If you want to leverage your raw data to make strategic decisions and build real-time customer experiences, you need a solid data infrastructure.
Data infrastructure is the technology that collects, stores, manages, and allows you access to your data. It includes everything from your data warehouse (or data lake) to all the cloud-native data tools that move data from place to place.
Many companies start with a cloud data warehouse as the central source for all data that’s fed by data tools like Shipyard, Fivetran, and Stitch. That’s largely because it costs less to leverage existing data centers through cloud technology than to build your own secure hardware setup to power a data-intensive business.
Whether you’re building your data stack from the ground up or looking to upgrade your data infrastructure, it’s important to know how to get started and which data tools are right for you.
What is data infrastructure?
Data infrastructure is the ecosystem of technology, processes, and people responsible for the collection, storage, maintenance, and distribution of data within an organization. When well-structured and managed consistently, data infrastructure unlocks crucial insights about company performance and uncovers opportunities (or problems) hidden in patterns of big data.
You need to bring together datasets that come from a wide range of sources—including Amazon AWS and Microsoft Azure cloud apps, internal PostgreSQL databases, and APIs. One of the main goals of data infrastructure is to make sure all of this data is high quality, moves through the right data pipelines, and ends up in data formats that are useful to your business intelligence, customer-facing apps, and data engineering teams.
Data infrastructures vary from one data organization to another. You might decide that a combination of on-site and cloud-based data solutions are best or completely rely on cloud data solutions (from data warehouse to data analysis). An ETL (extract, transform, load) platform could be exactly where you need to start. Or you might already have one and just need to build the right data pipelines to connect it to your data warehouse.
While there are many options, there is common ground to build from—ask yourself these questions when you’re building a modern data infrastructure. And no matter what kind of business you’re a part of, you’ll want to build your data initiatives to scale.
How to build a data infrastructure that scales
One of the biggest challenges to data engineers and data scientists today is to design and build a data stack that grows with your business. You might not need to support machine learning apps right now, but that could change in 6 months. In the very near future, you may lean on your data ecosystem for data-heavy AI operations, cloud data analysis, IoT device tracking, or real-time marketing initiatives.
If you don’t build your data infrastructure to handle some of these emergent technologies, you could quickly be stuck with legacy systems holding you back. Here are some steps to take to make sure that doesn’t happen.
Get buy-in from key stakeholders: Get executive buy-in to invest time (and money) into building out a scalable data infrastructure. Build out a simple picture of your current state, ideal state, and possible future state for data infrastructure. Estimate costs and investment over 1 year, 5 years, and even 10 years down the road. When you get buy-in at this level you’re less likely to get pushback in 2 years when you’re still investing in data infrastructure—and it helps executives plan out their investment roadmap.
Choose a single source of truth for business users: Choose a central location for data storage, data analysis, and data processing where your business users can source their metrics. Marketing and product teams can also use this central data location to build their real-time and personalized customer experiences without constantly having to request resources from data engineering teams.
Create a master schema for data elements: The schema defines exactly what fields exist within each table and what type of field it is (text, number, date/time, etc.). A master schema keeps consistency across tables by making sure every record includes certain fields while others are optional.
This quickly turns into an overwhelming project across silos in your organization. Be prepared to develop and update this schema overtime. And if you’re building the foundation of DataOps at your company, make sure to bring this into the design process early to avoid some costly data architecture mistakes.
Start small and expand your data infrastructure over time: Don't try to tackle everything at once. Start with a simple data stack, including only those data pipelines and workflows you absolutely need to run the business. Once you have the basics in place, start adding the nice-to-haves and exploring possibilities for growth.
With these data infrastructure guidelines in mind you can start to research which tools will be best for your company.
Tools for data infrastructure automation
Now that you understand what data infrastructure is and how to start building it, you need the right technology. Start by identifying your primary objective and goals and take inventory of current issues and constraints. What capabilities do you want the business to have? What data needs to go where?
Create a data flow diagram to visualize how information flows through your organization (both manually and electronically) to make sure you have everything covered. Then choose tools and design methods to fill any gaps by either implementing new technology or training existing staff. At the bare minimum, you’ll need some combination of data warehouse (or data lake), data ingestion tool, data transformation tool, and data orchestration platform.
Here are some of the data tools we recommend for building a data infrastructure that scales.
Snowflake Cloud Data Platform
Snowflake is a fully managed cloud data service that’s simple to use but powers a near-unlimited number of concurrent workloads. It’s your solution for data warehouses, data lakes, data engineering, data science, data application development, and securely sharing and consuming shared data. With Snowflake, you get the performance, flexibility, and near-infinite scalability to easily load, integrate, analyze, and share your data.
Fivetran Data Pipelines
Fivetran helps data engineers effortlessly centralize all your data so your team can deliver better insights faster. It helps you securely access and send all your data to one location to instantly connect hundreds of your most powerful databases and data sources.
dbt Cloud Data Transformation
dbt is a development framework that combines modular SQL with software engineering best practices to make data transformation reliable, fast, and fun. With dbt, data teams work directly within the warehouse to produce trusted datasets for reporting, ML modeling, and operational workflows.
Shipyard Data Orchestration
Shipyard integrates with Snowflake, Fivetran, and dbt Cloud to build error-proof data workflows in 10 minutes without relying on DevOps. It gives data engineers the tools to quickly launch, monitor, and share resilient data workflows and drive value from your data at record speeds (without the headaches). Hundreds of high-growth data teams use Shipyard to modernize their data workflows and build a solid data infrastructure that connects their data stack end-to-end (from day one).
Your individual requirements and business goals will determine which combination of these tools works best for your DataOps team. While you’re choosing your data tools, make sure they fit together to create an agile data infrastructure.
Tips for an agile and reliable data infrastructure
It’s important to consider how you can scale your data initiatives—as well as techniques you can use to minimize data loss and prevent downtime—when vendors go dark.
Here are some common best practices for building an agile and reliable data infrastructure that'll keep you up and running during times of peak usage (or when something goes wrong).
Set up continuous monitoring: This is one of the most basic steps to detect issues quickly and respond before they become a larger problem.
Keep Raw Data Around: Raw data helps you recreate the state of your data at any point in time. There are countless horror stories where data gets processed, deleted, and something was done wrong so the original data can never be restored. Storage is pretty cheap nowadays, so if you can help it, don’t delete it.
Automate workflows: Make sure to give your data engineers tools to automate data ingestion and data workflows. This helps with everything from data visualization for business intelligence to running automated security checks for data governance requirements. Data automation frees your data scientists to use their full skillset instead of wasting time pulling reports and manually updating data sources for the business.
When you design a redundant data infrastructure, your business can stay up and running through unexpected events. There’s a long list of benefits you get from investing in a solid data infrastructure.
Benefits of a solid data infrastructure
Everyone in your organization and each part of your business benefits from a solid data infrastructure. For DataOps, it means fewer manual tasks, more time focusing on what matters, and having all the right data where it’s needed. Marketing might use your infrastructure to personalize the messaging across the customer journey. Your executives might use real-time dashboards to make informed decisions.
Here are the main benefits that every organization will find with a solid data infrastructure in place:
Access critical business information quickly (or in real time): Executives pull reports during a meeting, marketing runs the numbers on their campaign, and your product teams understand how their features are received. Everyone in your business adds more value when they have the info they need at their fingertips.
Protect against ransomware attacks and security breaches: A solid data infrastructure is secure and keeps your data that way. Data workflows, data pipelines, data sources, and data warehouses are all protected at every access point.
Encourage innovation and collaboration: Teams across the globe work with shared datasets to get better project results and find new business opportunities.
Streamline compliance and data governance: Make it easier for financial services companies, public sector agencies, and other companies who handle sensitive information to meet data regulations and compliance standards.
Save money with more reliable systems and more efficient processes: Instead of scrambling to bring your company back online when it crashes under peak usage (Q4 shopping, holiday sales, PR campaigns, etc.), your reliable data infrastructure keeps business moving and saves money. Automating data workflows saves time and frees up data engineering resources to work on more valuable projects.
With a solid data infrastructure in place, you’ll find new benefits and opportunities every year. But you have to take the first step and start building.
How to get started with data infrastructure
A solid data infrastructure turns your raw data into organized datasets that your business uses to make strategic decisions, build new product features, and deliver personalized customer experiences. With the right data tools in place you can make data available in real time, secure sensitive data, and save your company from unnecessary downtime.
Shipyard helps you build a solid data infrastructure with data tools and integrations that work with your existing data stack (or modernize your existing legacy systems).
Sign up to demo the Shipyard app with the Developer plan that’s free forever (no credit card required). Immediately start to build data workflows (in 10 minutes or less), automate them, and see if Shipyard fits your business.
