The Future of Data Warehousing
Image created with AI, credit Microsoft Designer
“When building a data warehouse, rely on best-practice – not intuition”. – Ralph Kimball
The rise of Generative AI has transformed the technology landscape, but is too new for best-practices to have taken shape like the decades of data warehousing. While data warehousing has evolved since it came on the scene as early as the 1980s, its core principles remain steadfast.
The AI revolution has forced executives to reassess their data capabilities with newfound urgency. My journey as a consultant tells this story: In 2017, data warehouses were often met with blank stares and tight purse strings. The pandemic’s push toward remote work in 2020 sparked renewed interest, as leaders sought better visibility into their scattered operations. By 2024, amid the GenAI boom, data infrastructure projects – including data warehousing – have begun receiving unprecedented investment.
What is a Data Warehouse?
Many organizations mistakenly rely on their transactional databases (like ERP or CRM systems) as their primary reporting and analytics platforms, not realizing these systems are optimized for processing day-to-day operations rather than complex analytical queries. A data warehouse is a separate database system specifically designed to consolidate data from multiple systems and structure it for efficient reporting, analytics, and business intelligence – allowing organizations to analyze historical trends and patterns without impacting the performance of their operational systems. Unlike transactional databases that are optimized for quick individual record updates and real-time processing, data warehouses use different database schemas (like a star schema) and storage techniques that make it much faster and more efficient to run complex queries across large datasets, often spanning years of historical data.
Data warehousing’s 40-year evolution has yielded battle-tested principles that remain relevant today. The fundamentals are now solid: proven methodologies for data movement (ETL/ELT), dimensional modeling for analytics, and established patterns for handling both structured and semi-structured data.
While the foundation is set, GenAI has opened new frontiers in unlocking value from unstructured data – text, images, audio, and video. Fortunately, the groundwork laid by modern data architecture, particularly the pattern of using data lakes as landing zones before processing data into warehouses, has prepared organizations for this shift.
Data Warehouse Trends
In this blog, we’ll break down the biggest trends shaping the future of data warehousing and how they can impact your business.
- Merging Data Lakes and Warehouses: Modern platforms are blending the structure of warehouses with the flexibility of lakes to create “data lakehouses.”
- Zero ETL and Zero-Copy Sharing: open data formats simplify data movement and increase efficiency.
- Decoupled Compute Engines: Serverless and cloud-native architectures, in combination with open data formats, makes it easy to call the right compute for the job.
- Model Automation: Tools that automate analytical model generation, dev-ops and cascading changes based on dependencies reduce manual data engineering labor.
- Real-Time Insights: Innovations in real-time streaming and processing are meeting the demand for instant data analysis and proactive actions.
- AI Transforming Warehousing: AI can automate metadata generation, clean up data quality issues, write ETL, suggest performance optimization and more.
- Better Governance and Security: With tighter regulations and rise of cyber risk, robust data governance is essential.
Let’s take a closer look at some of these developments.
Merging Data Lakes and Data Warehouses
For years, businesses had to choose between data lakes, which handle unstructured data, and data warehouses, built for structured data. Now, with the rise of data lakehouses, you don’t have to pick. Lakehouses combine the flexibility to store unstructured data of lakes with the structured or semi-structured ability of warehouses.
How It Works: By using advanced file formats (Parquet) and metadata layers, lakehouses ensure one place for your unstructured, semi-structured, and structured data for smooth analytics while maintaining data integrity on a single copy of data. Your data science and data engineering teams can rejoice as they can work together harmoniously.
Zero ETL and Zero-Copy Sharing
ETL processes (Extract, Transform, Load) have long been a bottleneck, consuming time and resources along with laborious maintenance. Zero ETL services are provided by modern SaaS vendors by duplicating all your source system data to a data platform and automatically keeping it up to date. Zero copy sharing, thanks to open data formats and Apache XTable, further eliminates data pipeline needs by making data accessible between analytics platforms without duplication.
What It Means: Vendors finally realize customer’s need to have their data in a place where they can build their own analytics. Gone are the days of relying on a clunky API to pull all your data for analytics. With zero-copy sharing, multiple teams or external partners can access the same data without creating redundant copies. This is crucial for industries like healthcare and finance, where accuracy and efficiency are key and the stakeholders are numerous.
Decoupled Compute Engines
A compute engine used to be specific to its database. You would move data into a certain type of database for its unique strengths. Data platforms like Snowflake set the standard by separating compute and storage to automatically scale based on workload needs and turn off when they aren’t being used. Platforms like Microsoft Fabric took this one step further by unifying various compute engines for large data crunching, machine learning, and real-time analytics to be called as needed on top of the same copy of data.
Why It Matters: Serverless compute engines no longer rely on where the data is stored. Compute can burst when capacity is needed and turn off when it isn’t, which means better performance and lower costs. The ability to use different compute engines on the same copy of data to make the most of their strengths like Spark, SQL, or Photon reduces the need for moving data around and managing more data pipelines.
Model Automation
Data warehouse automation tools have revolutionized how we handle enterprise data architecture by automatically generating models and transformations. These tools help ease manual efforts of fixing analytical models as business needs change. Surprise, business needs change OFTEN!
How It Works: automation tools auto-generate dimensional models (like type 2 slowly changing dimension tables), act as containers for rapid deployment across environments, and perform cascading updates as models change to ensure data accuracy without manual intervention.
Real-Time Insights
If I had a penny for every time a customer told me they need their data in “real-time” I would be a millionaire. For context, real-time means down to the second data latency. Does your finance data really need to be real-time? No, but maybe your inventory does. Micro-batch processing, change data capture (CDC), and streaming integrations can provide low latency data (seconds to minutes) for real-time reporting. If you need sub-second latency direct streaming and skipping the data warehouse may be needed.
Why it matters: Reports can be more up-to-date than ever before. No more relying on a report that is only as fresh as last week or last night. To take it one step further, services like Microsoft Data Activator can push notifications to business teams based on rules or changes so you can act and actually make an impact when it is needed most. Imagine that?? Finally making business decisions when you need it! No more continuously checking reports and hoping you catch an insight.
AI Transforming Warehousing
Data warehouses can benefit from GenAI. They’re becoming cheaper to build with more proactive maintenance. Examples include, but not limited to:
- Generating meta-data based on the type of data in a column or table.
- Generating and maintaining data dictionaries.
- Building and maintaining data pipelines based on upstream changes and downstream model needs.
- Copilots for providing suggestions on optimal analytics development and query optimization.
Governance and Security: A Must-Have
With stricter regulations like GDPR and CCPA, solid governance and security are non-negotiable. A modern data warehouse should have tooling to:
- Track data lineage to ensure transparency and clear data definitions.
- Implement data sensitivity labels and controls to protect sensitive information versus relying on role-based access.
- Real-time alerts to detect poor data quality and anomalous data.
Conclusion
The future of data warehousing is exciting and full of potential. Having a data warehouse is a crucial step in your GenAI journey to ensure you have visibility over your data and ensure quality. Whether you’re managing data for a small business or a global enterprise, keeping these trends in mind will help you stay competitive.
A data warehouse or lakehouse has become a necessity for modern business. Fortunately, building them has become cheaper and more powerful than ever.
Don’t plan on ever hiring data scientists or running machine learning models? Opt for a data warehouse.
Want to get fancy and squeeze your data for every last drop of value? Opt for a data lakehouse.
Have questions or need a better strategy for your data? Let’s chat about building a smarter, future-proof data system for your business.
