As a leader in data engineering today, you are accountable not only for pipelines but also for influencing your organisation's thoughts, actions, and growth.
However, many teams continue to wrestle with one fundamental question. What exactly does data warehousing mean and how is it changing in the context of the modern technology stack?
Traditionally, data warehousing is not just an archive of structured datasets intended for reporting purposes. Warehousing has become the backbone of your organisation's ability to perform real-time, machine-learning based analytics and build AI-first systems.
In this blog, I will cover both antiquated notions of data warehousing and modern-day implementations in order to assist you in aligning your architecture with your organisation's objectives. At the same time, I will also connect traditional concepts with the cloud-native concepts that will drive the most performance, scalability, and intelligence.
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Defining What Data Warehousing Is/Is Not
The actual definition of a data warehouse is simple; it is a place to store, organise, and manage large amounts of structured data from various sources in order to enable businesses to perform analytics and make decisions. A data warehouse's main purpose is to support analytics while enabling decision-making.
Additionally, unlike transactional databases, data warehouses are designed with read optimised access instead of write. As a result, this means that a data warehouse should have significantly fewer read/write times than a traditional database.
The main features of a data warehouse:
- A complete place to store integrated data
- Built to support analytics (and has no transactional supports)
- Designed for historical data analysis
- Has a structured schema to support performance
This is important because the benefit of data warehousing to a data engineering lead is one significant result: data can be trusted, and data can be queried with a consistent level of performance for large volumes.
When you don’t have a data warehouse, an organization runs into:
- Data silos that are disconnected
- Reporting cycles that take an extended period of time
- Different departments using inconsistent measures
- Limited to no ability to prepare data for AI capabilities
A simple example would be an e-commerce business using different places to store their data.
For example:
- Orders would be kept in the transactional database
- Marketing data would be sourced from an ad-serving platform
- Customer/user behavioral data would be acquired through a variety of different tracking methods
A data warehouse pulls all the different places those types of data are collected for an e-commerce business into one coherent source of truth.
This data can then be utilized for revenue forecasting, cohort analysis, and personalization.
The overall important concept of data warehousing is that it takes unstructured data and converts it into structured intelligence.
Why is data warehousing important to modern businesses?
1. The unified decision layer:
Executives and cross-functional teams rely on a consistent set of dashboards and KPIs. When a data warehouse does not exist, every department's method for calculating metrics is different, leading to conflicting decisions made based on differences in calculations.
2. An infrastructure that can scale with growth:
The amount of data being generated is increasing exponentially, and traditional data architectures cannot support the scale/size of the data being collected. Data warehouses are designed to horizontally scale out to accommodate the following types of data:
- Billions of rows
- Complex joins
- Many thousands of concurrent queries
3. A foundation for artificial intelligence and machine learning initiatives:
Modern AI programs depend on clean, structured data that has a history associated with it. Industry studies indicate that more than 70% of AI and ML initiatives fail due to poor quality and/or lack of accessible data.
A data warehouse that is properly architected will help organizations overcome these challenges by providing:
- Feature corporate datasets
- Data lineage
- Data governance layers
4. Quicker time for gaining insight:
Optimized query engines enable teams to go from hours to seconds to generate analytics.
The overriding message: Data warehousing is a must-have.
Data Warehouse Design Fundamentals
Where design engineering begins is combining all the pieces to create complete infrastructure design, which allows modern infrastructure to provide multi-level functionality. While there are many layers included within a complete data warehouse model, the following are the fundamental layers:
1. Data Source
Every organization has a different array of sources; however, the most common types are as follows:
• Customer Relationship Management systems
• Software as a Service applications
• Internet of Things devices
• Internal database systems
2. Ingestion Layer
Ingestion layer is the point at which all data being received actually enters the system. The most common methods for ingestion are as follows:
• Batch ingestion via ETL or ELT pipelines
• Streaming - real-time data ingestion
3. Storage Layer
Utilizing data warehouse or database; however, examples of storage layers are as follows:
• Columnar storage systems
• Distributed filesystem
4. Transformation Layer
The transformation layer is the process of cleaning, structuring and creating a model of the received data. This can be accomplished via the following methods:
• ETL - Extract, Transform, Load
• ELT - Extract, Load, and then transform
5. Presentation Layer
The presentation layer is where the stored data is made available to users through various means, including the following:
• BI tools
• Dashboards
• API
6. Governance / Metadata
Includes the following:
• Data Catalogs
• Approval Workflows
• Lineage Tracking
Data Movement Example Flow
Data enters through raw (unstructured) form then moves through: Ingestion → Transformation → Data Stored → The Available for Analytic Use
Summary - A data warehouse is not a stand-alone solution. It is a multi-layered infrastructure solution designed for high availability and scalability.
Traditional Data Warehousing vs Modern Cloud Data Warehousing
Traditional Data Warehouse
• On-Premise
• Fixed structures
• High cost to get started
• Limited ability to scale
Modern Cloud Data Warehousing
• Cloud based
• Elastic Scaling
• Pay for what you use
• Compute & Storage separated
Major Option Differences
Feature | Traditional | Modern Infrastructure | On-Premise | Cloud Scalability | Limited | Elastic Cost Structure | Cap Ex | Op Ex Performance | Fixed | Dynamic
What Prompted This Shift?
Businesses today need
• Real-time Visibility
• Global scalability
• Faster Turnaround Time
Cloud based data warehousing meets all three of these needs.
Summary - The modern approach to Data Warehouse aligns with Agile Data Warehouse and AI Creation design principles.
Data Warehouse vs Data Lake - What's The
This is one common question that is asked by many engineering leaders.
Data Warehouse
Uses Structured Data Schema-on-Write Optimized for Analytics
Data Lake
Uses Raw, Unstructured Data Schema-on-Read Flexibly Stored Data
When To Use
Use Case Best Solution
Business Intelligence Dashboards
- Data Warehouse Raw Data
- Data Lake Machine Learning
- Both
Lakehouse Growth
A New Data Architecture That Combines Both Light Houses and Heavy Warehouses To Provide:
The Flexible Nature Of Data Lakes Data Warehouse Performance
How Data Warehousing Works Practically?
Let’s Break Down The Steps A Data Engineer Will Take To Implement A Data Engineering Workflow.
Step 1. Data Collection
Data Flows From Different Sources Into A Data Ingestion Pipeline.
Step 2. Data Cleaning
Removes Duplicates, Normalize And Format Data In The Same Way.
Step 3. Data Transformation
Apply Business Logic, Aggregate Data And Join Data.
Step 4. Data Modeling
Model Types They Can Use:
Star Schema, Snowflake Schema
Step 5. Data Consumption
End Users Have Access To Data Through:
Dashboards, Reports, API access.
Example Scenario
A SaaS Company Tracks Their Users Through:
User Sign Up How To Use Each Feature Revenue Generated By The Company
Data Warehousing Allows You To:
Predict User Churn, Perform Product Analysis & Predict Future Revenue.
Key Points
Data Warehousing Is A Data Pipeline And Is Not A Static Repository.
Best Practices For Modern Day Data Warehousing
An Engineering Leader’s Implementation Decisions Will Govern Success.
1. Utilize ELT Vs ETL
Using Modern Warehouses Is Fast And Efficient At Handling Their Transformations.
2. Design It To Scale
Utilize Distributed Systems And Partitioning Schemes When Designing And Building Out (Growing) Your Data Warehouse.
3. Focus On Data Quality
Develop Validation Checks In Your Data Warehouse Process Along With Monitoring/Reporting Alerts.
4. Optimize Your Query Response Time
Establish An Indexing And Join Strategy In Your Data Warehouse To Minimize The Redundant Data.
5. Utilize Cloud Technology
As An Engineering Leader This Will Make Data Security Easier For Your Organization.
Governance Development
Ensure the following:
Role-based access and tracking of data lineage.
Meet compliance.
6. Real-Time Capabilities
Streaming pipelines are becoming a necessity in these areas:
Fraud detection; and real-time analytics.
Key takeaway: Modern data warehouses must also include processes alongside technology.
Data Warehousing Challenges
Even the most well-established teams face hurdles.
1. Data siloing.
Disconnected systems result in users unable to use data effectively.
2. Poor-quality data.
Inaccurate data leads to poor decisions.
3. Performance bottlenecks.
Poorly written queries will cause delays in analytics.
4. Expense Management.
Cloud-based systems will become costly if not managed correctly.
5. Complexity.
More items to manage, pipelines, tools, and governance require additional time and costs.
How to Solve:
Standardize data models; automate pipelines; monitor usage & costs; and invest in observability.
Key takeaway: The largest risk in data warehouses is operational rather than technical.
Where is Data Warehousing Heading?
Data warehouses are changing rapidly.
1. AI-first platforms.
Data warehouses will be primarily designed to use AI rather than being strictly storage systems.
2. Real-time analytics.
Real-time analytics are using streaming-first methods as opposed to batch processes.
3. Liberalized Data Access.
Users throughout the organization will access their data directly.
4. Automation.
AI-based systems will provide optimisation in the following:
Query performance;
Pipelines and orchestration; and
Data quality.
5. Tool Convergence.
The stack will be merging/ simplifing into a single unified platform.
Key takeaway: Data warehouses will soon become intelligent and autonomous systems.
Real-Life Examples of How High-Performance Teams Differ.
Top data teams view their data warehouse as a deliverable rather than a backend system.
The focus is on:
Usability of data Alignment of Stakeholders Continuous optimization of data
For enterprises, teams that embed automation into their workflows often reduce reporting latency by 30%-50%.
The critical takeaway from this is that a well-designed data warehouse is a constantly changing system that is designed to fit the goals of the business.
Conclusion - Storage -> Intelligence
Data Warehousing has now evolved beyond just being a storage concept to becoming a strategic layer of intelligence.
For data engineering leaders, the objective is no longer about just building the pipelines; it is about designing systems that will:
Scalably grow as the amount of data increases; Provide real-time insight into the data being captured; and Enable AI-driven decision making.
Logiciel Solutions is here to help technology leaders transition from data collection to data acceleration.
Our data engineering teams utilize an AI-first approach to building scalable, trustworthy data warehouse systems, and modern analytical systems with AI capabilities.
If you are contemplating recreating your data architecture, now is the time to align your architecture to look forward to the future of data warehousing.
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Frequently Asked Questions
What is data warehousing in colloquial terms?
Data Warehousing describes the process of accumulating and consolidating data from multiple sources into one central repository for the purposes of conducting analysis and reporting (helping organizations to make better decisions by providing them with a consistent, structured, and historical data set).
Why would a business want to utilize data warehousing?
Data warehousing creates the ability for organizations to perform unified reporting and make better decisions; supports Artificial Intelligence initiatives; ensures data consistency for cross-team collaboration; without having to rely on separate datasets and having to wait longer for insights.
What are the differences between data warehouses and databases?
A database is capable of performing transactional operations such as inserting, updating. A data warehouse is specifically optimized to run analytical queries, conduct aggregations, and perform historical analysis on dataset.
What type of data warehouse can organizations create?
Common types of data warehouses are enterprise data warehouses (EDWs), operational data stores (ODSs), and cloud-based (any cloud vendor) data warehouses. Today's data architecture includes lakehouses.
How does a business utilize data warehousing?
Organizations use data warehousing to conduct data analytics, reporting, forecasting, customer insights, as well as being the foundation for AI and machine learning models.
