What is a data ingestion pipeline?

What is a data ingestion pipeline?

Cole Lehman
Cole Lehman

Every business has to manage overwhelming amounts of data from multiple systems and sources. Building data ingestion pipelines is a crucial step to making all that raw data useful. Data ingestion moves data from sources to the destinations that need to use it — e.g. customer data moving from website analytics to Salesforce. You can have as many data ingestion pipelines as you need to automate data flows.

Data ingestion pipelines are one component of your whole data science pipeline. They can include ETL (Extract, Transform, Load), ELT (Extract, Load, Transform), or Reverse ETL processes to move data from place to place. And while the data transformation included in those processes is a crucial step to make sets of big data useful, it isn’t always a part of data ingestion.

Data ingestion is technically just the movement from source to destination and sometimes transformation happens separately — though that’s less and less common with today’s data pipeline tools.

Here’s everything you need to know about data ingestion pipelines, what you can use them for, and the tools you can use to build them.

What does data ingestion mean?

Data ingestion is the process of transferring data from a source (SaaS app, analytics app, etc.) to a destination (cloud data warehouse, data lake, etc.). As the scale and complexity of businesses increase, so do the number of data sources, destinations, and use cases for data ingestion.

Automating data ingestion makes it possible to manage higher volumes of data or make data available in real time. The data being ingested begins in different formats — everything from APIs to CSVs — and ends up moving to destinations with its original schema and values or goes through data transformation along the way.

What is a data ingestion pipeline?

A data ingestion pipeline is the technology that moves raw data from a wide variety of sources to destinations like cloud data warehouses. Data ingestion pipelines help data teams keep their data flowing from place to place so that data scientists, data analysts, and others can make a higher percentage of the data useful to the business.

Data ingestion pipelines also power business processes — from real-time metrics and data visualization to enhancing the capabilities of tools like Salesforce and Hubspot. While it’s not always the case, data ingestion pipelines typically include extract, transform, and load (ETL) steps. The exact step collects data from the source. The transform step cleans and processes the data.The load step loads the processed data into the data warehouse.

Your data ingestion pipeline is linked to data sources at one end and destinations at the other. Here are some of the most common data sources and destinations you’ll work with in:

Common data sources

Your data sources make up the first part of your data ingestion pipeline — everything from SaaS vendors to social media analytics. Data will come in CSVs from other business units and in raw, unstructured data from 3rd party sources. Here are some common data sources you can ingest from:

  • Website analytics
  • Email marketing software
  • CRM software
  • Social Media Platforms
  • Cloud storage
  • HTTP Clients
  • SFTP and FTP
  • Business File Management

Common data destinations

Your data ingestion pipeline could send data to an app or to a data warehouse for storage and modeling by Machine Learning (ML) algorithms. Data destinations vary depending on your goal for the pipeline. Here are some common destinations:

  • Cloud data warehouse
  • Data lake
  • Relational databases
  • Apache Kafka
  • Snowflake
  • Amazon S3
  • Databricks

Your DataOps team can build as many ingestion pipelines as you need to give your business accurate real-time data, reliable reporting, and enhanced decision-making tools.

What can a data ingestion pipeline do for your business?

For one, a data ingestion pipeline can help streamline and improve your whole approach to DataOps. These pipelines make it easier for data scientists, data engineers, ML specialists, and data analysts to do their jobs every day. That leads to a savings in time and frees up those resources to solve complicated business problems.

When you don’t need to go through DevOps to get data pipelines built, it’s much easier to increase velocity on all your data initiatives and projects. Here are some of the most common reasons businesses invest in data ingestion pipelines:

  • Faster data analysis and real-time metrics for business insights
  • Increased accuracy in executive decision making from real-time data visualization and dashboards
  • Automated data processes to save time in workflows
  • Easier Machine Learning (ML) and Artificial Intelligence (AI) workload access
  • Improved personalization capabilities for customer experience

Ultimately, your data ingestion pipeline is a layer of your whole data science pipeline — making it possible to start the process of making massive amounts of raw data useful to the business. So, how do you build your pipeline? You can do it the hard way or the easy way.

How do you create a data ingestion pipeline?

In the past, data engineers had to build their data ingestion pipelines from scratch. That required writing the code, developing and maintaining event messaging, handling data transformation, and automating the whole pipeline from start to finish. That’s the hard way.

Today, there is a whole list of data tools that have all the code ready for you. The pipeline is built. You just have to connect one end to a source and the other end to a destination. For example, Shipyard makes it easy for someone on your data analytics team to set up a pipeline in 10 minutes.

Even though data ingestion pipelines are easier to build than they ever have been, it’s still important to know some of what goes on inside of them.

What are the major processes in a data ingestion pipeline?

From basic ingestion to streaming ingestion and data monitoring — your data ingestion pipelines are made of more than just a simple tube from one place to place. You might find that you need some custom steps in your pipelines, but these are some of the most common processes you’ll find in data ingestion.

Ingestion: This is where the data extraction occurs. The ingestion components of your data pipeline read the data from your sources — e.g. an API. You’ll have to run through a process called data profiling so that you make sure you’re extracting the right data. Once you have it outlined, you ingest the data you want from all sources without worrying about ingesting unwanted and useless data.

Batch ingestion: This is a more complex form of ingestion that operates on groups of datasets. Batch ingestion runs on a schedule (or specified triggers) and adheres to strict criteria set by your data team. It doesn’t run in real-time and can extract large amounts of data from multiple sources at once.

Streaming ingestion: Streaming ingestion is when data sources continuously send along new records or units of information as the data is changed. Enterprise data that’s critical to business operations can be worth the resources this requires. Streaming ingestion is ideal for when you need real-time data for low-latency business apps and analytics.

Monitoring and Alerting: Whenever you ingest data it’s critical to monitor for errors to fix them before they cause damage to your business. Set up automatic retries when ingestion processes fail the first time and get detailed visibility into all your data ingestion pipelines.

Data Transformation: Data transformation is the process of converting data from one format or structure to another. It involves changing your data values (and even foundational schema) to match the requirements of the data warehouse or tool your data is moving into next.

Where the transformation steps happen in your DataOps workflows depends on your unique set of data tools and technology. A modern data stack will likely have data transformation happening in multiple locations and in different directions, using ETL, ELT, or even reverse ETL. Some of your data will need to be transformed for storage in a cloud-based data warehouse and some will need to be transformed for use in customer-facing business apps.

Thankfully there are data ingestion tools that perform all of these processes and more. You just have to find the ones that fill in your data infrastructure needs and start to build your data ingestion pipelines.

Tools for data ingestion pipelines

All of these tools make it unnecessary for your data engineers to hand-code data ingestion pipelines. From streaming ingestion to data transformation, you can find a SaaS solution that fills in your modern data stack and makes building data ingestion pipelines easy and effective.

dbt Cloud data transformation

You’ll have to take care of data transformation somewhere along the data ingestion pipeline.

dbt enables data teams to work directly within data warehouses to produce accurate and trusted data sets for reporting, machine learning (ML) modeling, and operational workflows. It’s a crucial tool for preparing your data to be modeled and validated. And it combines modular SQL with software engineering best practices to make data transformation reliable, fast, and easy.

Snowflake cloud data platform

Having a central cloud storage solution for data ingestion is a critical part of building pipelines.

Snowflake is a powerful solution for data warehouses, data lakes, data application development, and securely sharing and consuming data. It’s a fully managed cloud service that’s simple to use and gives your data analytics team the performance, flexibility, and near-infinite scalability to easily load, integrate, analyze, and share data.

Fivetran ETL

Fivetran delivers simple, extensible ETL built specifically for data teams. It helps you put analysis-ready data into your data ingestion pipeline. With Fivetran, you can extract data from the sources that matter, load it into leading data platforms, and analyze it with effective data analysis tools. From there your machine learning algorithms can take over and find the patterns you need to solve business problems.

Shipyard data orchestration

Shipyard integrates with dbt Cloud, Snowflake, Fivetran, and many more to build error-proof data workflows in 10 minutes without relying on DevOps. It gives your data engineers the ability to quickly launch, monitor, and share resilient data workflows and drive value from your data at record speeds. This makes it easy to build a web of data workflows to feed your data ingestion pipeline with many datasets. Shipyard gives your data team the ability to connect their data stacks from end to end.

Start building your data ingestion pipeline

Identify the data you want to gather and move to a new destination for use. Then it’s as simple as giving your data engineers a tool to build a pipeline between the source and destination. Shipyard gives you data ingestion pipeline tools and integrations that work with your existing data stack or modernize your legacy systems.

Sign up to demo the Shipyard app with our free Developer plan — no credit card required. Start to build data workflows in 10 minutes or fewer, automate them, and see if Shipyard is what you need to build your data science pipeline.