Data ingestion is the process of connecting to multiple data sources and transporting the data from each source into a single repository, typically a database, data warehouse, or data lake. Once the data is in the central repository, it can be accessed and analyzed by anyone in the organization with access rights. Data ingestion can occur in batches on a schedule, or it can occur in real-time with a steady flow of data from the source system into the central repository.
Although data ingestion is often used interchangeably with data integration, the two are not the same. Data ingestion imports the data in the new repository in its raw form. With data integration, the data is transformed as part of the process of moving it from the source system through an ETL (Extract, Transform, Load) process. In addition, in some architectures, integrating data means the data stays in the source systems but is accessible through a centralized application, like a search engine.
The Benefits of Data Ingestion
The most significant benefit of data ingestion is that you can get it into a central repository quickly because no transformation processes are necessary when you move it from the source system. Once it’s in the repository, it can be cleaned, ensuring it’s consistent and correct. At this point it can also go through any transformation processes necessary as part of a broader data pipeline.
Centralizing data is also key for analytics systems that look at all the data and derive common themes and insights.
For example, a customer data platform (CDP) ingests data from source systems such as marketing automation, CRM, ERP, web analytics, social media, and others. Once in the CDP, the data is cleansed by automating actions such as resolving identities, deduplicating profiles, resolving discrepancies between data, and discarding inaccurate data. The cleansed data is then available to analytics engines, including machine learning (ML) processes, and delivered back to external systems through data activation for campaigns and programs.
Challenges with Data Ingestion
Ensuring that data ingested into a central location is performed securely is critical, especially when it’s customer data or other proprietary and confidential company information. Proper data governance policies are essential to managing this process. The process of moving the data from source to destination must be secured. And once the data is in the new repository, it also needs to be adequately secured so that only the right analytics tools, systems, and people have access to it.
FAQ
What is the difference between data ingestion and data integration?
Data ingestion imports raw data from source systems into a central repository without transforming it, preserving the original format and structure. Data integration, on the other hand, involves transforming and harmonizing data as part of the transfer process through ETL (Extract, Transform, Load) pipelines. Ingestion prioritizes speed and simplicity, while integration focuses on making data immediately usable by standardizing formats and resolving inconsistencies during the move.
What is the difference between batch and real-time data ingestion?
Batch data ingestion collects and transfers data in scheduled intervals—hourly, daily, or weekly—making it efficient for large volumes of data that do not require immediate processing. Real-time data ingestion streams data continuously from source systems as events occur, enabling near-instant availability for analytics and activation. The choice between batch and real-time depends on your use case: real-time is essential for personalization and time-sensitive customer interactions (see real-time CDP), while batch is sufficient for reporting and historical analysis.
What types of data sources can be ingested into a CDP?
A CDP can ingest data from virtually any customer-facing system, including CRM platforms, marketing automation tools, web and mobile analytics, e-commerce platforms, point-of-sale systems, social media, customer support software, and third-party data providers. CDPs support both structured data (like transaction records and form submissions) and semi-structured or unstructured data (like JSON event logs and customer service transcripts). This broad ingestion capability is what enables CDPs to create comprehensive, unified customer profiles.
Related Terms
- Data Orchestration — Coordinates ingestion schedules and dependencies across sources
- Real-Time Data Processing — Handles streaming ingestion for low-latency use cases
- Data Validation — Checks ingested data for errors before downstream processing
- Data Aggregation — Summarizes ingested records for reporting and analysis