The term modern data stack gets used in almost every discussion about data today. However, if you are a VP or Head of Data, your main concern is not about what it actually is, but rather whether or not it delivers on its promise.
The pitch you have likely heard gives you these highlights:
- Faster analytics
- Scalable pipelines
- Infrastructure ready for AI
- Reduced operational cost
In reality, the vast majority of teams that adopt a data stack find themselves with an assortment of disparate tools, continuously increasing costs, and an unclear line of ownership.
So let’s start over.
This guide will provide a comprehensive explanation of the modern data stack in straightforward spoken English; however, most importantly, it will help you think about it as an integrated system rather than merely a collection of individual components.
A Quick Overview: What’s the Modern Data Stack?
At its simplest definition, the modern data stack is a cloud-based architecture designed for the collection, storage, transformation, and analysis of data.
While these systems may have similar architectural features as traditional systems, they also have eight distinct characteristics.
These include all of the following:
- Modular
- Scalable
- API Driven
- Built on Cloud Infrastructure
The best way to explain the modern data stack is that it consists of several different tools which can each be viewed independently but all together they help move data from the source to insights with minimal effort.
A Beginning-to-End Overview of a Modern Data Stack
There are many ways to generate data. Data is ingested into a pipeline Data is stored in a data warehouse Data is transformed into meaningful models Data is used through business intelligence or AI tools.
Because of this natural progression, many leaders are now asking:
"What is a modern data stack, and why is it important?"
The reason is that it has everything to do with how quickly your company can turn raw data into actionable decisions.
Why the Modern Data Stack is Important Right Now
The demand for a modern data engineering stack is driven by three main catalysts:
1. The rapid increase in data volume
Organizations are generating more data now than ever. Old technologies can't accommodate the volume of new data.
2. The migration to cloud infrastructures
Modern applications require cloud-native architectures that allow for the ability to scale up and down on-demand and provide flexibility for the changing marketplace.
3. The integration of AI and real-time analytics
Modern application development requires real-time data pipelines and the integration of AI with those pipelines.
As reported by McKinsey, organizations that effectively leverage their data will be 23x more likely to acquire customers and will be 6x more likely to retain customers.
Key Takeaway: The modern data stack is not a trend; it's a reaction to the need for businesses to be nimble, scalable and able to manage complexity.
What are the key components of a modern data stack?
A frequently asked question by leaders is:
"What are the key components of a modern data stack?"
The components can be grouped into five layers.
1. Data Sources
This is where data is collected from,
- Software as a Service (SaaS) applications, such as (CRM and ERP)
- Product analytics applications
- Databases and/or Application Programming Interface (API) calls
Data source connections produce raw data - either unstructured or semi-structured data which will need to go through the
Data Loading (ETL / ELT)
Data-injected into a database from a source.
Some such examples are:
- Fivetran
- Airbyte
At this stage we ask another primary question:
How do I select anETL/ELT tool for developing scalable data pipelines?
You want:
Reliability Connector Support. Scalability. Monitoring Capabilities:
3. Data Storage (DW)
The Data Warehouse is the main source of the modern data stack.
Some common platforms include:
- Snowflake
- BigQuery
- Redshift
This level contains all the:
- Storage
- Query Execution
- Data
4. Data Transformation
The Raw Data is transformed into usable information.
Common tools for data transformation are:
- dbt
- Spark
This is where the logic of the business will be.
5. Data Consumption
The outputs will be consumed by using:
- BI tools (Tableau, Looker)
- ML Models
- Dashboards & Applications
The overall point is that the modern data stack is not about the tools, it is about how they integrate together across all three layers.
How Does a Modern Data Stack Architecture Work?
Most executives are looking for a diagram of a modern data stack. Most diagrams do not accurately depict the reality.
Here is one example of how to visualize the data stack architecture: a layered architecture model.
- Layer 1: BI, Dashboards, AI apps
- Layer 2: Transformation and Modeling
- Layer 3: Data Warehouse
- Layer 4: ETL Pipes
- Layer 5: Applications and Systems
This is a flexible architecture as you can change your tools without disrupting the entire system.
Modern Data Stack for Artificial Intelligence
Another new area of interest:
How does a modern data stack support AI?
A stack designed for AI has:
- Feature Store
- Real Time Feed
- Model Training
- Governance
Artificial intelligence requires high quality, structured data.
Which Cloud Platforms can Support the Modern Data Stack?
CSF to Data Warehouse Cloud Modern Datastack
When it comes to building a modern data stack, one of the first things to answer is which cloud platforms support this.
Several major cloud providers support a full ecosystem for developing a modern data stack. When selecting these platforms, you should consider your current infrastructure, your teams experience with cloud technologies, and what features your current data stack has that you would like to replicate.
Also, while investigating what cloud platforms to use, another common question people pose relates to which Data Warehouses are commonly used in modern data stacks.
Common Data Warehouse Options
As it turns out, the answer to this question is still the same as it has been.
- Snowflake
- BigQuery
- Redshift
These three Data Warehouses are the major components of a well-designed modern data stack.
They enable:
- Scalable Storage
- Fast Querying
- Integration into Data Transformation Tools
ETL Tools for a Modern Data Stack
Many executives have or will begin asking the question: what are the modern data stack tools?
Ingestion Tools:
- Fivetran
- Airbyte
Storage Solutions:
- Snowflake
- BigQuery
Transformation Tools:
- dbt
Tools for Orchestration:
- Airflow
Business Intelligence Solutions:
- Looker
- Tableau
Modern Data Stack ETL Tool Selection Criteria
Another important question that must be considered when designing a modern data stack — how do I select the best ETL tool for a modern data stack?
- Ease of Integration into your existing technology stack
- Data Reliability
- Monitoring and Alerting Capabilities
- Cost Efficiency
While developing a modern data stack, do not over-optimize early! Start Simple and then Scale!
Reasons Why Companies Are Investing in Modern Data Stacks
1. Scalability
Cloud-native systems are always in a position to scale as their datasets grow.
2. Flexibility
You can easily change Third-Party Tools within your Modern Data Stack without Require Reengineering of the Entire System.
3. Speed
- Modern Data Stacks provide quicker processing times for Data and Analytics.
4. Cost Optimization
- You only pay for what you use on the cloud.
5. AI-Ready for Advanced Analytics and Machine Learning
Deloitte has reported that companies employing a cloud-native architecture have increased their operational efficiency by as much as 40%.
Today’s Modern Data Stack Challenges
While there are many benefits to the modern data stack, organizations still face significant challenges.
1. Proliferation of Tools
The overwhelming number of tools available has led to complexity.
2. Data Governance
It is often difficult to maintain data quality and compliance.
3. Management of Costs
The costs of cloud usage can grow dramatically if not carefully controlled.
4. Lack of Resources
Teams need to acquire new skills related to managing modern systems.
5. Integration Complexity
Some tools do not work together effectively.
Is the Modern Data Stack Still Relevant?
There has been a significant increase in internet searches using the phrase “Is the modern data stack dead?” to which there is a very clear short answer: No.
What is changing within the modern data stack?
- There is a movement toward unified platforms.
- There is an increase in the popularity of lakehouse architecture.
- There is an increase in focus on data governance.
Although the concept of the Modern Data Stack is not dead, the implementation is maturing.
Example of a Company Building A Modern Data Stack
A Software as a Service (SaaS) company had challenges with:
- Fragmented data pipelines
- Reporting lagged behind
- Expensive cloud bill
Logiciel assisted in:
- Designing a scalable architecture for a modern data stack
- Creating a centralized data warehouse
- Improving ETL workflow efficiencies
Results:
- Analytics improved by 35%
- Cost of analytics decreased by 25%
- All teams had access to reliable data
“Modern Data Stack” vs “Traditional Stack”
It’s critical that you understand the key differences between a “Traditional” and a “Modern” Data Stack.
Traditional
Modern
On-Premise
Cloud-native
Monolithic
Modular
Batch processing
Real-time
Heavy Maintenance
Managed
Key insight: A Modern Data Stack is built to provide speed, scale, and flexibility.
What is the modern data stack?
What are the core elements of a modern data platform?
What are the tools used in the modern data stack?
What cloud platforms are compatible with the modern data stack?
What are the advantages of using a cloud-native data architecture?
Final Thoughts on the Modern Data Stack
Think of the modern data stack as a series of decisions related to system architecture vs a checklist of various tools.
The best modern data stacks align with:
- The business goals of the organization
- The capabilities of the team using the stack
- Future use cases that may involve AI
- A balance of governance and flexibility
If you treat architecting the modern data stack as an exercise in tool selection, you will face challenges as your organization grows, but if you treat it as an architecture strategy, you should have no issues scaling.
What does Logiciel think about the modern data stack?
At Logiciel Solutions, we work with engineering leaders to help them architect and build modern data stacks that provide true business value, not just colorful dashboards. Our AI-first software engineering teams design and build scalable, reliable, and cost-effective data systems that are built for advanced analytics and AI workloads.
Therefore, if you are currently evaluating or redesigning your modern data stack, we can help you move away from multiple, disparate tools and towards a high-performance integrated data platform.
If you would like to discuss how we can assist you in accelerating your data strategy, please contact us to schedule a call.
