Many kinds of business—from machine learning startups to global e-commerce brands and enterprise software companies—face the challenge of collecting raw data from an overwhelming number of data sources and making it useful to the business.
Every business needs to aggregate data from a long list of sources, including: cloud apps and databases (Azure, AWS, etc.), IoT devices, website analytics, web servers, data lakes, social media platforms, customer data platforms, customer relationship management platforms, product information management systems—the list goes on and on.
Data ingestion automates the movement of data from almost any source to a target location that makes the raw data useful to your business.
For example, you need to build data ingestion pipelines to feed an omnichannel customer experience. As part of that, your marketing team is tasked with the ongoing challenge of delivering personalized content (that’s based on real-time behaviors) at just the right moment in the customer journey. Real-time data ingestion makes these kinds of initiatives possible.
Data ingestion takes unstructured data and makes it useful to: drive effective business decisions, create new customer experiences, improve machine learning outcomes, and connect data sources with data ingestion tools.
Here’s a 101-level introduction to what data ingestion is, the different types of data ingestion used to bring data sources together, and how a clearly defined data ingestion process (and the right tools) benefits your business.
What is data ingestion?
Data ingestion is how you collect, structure, and move data from many different data sources to different target locations or a central source of truth that makes the data useful to your business.
That could mean creating a data workflow to move all of your customer data from social media and website analytics into a customer data platform (CDP). It could also mean connecting data sources between Slack and Google Sheets to improve an Agile team’s development workflow by showing them their progress and accomplishments in new ways.
Whatever you need data ingestion for, it’s likely that your company has an overwhelming number of sources to pull from—including cloud applications, data warehouses, data lakes, SaaS apps, IoT devices, internal databases, social media analytics, and more.
Data ingestion tools, such as Fivetran, bring all of these datasets together and make it useful to the business. Data ingestion moves massive amounts of data (that only makes sense to machines) or just the right amount to be useful for your marketing teams.
A full discussion of data ingestion requires going into technical details—APIs, database languages, data security—so instead of outlining every detail we’ll dive into why it’s important for your business.
Why is data ingestion important?
Most companies have massive amounts of raw data, but they don’t know how to make it useful. You have real-time data available for everything that happens on your website. Great, but how does that data get into your analytics platform? That’s what data ingestion does—it moves raw data from one important source to a location you can use it.
Your company might want better business intelligence so that it can make sharper decisions or plan budgets more accurately for next year. You might need to move terabytes of IoT data into a data warehouse just to deliver what looks like a simple customer experience on the front-end. Data ingestion makes all of these things possible.
Data ingestion is just one facet of DataOps—a cooperative and process-oriented approach to data management. As a data engineer, your job is to ensure all your company’s data is structured, shared, secure, and usable—and getting it there isn't easy. The data ingestion process and data ingestion tools make it easier for you to launch, monitor, and share data workflows.
Here are some of the different types of data ingestion you can combine to make data useful to your company—from event-based to batch-based data ingestion.
Types of data ingestion
Some companies need real-time data ingestion for IoT devices 24/7. Other companies only need batch-based data updates every twelve hours to run their business well. Those are two drastically different types of data ingestion that require different resources.
It’s easy to think your company needs access to all the data flows from all the data sources all the time. But, the reality is likely more nuanced than that.
Big data that adds value to your business intelligence is about much more than having real-time data integration from every data warehouse, data lake, ETL platform, and data source you can imagine. That’s likely too much information to be useful for anyone and costs too much money to implement. It’s about creating automated data workflows tailored to get the right data to the right place at the right time.
For example, your marketing team might want event-driven data ingestion which occurs based on certain conditions being met—like any time a product is added to a shopping cart. Your merchandising team might only ingest data every Monday after 8:00 am, which is an easy data ingestion pipeline to automate. For both cases, a data engineer has to find the right combination of ingestion techniques.
Each type of data ingestion has its strengths and weaknesses, but usually all DataOps team members will want to implement a few different methods. Getting the data ingestion process right is another one of the ways you can improve DataOps at your company.
Here’s a quick overview of the different types of data ingestion.
Event-driven data ingestion
Items added to carts, email newsletter subscribes, certain navigation paths—there are countless reasons to automate data ingestion based on events. Instead of having real-time data streaming from your website, you could choose to only ingest customer data from an API when it’s a relevant use case and save some resources.
Your business operations also have important events that aren’t based on customer interactions—like product shipping, arrival, and receiving. Using event-based data ingestion is a great way to get specific data often, without requiring absurd amounts of storage.
Real-time data ingestion
IoT companies, self-driving cars, and AI-heavy services require real-time data ingestion workflows to function well—and to keep customers safe. When you’re dealing with thousands of messages or API calls per hour and need to know what’s happening at every moment, real-time data ingestion is for you.
It’s a resource-heavy method, but for the right products and services it’s worth the investment. For example, if you’re building a conversational-AI based on Google’s LaMBDA 2 language model (when it’s available), you’ll need a real-time data workflow to make the conversation as human as possible.
A more common use case involves streaming data from cloud applications built on AWS or Azure, your cloud data warehouse, or data lakes directly to your data analytics platform for marketing campaigns.
Batch-Based Data Ingestion
Batch-based data ingestion is just what it sounds like—a way to ingest data that works in batches. Instead of streaming live data from source to destination, batch-based ingestion processes all of your data in predetermined and scheduled batches.
The advantage? Batch-based ingestion typically reduces both time spent processing incoming data and overall storage requirements for most users. Because records are ingested together, there's less need for them to be saved individually.
Many companies think they want real-time ingestion but don't really need live, real-time information streams to accomplish their goals. Batch processing makes it easy to deal with large quantities of incoming data without completely overwhelming systems, which can result in cost savings or increased efficiency throughout an organization's operations.
Combined (Lambda) data ingestion
Combined (Lambda) data ingestion mixes real-time, batch-based, and event-based methods of data ingestion. This means you get all the data you want, when you want it, where you want it—instead of dealing with too much data, too little data, or data that arrives too late.
If you're building a customer-facing product or service on top of multiple data sources (including Azure and AWS data warehouses), it's essential to understand exactly what type of data ingestion technique is best for your needs. It’s highly likely you’ll use a version of combined data ingestion to meet everyone’s requirements.
Benefits of data ingestion
Every company will benefit from data ingestion in unique ways. You might finally fulfill your CEO’s business intelligence dream of having a single dashboard to show all of your operating metrics in real-time. Or, you could discover the opportunity for a new product based on data analysis that wasn’t possible before you included data from certain sources.
Here’s a short list of common benefits that companies experience when they implement data ingestion. These companies are able to:
- Deliver accurate data on time, every time
- Combine data from a variety of sources in an automated way
- Make decisions faster, collaborate better, utilize sharper business intelligence, and lower costs associated with storing data
- Create new sources of revenue and new products out of new use cases discovered in big data patterns
- Use data analytics tools more effectively, reduce costs by eliminating redundancies, and save time with automated data management
- Innovate more rapidly than ever before through self-service access to consolidated data
- Increase data security across the business by defining your data ingestion process and protecting your data ingestion pipeline
What’s the difference between data ingestion and ETL?
Many companies are familiar with ETL (extract, transform, load) and it’s important to know that data ingestion is slightly different. Your data ingestion strategy takes data from any number of sources, including ETL platforms and other data warehouses. You may still need to use ETL for one part of the business but that doesn’t mean it replaces the need for data ingestion across others.
Data ingestion challenges
Data ingestion is tricky because it involves a lot of variables—including latency between systems or other applications. For example, if a company wants to get inventory information from one database (Salesforce) but has their customer data in another (Oracle), then it may take longer to integrate customer data in Salesforce.
This creates issues as businesses try to make more informed decisions based on all of their data points, which is why having an effective data ingestion strategy is so important. You identify the most valuable opportunities to create data workflows from place to place and plan to create data ingestion streams based on priority over time.
Some of the most common data ingestion challenges you encounter may sound very familiar:
- Eliminating data silos
- Creating cross-functional workflows
- Legacy data sources (data warehouses, data lakes, unstructured data)
- Broken data schema
- Data integration
- Source data formatting
- Data quality
- Data integration
Ready to overcome these challenges and get your data into all the right places?
Start to build your data ingestion strategy
It helps to outline your approach to data ingestion instead of just starting to build workflows for every available data source. Take some time to work with the rest of your DataOps team to identify and outline your biggest revenue opportunities and most problematic pain points. Once you know what’s valuable to the business, you can prioritize your DataOps roadmap, find the right data ingestion tool, and get to work.
We designed Shipyard to give data engineers the tools to quickly launch, monitor, and share resilient data. You can sign up to demo the Shipyard app with the Developer plan that’s free forever (no credit card required). You can immediately build data workflows that ingest data, automate them, and see if Shipyard fits your use cases.
