Here's how you can revolutionize data warehousing architectures as a professional.

Data Warehouse: A big storage space for a company's historical information, all in one place. Why? To understand trends, make better decisions, and see the bigger picture. Imagine comparing sales figures year-on-year to see what's working. How? Data gets collected from various sources, cleaned up (like fixing typos), and organized for easy analysis. Who uses it? Analysts, business leaders, anyone who wants to understand the company's data to make informed choices.

Few ways to revolutionize Data Warehouse architectures: 1. Cloud adoption - Leverage cloud services for scalable, on-demand data storage to reduce cost. 2. ETL to ELT - Move to ELT model that allows handling of very large volumes of unstructured data and is more aligned with the business ask. 3. Data Models - Efficient data models to implement complex queries and analytics and constantly evolve the models to meet the ever changing demand/requirements 4. Real-time data & Analytics integration - Enables real-time data access and insights to be available on demand 5. Security Controls - Robust security measures, encryption, role based access etc. to ensure data integrity is maintained

Transition traditional on-premises data warehouses to cloud-based solutions like Amazon Redshift, Google BigQuery, or Snowflake. Cloud data warehouses offer scalability, elasticity, and built-in optimizations that can revolutionize data storage and processing.

Cloud data warehouses operate on a pay-as-you-go model, eliminating the need for upfront hardware investments and reducing the total cost of ownership.

Consider the Data Lakehouse. While cloud-based warehouses excel, explore the data lakehouse concept. It merges the structured data organization of a data warehouse with the raw, flexible storage of a data lake. This empowers businesses to house all data (structured, semi-structured, unstructured) for future analysis possibilities. However, a data lakehouse requires robust data governance practices to maintain data quality and accessibility. By strategically choosing between cloud warehouses and data lakehouses, you can tailor the architecture to your organization's specific needs and future data aspirations.

Implement efficient mechanisms for extracting data from diverse sources, including databases, files, APIs, and streaming platforms.

Move towards modern Extract, Load, Transform (ELT) processes that leverage the processing power of cloud platforms. This approach shifts data transformation closer to the analytics stage, reducing latency and simplifying data workflows.

Break Down Silos with Data Lakes. Integrate data lakes alongside your data warehouse for a more holistic data picture. Data lakes store raw, unprocessed data, allowing you to capture valuable insights beyond what's traditionally stored in the warehouse. This empowers advanced analytics like machine learning and real-time processing. However, implement data governance to ensure data quality and security across both systems. Remember, a well-architected data warehouse with optimized ETL and a connected data lake empowers data-driven decision making at all levels of your organization.

Move T in ETL to warehouse layer than intermediate layer for efficiency of resource utilization and also keep transformation near to analytics layer. Many tools like DBt works great for transformation of data. This way we have the raw data as-is in data lake and can be used for various transformations without reading same data multiple times.

The Data Vault approach has several benefits: 1. the separation into well-defined table types (like Hub, Links, Satellites, PIT, Bridge) simplifies the implementation of standardized load patterns for the ETL processes. 2. many business requirement changes can be made simply by adding them to the data model (e.g. by adding a satellite), so that existing ETL processes do not have to be changed, but can simply be supplemented with new ETL processes. 3. from 1. and 2. there is a high potential for automating Data Vault. Due to the higher number of tables resulting from data vault modeling, the highest possible level of automation is strongly recommended. In contrast to Data Vault, dimensional modeling is particularly suitable for data marts.

Begin by thoroughly understanding the business requirements and objectives that the data warehouse is intended to support. Utilize star schemas or snowflake schemas to optimize query performance and facilitate complex analysis across multiple dimensions.

Consider a Data Governance Framework. While sophisticated models are crucial, ensure data quality and consistency with a strong data governance framework. This framework establishes data ownership, access controls, and quality standards. Standardized data definitions and clear roles for data stewards will minimize errors and streamline analysis. Additionally, explore data virtualization for a unified view across disparate sources. This eliminates the need for complex data integration and empowers business users with self-service analytics

Die Frage des Datenmodells und seiner passenden Gestaltung ist unabh?ngig von der Entscheidung, dieses on-premises oder in der Cloud zu realisieren. Das Datenmodell muss zu den Anforderungen des Unternehmens passen, also zu den Antworten, die auf Fragen erwartet werden. Bestimmte Antworten sind für viele Unternehmen gleich, aber meiner Erfahrung nach ist ein Anteil von 30-40% der Fragestellungen recht spezifisch. Diese Fragen erfordern z.B. andere Dimensionen und stellen so andere Anforderungen an Datenmodell wie ETL-Prozess.

Dimensional models includes Star schema and snowflake schema as common models. 1. Star Schema: It uses dimensional modeling i.e, dimension tables with descriptive attributes that provide context for the measures in the fact table. Advantages: 1. Easy to understand and query. 2. Optimized for aggregations and reporting. Considerations: 1. Denormalized dimension tables may have redundant data. 2. Requires proper ETL processes. Snowflake Schema: It is a normalized version of the star schema. Advantages: 1. Reduces redundancy leading to space efficiency. 2. Easier updates to dimension data. Considerations: 1. More joins in queries. 2. Slightly slower due to additional joins.

Events triggered by data streams can be processed asynchronously, allowing for decoupled and scalable architectures that can handle bursts of incoming data without overloading downstream systems.

Break Down Data Silos. Beyond real-time data, consider a data lake architecture alongside your data warehouse. This lets you store and analyze all your data, structured and unstructured, in its native format. This breaks down data silos and unlocks valuable insights previously hidden across your organization. Utilize data lake management tools to curate and govern this data for seamless integration with your data warehouse for focused analysis. This hybrid approach provides the best of both worlds: real-time insights and historical analysis from a unified data platform.

Bei der Anwendung von Echtzeit-Daten muss das Unternehmen kritisch bewerten, wo diese wirklich notwendig sind. An vielen Stellen wird heute Echtzeit verlangt, wo sp?ter kein Nutzen damit erzielt wird. Die Implementierung ist dadurch deutlich aufw?ndiger, dass diese Forderung erhoben wird. Nur, weil alles schneller bekannt werden kann, hei?t das nicht, dass dies einen Nutzen für Unternehmen bringt. Ein Dashboard in der Produktion über die aktuelle Auftragslage auf den einzelnen Maschinen muss nur in dem Intervall aktualisiert werden, in dem Mitarbeiter mit der Information arbeiten k?nnen. Wohingegen die Prozessdaten einer automatischen Fertigungslinie als Stream aufgezeichnet werden und angezeigt werden müssen, um Erkenntnis zu liefern.

San based and cloud based architecture usage for real time data processing loading analysis & analytics implementation and reporting

Ensure date quality issues are resolved quickly and in a timely manner. Utilise the transformative power of automation and AI to streamline processes and to ensure maximum benefit of data assets to the organisation.

Monitor key performance indicators (KPIs), model drift, and data quality metrics to identify opportunities for refinement and enhancement.

Break Down Silos. Data warehouses traditionally exist as separate entities. To unlock true potential, break down data silos by fostering an enterprise data lake. This unified repository combines structured and unstructured data from various sources, allowing for broader analysis and fostering collaboration across departments. While data governance is crucial, prioritize data accessibility and encourage exploration to empower data-driven decision making at all levels.

Integrate machine learning and AI capabilities into the data warehouse. This allows for automated data analysis, anomaly detection, and predictive insights, leading to more informed business strategies.

PowerBI is a powerful tool and there are many Tableau, SAP BO. I have seen business got powerful insights live on a regular basis to make/tweak decisions unlike earlier DWH which used to provide updates on weekly/monthly refreshed data. Easy to integrate the Analytics tools with other channels like email, teams, confluence or other intranet media you might use in your org

To fundamentally transform data warehousing, it's crucial to incorporate advanced analytics into its architecture. This integration enables users to extract deeper insights directly from the warehouse. Acquaint yourself with a range of analytics tools and platforms, and learn how to embed them within the data warehouse environment to create data marts quickly. This enables users to use the data quickly without waiting for preprocessing and store in defined models.

Embrace agility. Move beyond traditional, monolithic data warehouses. Explore agile methodologies like data lakes or data vaults. These offer more flexibility to ingest diverse data sources, including unstructured data, for advanced analytics. This future-proofs your architecture for evolving business needs. Champion Data Governance. Data quality is paramount for insightful decisions. Establish a data governance framework with clear ownership, standards, and access controls. This ensures consistent, high-quality data across the warehouse, fostering trust and enabling reliable analytics.

Data security is vital in today’s world as a data breach may result in legal consequences and cause substantial financial losses and reputational damages and loss of trust and confidence in an organisation. Appropriate measures and encryptions must be in place to safeguard against data breaches

"revolutionize data warehousing architecture" is the wrong way to look at data warehousing. It is not about revolutionizing the architecture like this article suggests, i.e. using real-time data, advanced ETL, etc. It is not about choosing a fancy architecture, excellent ETL tool, top notch data model or amazing analytic/BI tool. Data warehousing is about understanding the requirements of your own company, and fulfill it. Not understanding what's out there in terms of technology, but what the business users INSIDE your company NEED. Without this understanding, everything else is useless. You can't implement anything without first understanding what is required.

Continuously evaluate and adopt emerging technologies and trends in data warehousing, such as serverless architectures, containerization (e.g., Kubernetes), and augmented analytics, to stay competitive in the rapidly evolving data landscape.

Advocate for self-service analytics tools within the data warehouse. This empowers business users to explore data independently, fostering data-driven decision making across the organization.

One thing I would point is in any data warehousing solution key challenges are security and how much time it takes when extracting and transforming the data .In many case when handling the peta bytes of data ETL consumes more time. So trying to understand how the data is stored and building ETL helps .I will try to breakdown in two examples. If you are trying to read the data in gzip format as a source from single file it takes lot of time in this scenario we are trying to reduce the storage cost. But when comes to gzip one single large file extraction take lot of time more dpu are needed. But if the source data is zstd and z order is enabled on the column which is used frequently ,it achieves faster performance than stored in parquet.

Differentiating between OLTP (Online Transaction Processing) and data warehouse models is crucial for seamless usage and optimal performance in data management systems.