What Is Data Ingestion? Full Guide 2025

Globally and across all types, the volume of data created, captured, copied and consumed is growing at an annual rate of 19.2% — and it’s showing no signs of slowing. Modern enterprises need access to the most timely information available, and they need to be sure that the data is accurate, relevant and complete.

Organizations that manage the data ingestion process efficiently have a distinct competitive advantage. They can respond more quickly to changes in customer behavior, bring products to market faster, develop new revenue streams, comply with regulatory restrictions and be less exposed to sudden shifts in circumstances.

Enterprises that fail to ingest data properly develop blind spots, resulting in missed opportunities and increased risk. Poor ingestion practices may also lead to missing records, duplicated data, inconsistencies, compliance failures, security breaches and transformation errors that propagate throughout the entire ecosystem, undermining user confidence and decision-making accuracy.

A third form of data ingestion is a hybrid of the previous two. It involves collecting data continuously but processing it in small batches at regular intervals, minutes or even seconds apart. This approach makes fewer demands on system infrastructure than real-time processing while reducing the lag between collecting data and taking action. For example, an ecommerce site might employ micro-batch ingestion to make recommendations to visitors based on the contents of their shopping cart, or a regional medical center might use it to determine the availability of hospital beds during a public health crisis.

A single organization may employ different forms of data ingestion for different applications — batch processing for daily reporting, real time for fraud detection and a hybrid approach for communicating with web or mobile customers, for example. The choice of ingestion method will depend on factors such as data volume, latency requirements, infrastructure costs, technical complexity and how critical the data is to the business.

Data ingestion performs an essential step in the process of creating data warehouses and data lakes by delivering information to a staging area, where it can be extracted, transformed and loaded (ETL) into the warehouse or lake. Thus, data ingestion is complementary to the ETL or extract-load-transfer (ELT) process.

Imagine this process as analogous to how a restaurant works. Data ingestion platforms are like the trucks that deliver raw ingredients (data) to the kitchen. ETL/ELT operations clean, chop and season those ingredients, then pass them onto the chef — the data warehouse or data lake. Diners submit their queries (orders) to the chef, which responds by mixing and matching those ingredients into the answers they're looking for (their meal).

In some cases, data warehouses and lakes can accept data directly without the need for ETL or ELT; in this setting, the data ingestion platform acts more like the drive-through window at a fast food restaurant. Examples include point of sale or trading systems where the formats never vary and the data doesn't need to be transformed, or equipment sensors where the data is consistent and needs to be acted upon quickly.

A streamlined data ingestion process can scale as the volume of data grows without requiring significant upgrades or rebuilds.

Automation can enhance the data ingestion workflow by orchestrating when data is ingested, triggering downstream processes after data has been validated, and dynamically scaling resources as demand increases. Following best practices, such as adopting consistent data formats and breaking ingestion pipelines into discrete components, can help create self-managing systems that run with minimal human intervention.

Keeping information safe and secure is paramount for organizations. They may need to encrypt sensitive data in transit and at rest, implement strong access controls at every stage and maintain audit trails. Publicly held companies and enterprises handling highly regulated financial, health or personal data must follow specific rules for storing and protecting that data, adding to overall complexity.

Informatica's data ingestion platform centers around its Intelligent Cloud Services, which allow enterprises to exchange data between on-premises and cloud-based applications. It's designed for enterprise-scale deployments with strong governance, security, and compliance capabilities, and is commonly used for enterprise data warehouses, master data management, and large-scale data migration projects.