software: The Top 7 Picks

In the contemporary marketplace, many data warehouse applications and tools can be abundant. 

However, when determining the most suitable tool for data warehousing requirements, it is crucial to conduct a meticulous evaluation of their capabilities and the value they bestow upon companies. Here are the top data warehouse softwares on the market.

Amazon Redshift

AWS Redshift, a user-friendly and cost-effective data warehouse platform, facilitates efficient data analysis for businesses of all sizes. As a global leader in cloud infrastructure and platform services, AWS has garnered the trust of a diverse clientele. 

With key features such as real-time and predictive analysis, flexible scaling, automatic backups, and federated query capabilities, Redshift is highly rated and recommended by users. 

Although it requires configuration, its seamless integration with AWS services makes migration easy. Pricing for AWS Redshift begins at $0.25 per hour.

Azure Synapse Analytics

Microsoft’s Azure, launched in 2010, now offers over 200 products and services, including data storage, big data platforms, and data analysis tools. 

Azure Synapse Analytics, which merges corporate warehousing and big data analysis, is used by both small and large businesses across various industries. 

It is highly rated by users for its key features, such as smart workload management and high-performing engines, as well as its diverse transformation tools and effective AI integration. 

However, some drawbacks include the lack of support for serverless architecture and a complex pricing structure. The pricing starts at $5 USD per terabyte of data processed, with Tier-1 pricing at $4,700 for 5000 Synapse Commit Units.

Oracle Autonomous Data Warehouse

Oracle Autonomous Data Warehouse is a cloud-based service offering advanced data management on Oracle’s cloud infrastructure. 

Designed for analytical tasks, it improves organizational productivity and reduces operating expenses. 

Geographically dispersed architectures cater to large companies with multiple workloads and significant budgets. 

The service has received high ratings and numerous accolades, including being named CRN’s 2020 Product of the Year Overall Winner.

Key features include self-managing abilities, secure data encryption, compatibility with third-party products, and in-built ETL and performance metrics. The service offers advanced functionality, easy integration, a fully managed relational database, and adaptable scaling. However, the pricing is costly, starting at $1.3441 per hour.

Teradata Vantage

Teradata presents a refined product lineup of cloud-based solutions, ideal for various infrastructures. Vantage, their flagship product, excels in enterprise analytics, offering boundless intelligence and scalability. It serves clients from multiple industries and has been acknowledged as a leader in the Gartner Magic Quadrant for Cloud DBMS.

Key features include an integrated multicloud platform, AI and machine learning-driven models, and improved Clearscape Analytics capabilities. 

Its benefits are extensive analytics, pay-as-you-go pricing, and swift data access. However, it is best suited for large-scale businesses. Pricing starts at $9,000 per month, based on consumption.

SAP BW/4HANA

SAP BW/4HANA, a renowned data storage solution, offers cloud and on-site deployment options, supporting various management aspects. With a 4-star average rating, it is trusted by industry professionals. 

The capabilities include easy integration with SAP and non-SAP apps, built-in predictions, and advanced analysis. 

The advantages can be AI-enhanced performance, reduced shadow analysis, and user-friendly experience. However, the high license cost is a downside. Pricing plans can be requested directly from SAP.

How to Create Data Warehouse for Customized Enterprise Use

Building a data warehouse involves several steps, from defining your objectives and choosing the right technology to design your data model and implementing ETL processes. Here is a detailed explanation to guide you through the process:

Define Your DMS Objectives

Before starting, it’s crucial to understand your organization’s goals and requirements for the data management system. This will help you determine the scope and scale of the project. Some key questions to ask are:

• What are the main business processes that the data warehouse will support?
• What types of analysis and reporting do stakeholders need?
• What are the expected benefits and return on investment (ROI)?

Assemble a Project Team

Assemble a team of professionals with the necessary skills for the project. This may include data architects, data modelers, ETL developers, business analysts, and project managers. Each member will bring their expertise to the different stages of building the data warehouse.

Choose the Right Technology Stack

Selecting the appropriate technology stack for your data warehouse is critical for its success. When choosing a data warehouse platform, some factors to consider include:

• Scalability: Can the platform handle your organization’s current and future data storage and processing needs?
• Performance: Does the platform provide adequate query performance and response times?
• Compatibility: Is the platform compatible with your organization’s existing systems, tools, and infrastructure?
• Cost: Does the platform fit within your organization’s budget?

Popular data warehouse platforms include Amazon Redshift, Google BigQuery, Snowflake, and Microsoft Azure SQL Data Warehouse.

Design the Data Model

Designing an effective data model for your data warehouse is crucial to ensure that the data is organized, structured, and easy to analyze. There are two main approaches to data modeling in a data warehouse:

• Star schema: A star schema consists of a central fact table surrounded by multiple dimension tables. The fact table contains quantitative data, while dimension tables contain descriptive attributes related to the facts.
• Snowflake schema: A snowflake schema is a more normalized version of the star schema, where dimension tables are split into multiple related tables. This design can reduce redundancy, but may require more complex queries.

Identify and Collect Data Sources

Determine which data sources your organization will use to populate the data warehouse. Data sources can include relational databases, flat files, APIs, or third-party data providers. Ensure that the data sources are accurate, up-to-date, and relevant to your objectives.

Develop ETL Processes

Extract, Transform, and Load (ETL) processes are responsible for extracting data from source systems, transforming the data to fit the data warehouse schema, and loading the transformed data into the data warehouse. ETL processes involve:

• Data extraction: Retrieve data from source systems and store it in a staging area.
• Data transformation: Clean, normalize, aggregate, and enrich the data to fit the data model.
• Data loading: Insert the transformed data into the data warehouse tables.

Implement Data Quality and Governance

Establish data quality and governance policies to ensure that the data in the warehouse is accurate, consistent, and secure. Some key aspects of data governance include:

• Data quality management: Implement processes to detect and correct data errors and inconsistencies.
• Data lineage: Track the movement and transformation of data from source systems to the data warehouse.
• Data security: Protect the data warehouse from unauthorized access, data breaches, and other security threats.

Test and validate the data warehouse

Before deploying the data warehouse, conduct thorough testing to ensure that it meets your organization’s requirements and objectives. Test the data model, ETL processes, and data quality to ensure the data warehouse is functioning as intended.

Enterprise Data Warehouse Architecture

The central component of a business intelligence solution is an enterprise data warehouse designed to address specific data management and analysis requirements. Enterprise data warehouses usually feature the following architectural elements:

• Collection of data through a data source layer: Application databases, corporate systems (e.g., CRM, ERP, document management software, HRM), and external sources (e.g., social media, government reports, selected stock market trackers) all contribute data to the organization-wide data warehouse.
• Utilization of a staging area: As a temporary storage space, the staging area is used for data processing during the extract, transform, and load (ETL) process. ETL combines and structures data from different sources, making it suitable for storage in the main data warehouse. Cloud-based data warehouses use ELT (extract, load, transform) due to their scalability, allowing the transformation step to occur after data loading.
• Centralized data storage in the data storage layer: This layer enables data accessibility for analysis (querying, reporting) and sharing.
• Application of analytics and BI tools: To access the data within the organization-wide data warehouse, various tools such as OLAP, data mining, reporting, and visualization tools can be employed.

Types Of Data Warehouse Architecture

When examining the features of an Enterprise Data Warehouse (EDW), it’s important to discuss the most suitable technical approach. Different types of businesses may require distinct data storage and processing methods. Factors like data volume, analytical complexity, security concerns, and budget can influence the choice of system setup. As such different types of EDW software achitecture exist, with the three major types being;

• On-premises data warehouse architecture
• Cloud-hosted data warehouse architecture
• Virtual data warehouse architecture

On-Premises Data Warehouse

An on-premises data warehouse employs local, dedicated hardware and software for centralized data storage. 

Storing data on physical servers eliminates the need for data integration tools between multiple databases. Instead, the EDW connects with data sources through APIs, allowing for continuous gathering and processing of information. 

This work takes place either in the staging area (where data is transformed before being loaded into the data warehouse) or within the warehouse itself.

A traditional warehouse is often viewed as superior to a virtual one (discussed below) because it lacks an additional abstraction layer. This makes the job of data engineers easier and streamlines the management of data flow during preprocessing and reporting.

However, the downsides of a traditional warehouse, which depend on the specific implementation, often include:

1. A pricey technology infrastructure (involving both hardware and software), and
2. The requirement to hire a team of data engineers and DevOps specialists for the setup and maintenance of the entire data platform.

When to Use

An on-premises (Classic) EDW is appropriate for organizations of any size that seek secure data processing and wish to maximize the benefits derived from their data. These warehouses offer adaptability in data platform architecture styles and facilitate intentional scaling while addressing data privacy issues.

Cloud-Hosted Data Warehouse

Cloud-based data storage centers offer a convenient, scalable solution for organizations of all sizes, with computing, storage, and service components. The infrastructure is typically maintained for you, eliminating the need for creating and managing servers, databases, or tools. 

When to Use

Data security should be considered when selecting a provider to ensure your sensitive business data is protected. If you want a fully managed data integration, storage center maintenance, and business intelligence support, cloud-based platforms are an ideal choice.

Virtual Data Warehouse

A virtual data warehouse is a kind of EDW that serves as an option to an on-premises datawarehouae. In essence, these are several databases linked virtually, allowing them to be searched as one system. 

The data remains in its original locations; it doesn’t need to be physically moved but can still be accessed using analytical tools. Virtual storages are useful if you don’t want to deal with the underlying infrastructure or if your data is already easy to manage. 

However, this method has many disadvantages:

• Numerous databases will need ongoing software and hardware upkeep and expenses.
• Data in a virtual DW still necessitates transformation software to make it accessible for end users and reporting tools.
• Complicated data queries might take too long because the necessary data may be located in two different databases.

When to Use

Virtual EDWs are appropriate for companies with unprocessed data in a standardized format that doesn’t need intricate analysis. It’s also suitable for organizations that don’t consistently use BI or are just beginning to explore it.

Data Warehouse Requirements

Even though every data warehouse has minor variations, nearly all of them share four essential needs: capacity, tools for handling data, organization, and software foundation. 

In establishing and sustaining a successful data warehouse, four key elements must be considered.

First, determining the optimal facility and hardware size is a complex task, as it involves a combination of known and unknown factors. It is crucial to account for both present equipment and future expansion needs, while also acknowledging that advancements in server production could reduce required space. 

To avoid excessive unused storage capacity, businesses should estimate their hardware needs for the next 12 to 18 months.

Second, data management tools facilitate data transfer to and from the warehouse. The choice of software is a vital requirement, as it depends on desired functionality, user profiles, and the complexity of the data requested. Third, selecting the appropriate data warehouse structure is essential, with factors such as data type, query complexity, and user expertise influencing the decision. Finally, the software platform significantly impacts various support and maintenance-related decisions.

Building A Data Warehouse: Costs Revealed

The cost of developing a data warehouse project is influenced by several factors: 

1. the number of involved data sources (e.g., ERP, CRM, SCM); 
2. the variations in data structure, format, and value usage across these sources; 
3. the complexity and volume of the data; the necessity for data security and privacy; the inclusion of multiple data paths and elements (e.g., customers, wages, transactions); and 
4. the desired performance characteristics, such as speed and scalability.

Overall, the cost of creating a data warehouse can range anywhere between $50,000 to $1,000,000 with the number of users also being a factor.

Conclusion

Enterprise Data Warehousing (EDW) offers numerous advantages for organizations, including improved decision-making, enhanced project efficiency, and centralized data storage. A variety of architectural models cater to different needs, while an array of top software solutions facilitates seamless implementation. 

However, it is crucial for businesses to carefully consider the development costs involved, taking into account factors such as data complexity, security, and performance requirements. By thoughtfully selecting the right architecture and software, organizations can unlock the full potential of a well-designed data warehouse. 

To get a better understanding of pricing and estimates, contact us.

The post Building A Data Warehouse: Guide for Data Scientists & Business Analysts appeared first on Code&Care.