When making important decisions in your organization, ensuring the integrity, accuracy, and completeness of the data used to inform it is key. This is where data warehousing comes in.
Without it, you are forced to rely on the raw data stored within each application. Not only is this process slow, but the accuracy of the data can be compromised when depending on human processes to retrieve it from various applications.
Data warehouses enable businesses to run these powerful analytics by pulling, storing, and processing data to make it ready for decision-makers to access.
Do you want to know more about how a data warehouse can solve this issue, and how to implement data warehousing in your organization? Keep reading to find out.
What Is a Modern Data Warehouse?
In essence, a Modern Data Warehouse (MDW) is a data management system designed to support business intelligence (BI) activities, especially in terms of analytics. Unlike traditional data warehouses, which could be pretty rigid and slow to adapt, an MDW is built to be flexible, scalable, and super efficient at handling massive volumes of data from various sources — we’re talking both structured data (like numbers and dates) and unstructured data (like text and images).
The core idea behind an MDW is to have a single repository where data from different places (your CRMs, ERPs, social media, IoT devices, and more) can be stored, cleaned, and transformed. Once it’s in there, this data becomes ready for analysts and business users to slice and dice, helping them make informed decisions based on real insights.
Key components of an MDW include:
- Data Integration Tools: These are used to bring data from diverse sources into the warehouse, often involving processes like ETL (Extract, Transform, Load).
- Storage: This isn’t just about having a place to keep your data; it’s about having scalable, secure storage that can handle variety and volume without hitch.
- Data Processing and Management: Once the data is in, you need powerful processing capabilities to manage and query the data efficiently.
- Analytics and BI Tools: The whole point of storing and processing this data is to analyze it. Modern Data Warehouses are closely integrated with analytical tools to help users gain insights and make data-driven decisions.
What really sets MDWs apart is how they embrace cloud technology. By leveraging cloud services, MDWs offer incredible scalability, meaning they can grow as your data needs grow, without requiring a massive upfront investment in hardware. Plus, they can integrate nicely with AI and machine learning models, making it easier to predict future trends and patterns.
Modern Data Warehouses are powerful, flexible systems designed to make business intelligence easier and more comprehensive by harnessing and analyzing data from everywhere. Whether you’re a small business or a large enterprise, leveraging an MDW can dramatically improve how you interpret and act on data.
Comparing Traditional and Modern Data Warehousing
| Feature | Traditional Data Warehousing (TDW) | Modern Data Warehousing (MDW) |
|---|---|---|
| Architecture | Monolithic and often on-premise, centered around a single, central database. | Distributed, flexible, often cloud-based with options for hybrid models. Utilizes data lakes and warehouses. |
| Data Types Supported | Primarily structured data from internal sources. | Both structured and unstructured data from a wide range of sources, including IoT, social media, logs, etc. |
| Scalability | Limited by hardware and infrastructure. Scaling up requires significant investment and time. | Highly scalable on-demand with cloud resources. Cost-effective scaling options. |
| Cost | High upfront cost for infrastructure and maintenance. | Pay-as-you-go pricing models with lower upfront costs thanks to cloud services. |
| Performance & Speed | Can struggle with very large datasets or complex queries. | Optimized for high performance and speed, even with very large datasets or real-time processing needs. |
| Flexibility & Agility | Changes to the data model or system architecture can be challenging and time-consuming. | Highly agile and adaptable to changes in data sources, volume, formats, and analytics needs. |
| Data Processing | Batch processing is common, with limited capabilities for real-time processing. | Supports both batch and real-time data processing and analytics. |
| Integration | Integrating new data sources can be difficult and requires significant effort. | Designed for easy integration of diverse data sources, including cloud services and SaaS platforms. |
| Analytics & BI | Often requires moving or exporting data to specialized tools for advanced analytics. | Deep integration with advanced analytics, AI, and machine learning capabilities. |
| Data Management | Typically relies on ETL (Extract, Transform, Load) processes, which can be cumbersome. | Utilizes more flexible ETL, ELT (Extract, Load, Transform), or data virtualization techniques. |
| Security & Compliance | Security centered around on-premise controls and access management. | Advanced security features including data encryption, identity management, and compliance features in the cloud. |
| Storage | Relies on physical storage which can be a bottleneck. | Utilizes cloud storage, offering virtually unlimited storage capacity. |
| Data Recovery and Backup | Manual backups; recovery can be slower and more complex. | Automated backups and disaster recovery solutions as part of cloud services. |
Data warehouse vs. database
It’s easy to confuse both terms as a data warehouse and a data base share some similarities.
A database is a key component of a data warehouse and can be defined as a storage system where data can be quickly recorded and retrieved. A database collects data for transactional purposes, application support but also to enable reporting.
Common databases that are commonly used in the enterprise include ERP, SQL databases, Customer Relationship Management (CRM) systems, business process management systems but also Excel spreadsheets.
In comparison, a modern data warehouse is designed to centralize and store large amounts of data from multiple databases and make them easier to analyze.
A data warehouse uses an automated process called ETL and which stands for extracting, transforming, and loading data into a data warehouse and brings a substantial advantage when it comes analyzing data without the technical expertise.
Data warehouse vs data lake
Data lakes and data warehouses are both used to store, manage, and analyze data. They complement each other and support different use cases even though they have some overlaps.
A data warehouse is a repository that stores structured, cleaned and organized data in order to serve a specific business purpose. In comparison, a data lake stores large volumes of structured, semi-structured, and unstructured data in its native format, and processes it later on-demand.
The need for a data warehouse becomes crucial when an organization has a high level of data diversity and analytical requirements and want to make better decisions in less time. In this scenario, the data warehouse will do the analytic work based on the best data available to ensure decisions can be made faster.
Key Characteristics of a Modern Data Warehouse?
With the rise of cloud technology, data warehousing has undergone many changes over the past ten years to provide inbuilt scalability, high availability, performance, and flexibility.
While traditional on-premises data warehouses can still meet an organization’s objectives, they struggle with modern data architecture and are not scalable and cost-efficient enough to deal with all the data that an organization is generating and which keeps on growing.
A modern data warehouse allows to combine all kinds of data, at any scale, and easily to get business intelligence insights through dashboards, visualization tools as well as advanced analytics for all your users.
Additionally, a modern data warehouse focuses on value instead of transaction processes and is primarily built for analytical purposes.
Microsoft has introduced various cloud-based services through Azure to support the modern data warehouse goals and enable a flexible deployment:
Source: https://docs.microsoft.com/en-us/azure/architecture/solution-ideas/articles/modern-data-warehouse
1. Ingest
Azure Data Factory is a cloud-based ETL and data integration service that allows you to create data-driven workflows for orchestrating data movement and transforming data at scale.
SQL Server Integration Services (SSIS) is a platform that performs high-performance data integration tasks such as extraction, transformation and ETL for data warehousing.
2. Store
Azure Data Lake is a hyper-scale repository that allows you to store data of any size and kind.
Azure Blob Storage allows you to store and access massive amounts of unstructured data.
3. Prep & Train
Azure Data Bricks: Your data in Azure Blob Storage/Azure Data Lake can then be leveraged to perform scalable analytics with Azure Databricks and obtain cleaned and transformed data.
4. Model & Serve
Move your clean and transformed data to Azure Synapse Analytics and combine it with your current structured data to create one single data hub. You can use built-in connectors between Azure Databricks and Azure Synapse Analytics to move data at scale,
Azure Analysis Services is a cloud data analytics platform that enable large amounts of data to be queried for ad-hoc analysis.
Power BI is a suite of business analytics tools that connects to various data sources and simplify data preparation to create visually interactive reports that are easy to consume.
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