Data ingestion is the first step in every data pipeline. It refers to the process of collecting data from source systems and moving it into a warehouse, lake, or analytics platform where it can be queried.

When data ingestion fails, you get stale dashboards, broken reconciliations, and machine learning models trained on incomplete data. Worse still, bad data can affect your bottom line. Over 25% of organizations reported that they lose $5 million USD or more annually to poor data quality.

Below, we’ll take a closer look at what data ingestion is, the main data ingestion patterns, the use cases that drive most pipeline investment, and the challenges that teams face.

Highlights

• Data ingestion moves data from source systems into a destination where it can be stored and queried. The pattern a business chooses determines how fresh that data is.

• Reliable ingestion depends on two things: completeness (i.e., all the records that should be there are there) and timeliness (i.e., the data lands before the first person needs it).

• A modern payment provider can sync data directly to destinations such as Snowflake, Redshift, and Amazon S3. This gives businesses access to their full transaction history without custom engineering or third-party connector vendors.

Data ingestion is the process of pulling data from source systems and loading it into a destination where it can be stored, queried, and used. It feeds data into warehouses, data lakes, and analytics platforms.

In a payments context, data ingestion can involve collecting data from disparate sources, including point-of-sale (POS) systems, ecommerce websites, and payment gateways.

What are the main types of data ingestion?

How fresh your data needs to be—and how stale data can be before it stops being useful—determines what kind of data ingestion method is best for your business.

These are the main methods to consider.

Batch ingestion

Batch ingestion pulls data on a schedule and moves it in bulk. Latency is hours or days, which is fine for many workloads. Finance closes, weekly executive reports, and historical trend analysis (e.g., churn analysis) can typically all use this kind of data.

Streaming ingestion

Streaming ingestion processes events as they’re produced, which drops latency to seconds or less. The infrastructure is more demanding—you’re typically working with systems such as Apache Kafka or cloud-native equivalents—and your consumer applications need to handle out-of-order events and at-least-once delivery. It’s generally the right call when the value of data requires fraud signals, live inventory, and real-time personalization.

Change data capture

Change data capture (CDC) reads a source database’s transaction log and emits only what changed, landing in the minutes-latency range without the overhead of repeated full-table reads. It sits between batch and streaming in both complexity and freshness, and it’s particularly useful when you need near real time (NRT) accuracy from a relational system.

What are common data ingestion use cases?

Data ingestion exists to serve some process downstream. The pattern you choose depends heavily on how the data is used.

Here are the common data ingestion use cases:

• Business intelligence (BI) reporting: Revenue, conversion, churn, and support volume feed dashboards that teams check daily. Ingestion freshness determines how current that data is.

• Financial reporting: Month-end and quarter-end closes depend on complete, accurate transaction data landing in a warehouse where finance can run their queries. Completeness matters as much as freshness here.

• Customer and product analytics: Behavioral event data combined with customer relationship management (CRM) and transaction data gives product and growth teams the full picture. Ingestion is what connects those source systems and makes the combined dataset able to be queried.

• Fraud monitoring: A decision made on data that’s 12 hours old is often a decision made on irrelevant data. Fraud detection is one of the cases where streaming or near real time CDC is worth the added effort.

• Machine learning: Training pipelines need historical data in bulk; inference pipelines need fresh features. Ingestion serves both: batch ingestion can be used for training sets and lower-latency patterns can be used for feature stores.

What does good data ingestion look like?

When data arrives both complete and on schedule, analysts can stop doubting their numbers and running reconciliation checks before every report.

Good data ingestion promises completeness. All the records that should be there are there. A well-designed ingestion layer handles deduplicating, backfilling gaps, and catching late-arriving records before they become reporting errors.

The data also arrives when it’s needed. That doesn’t always mean as fast as possible: it means the data lands before the user needs it.

What are the main challenges for data ingestion?

Ingestion looks straightforward until you’re running it across multiple source systems. The following data ingestion challenges consistently cause trouble.

Security and data exposure

Moving sensitive data (e.g., financial transactions, personally identifiable information, payment records) through ingestion infrastructure creates exposure at every hop. Teams that route payment data through a third-party extract, transform, load (ETL) connector are giving that vendor access to their full transaction history. Whether that’s acceptable depends on vendor contracts, compliance requirements, and risk tolerance.

Scale

Volume compounds over time. Schema changes in source systems can break pipelines in ways that don’t always surface immediately. Partitioning strategies, incremental loads, and schema evolution handling are engineering problems that need consideration before they become incidents.

Fragmentation

In many organizations, ingestion is unorganized rather than a system that was designed intentionally. Let’s say the data team built a connector for Salesforce, while engineering built a separate one for the production database, and finance has a comma-separated values (CSV) export that someone uploads every Monday. The result is duplicate, inconsistent data pipelines that are hard to monitor and harder to trust.

Quality failures

Pipelines sometimes break in an obvious way: a job errors out, or a dashboard goes blank. But failures can be hidden as well. For instance, a schema change upstream might drop a column, and then downstream tables start missing data, or an application programming interface (API) rate limit might cause partial loads that look complete. Without monitoring that checks row counts, value ranges, and referential integrity, you won’t know until something breaks badly enough to get noticed.

How does data ingestion differ from ETL and ELT?

Data ingestion, ETL, and ELT describe overlapping parts of the same pipeline, but they mean different things.

• Data ingestion: This is the act of moving data from a source into a target system. It’s about transport and delivery. It doesn’t consider whether the data changes in transit.
  
• Extract, transform, load (ETL): This is an architecture where data is extracted from the source, transformed in the middle—historically in a dedicated transformation tool or staging server—and loaded into the destination in its final, query-ready form. The transformation happens before the data arrives.
  
• Extract, load, transform (ELT): This is that architecture but with the last two steps reversed. Raw data lands in the warehouse first, and transformation happens there using structured query language (SQL) or tools such as the data build tool (dbt). This became practical as cloud warehouses became cheap and powerful enough to run heavy transformations at scale, and it’s now the dominant pattern for modern data stacks.
  

How does a payment provider help with data ingestion?

Stripe Data Pipeline is a direct sync from Stripe to your warehouse or cloud storage destination without an intermediary. It’s available to existing Stripe users and connects to destinations such as Snowflake, Redshift, and Databricks. Setup doesn’t require writing code or configuring connectors.

Here’s how Stripe Data Pipeline helps with data ingestion:

• Data freshness: Syncs run continuously, with most data available within a few hours of the underlying event.

• Historical data: When you connect, you get access to your full Stripe history, rather than just data from the connection date forward.

• Data completeness: Stripe Data Pipeline includes prebuilt financial reports, such as payouts reconciliation and balance summary, along with curated datasets for common use cases such as monthly recurring revenue (MRR) and fraud analysis. Third-party vendors cannot sync these data sources, and require manual exports or reconstructing data.

• Reduced vendor exposure: Because the sync is direct from Stripe to your warehouse, your payments data doesn’t pass through a third-party vendor’s infrastructure.