Your modern data stack is like a supply chain for data: Raw materials are sourced from disparate places, transformed into products, shipped, and used by consumers. When your needs expand, you scale your operations accordingly.
This architecture is made up of several components, all working together to move, store, and use data as efficiently as possible. When data stacks are hosted in the cloud, they are flexible, efficient, and modular.
Let’s break down how this framework functions, its main benefits, and how to build a modern data stack from the ground up.
What’s the modern data stack?
A modern data stack is a collection of cloud-based tools, systems, and workflows that let you ingest, store, transform, and analyze data at scale. Using this digital assortment, you’ll have full visibility and control over the information you collect and how you use it. Whether it’s allowing users to add new data sources or letting you find actionable insights from your content, modern data stacks find a way to do it all.
As each layer is independent, you can scale your data stack to meet whatever needs your business has at the moment. Running low on storage? Scale your data warehouse. Need to expand analytics capabilities? Add in another BI tool to keep the insights flowing.
Modern data stack vs. legacy data stack
While legacy stacks exist on-premises, modern data stacks are cloud-based. With just a few technical configurations, your system can be up and running in no time. This approach lets you scale your data infrastructure quickly, avoiding any downtime commonly associated with scaling local servers.
Migrating over to a modern stack also lowers the technical barrier to entry for ingestion. Instead of having to build and maintain complex pipelines, teams can rely on managed services to automatically extract and load data from source systems. Less manual effort and more reliable data.
What are the benefits of a modern data stack?
When so many internal processes rely on high-quality, consistent information, a modern data stack removes friction across the entire analytics lifecycle.
Here are some of the main benefits of a modern data platform:
- Increased data scalability: Cloud services decouple storage and compute, letting you scale whichever service you need elastically. If you’re ready to ingest more data, your system can automatically scale to accommodate.
- Automation: A fully managed stack automates everything from data ingestion and validation to monitoring and syncing. And, with over 700 connectors to easily deploy, Fivetran lets you automatically ingest data from top sources with ease.
- Data centralization: By ingesting and aggregating data into a centralized storage system, you improve visibility over your content. Centralization leads to easier compliance, governance, and analysis through your singular view.
- Increased business flexibility and agility: Whenever you want to add new tools, extend ingestion to new sources, or add analytics engines, a cloud-native solution is ready to deploy.
Components of the modern data stack architecture
Data stack architecture interacts with data across the entire ecosystem, spanning from source connectors all the way to analytics and governance. There are several components that work together to make sure all details move securely and effectively from one location to another.
Data pipeline
Data pipelines handle extracting and loading data from your source systems (like SaaS apps, event streams, and databases) into a centralized destination. Modern data stacks use automated data pipelines to reduce manual maintenance and minimize latency.
Destination
Businesses typically store their company information in a cloud data warehouse or lakehouse, helping to centralize all of their content. Several platforms, like Snowflake, Databricks, and BigQuery, handle huge volumes of semi-structured and structured data. They also separate storage from compute, meaning you can scale one or the other if needed.
Transformations
After loading data into a destination, you’re ready to clean, model, and enrich it. These processes describe the transformations you apply to raw data, helping to standardize and ready it for analytics workflows. To keep full visibility over your content, you can transform data directly inside a cloud warehouse.
Business intelligence and data visualization
BI and visualization turn transformed data into actionable insights for your business. This information is then used to produce up-to-date dashboards, reports, and analytics that let your business make data-first decisions. You’ll find that many leading tools in this space require very little technical knowledge, democratizing access to analytics.
Data governance
The data governance layer refers to all the processes you use to make sure your data follows regulatory guidance. You may have lineage tracking, access controls, metadata management, and data quality checks throughout your data pipelines to embed governance.
Data orchestration
With the sheer volume of moving parts in a modern data stack, you’ll need to keep everything working smoothly. Orchestration tools bring a level of order to your system, triggering workflows sequentially, managing dependencies, and monitoring ecosystem health. These tools will be your go-to solution if you notice performance issues and want to debug your system.
How to build a modern data stack
A data stack includes numerous tools, systems, and processes to ensure high-quality data makes it from source to analysis.
When building out your data architecture from scratch, use the following steps:
- Select and implement a data warehouse: Your data warehouse needs to be able to hold all of your data, scale alongside your storage and compute needs, and integrate with other parts of your data stack. Look for cloud-native solutions that prioritize performance, flexibility, and scalability.
- Connect source systems using a data ingestion tool: Data ingestion tools allow you to connect to SaaS tools, databases, and event sources with pre-built connectors. These automate ingestion, letting you focus your data engineering efforts elsewhere.
- Transform and model data for analysis: Use transformation tools inside your warehouse to clean and structure raw information. These resources need to produce analytics-ready datasets your teams can use in their own workflows.
- Enable reporting and analytics with BI tools: BI systems let you easily select data for analysis and find patterns and trends. Reporting engines help pull insights from your information that you can turn into actionable business advantages.
- Push curated data back into operational tools: Sync enriched data from your warehouses back into the operational tools you use every day, like CRMs or marketing platforms. Reverse ETL connections make sure your operational tools always have timely, accurate, and useful information to use.
- Train teams on the modern data stack: Help your teams understand the moving parts by offering training modules. Write up some documentation or offer some demos to make sure everyone knows how to get the most from your new data stack.
Top 3 modern data stack tools
With so many different components to juggle, we’ve cut through the noise with some recommendations for each segment.
Here are three modern data stack tools that make ingestion, storage, and analysis as easy as possible:
- Fivetran: Fivetran offers fully managed pipelines, letting you completely automate the data integration process. Get high-quality, reliable data continuously powering your data architecture.
- Snowflake: Snowflake is a cloud-native data warehouse that lets you independently scale storage and compute based on your needs.
- Looker: Looker is a business intelligence platform run by Google, offering cloud-native, AI-driven analytics workflows for your data. Use it to turn your transformed data into actionable insights.
How Fivetran powers the modern data stack
When discussing Fivetran’s automations, Sean Rober, Head of Analytics at Zenefits, stated that “Fivetran has easily saved us 20 hours a month in human capital,” allowing the team to focus on revenue-driving tasks with the time saved from not managing ingestion.
Fivetran serves as the critical ingestion layer of the modern data stack, completely automating extraction and load so your business can transform data however it likes. With 700+ pre-built connectors, zero-maintenance pipelines, and seamless integration into modern destinations like Databricks and Snowflake, Fivetran is the full-scale service that powers your data infrastructure.
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