What is data ingestion?

Data sources include financial systems, third-party data providers, social media platforms, IoT devices, SaaS apps, on-premises business applications such as enterprise resource planning (ERP) and customer relationship management (CRM).

These sources contain both structured and unstructured data, as well as semi-structured data. Once data is ingested, it can be routed to data lakes, data warehouses, data lakehouses, data marts, relational databases and other downstream applications.

Many data ingestion tools automate this process, collecting and moving raw data from multiple sources. Depending on the architecture, ingestion pipelines can also validate, filter, format or transform data as it moves and prepare it for use by data analytics software and other business applications.

Data ingestion can involve complex pipelines that typically require expertise in data science, data engineering and software development. The data might be sanitized and transformed into a uniform format by using an extract, transform and load (ETL) process or extract, load and transform process (ELT) to manage the data lifecycle effectively.

Data ingestion is the first step in processing data and extracting value from the large amount businesses collect today. A well-planned data ingestion process helps maintain the accuracy, consistency and reliability of data as it moves between systems, supporting data analysis, AI applications, operational workflows and other business processes. There are three key reasons why data ingestion is essential:

Data ingestion is the process of taking raw data from various sources and preparing it for analysis. The exact steps depend on the source, destination and ingestion method. A simple ingestion pipeline might capture and deliver data with little or no transformation, while a more complex pipeline can also discover, validate, filter, transform or enrich data along the way. Common steps in a pipeline include:

The exploratory phase where available data across the organization is identified. Understanding the data landscape, structure, quality and potential uses lays the groundwork for successful data ingestion.

Once the data sources are identified, data acquisition involves collecting the data. This step can include retrieving data from many sources. These sources include structured databases such as MySQL, application programming interfaces (APIs), file formats such as CSV and JSON and unstructured sources such as images and paper records. The complexity lies in handling the variety of data formats and potentially large volumes and safeguarding data integrity throughout the acquisition process.

After acquiring the data, validation helps identify errors and ensure consistency with set rules. Data is checked for errors, inconsistencies and missing values. The data is cleaned and made reliable and ready for further processing through various checks such as data type validation, range validation and uniqueness validation.

Here is where validated data is converted into a format suitable for analysis. It might involve normalization (removing redundancies), aggregation (summarizing data) and standardization (consistent formatting). The goal is to make the data easier to understand and analyze.

The final step places the transformed data into its designated location, such as a data warehouse or data lake, where it’s readily available for analysis and reporting. This loading process can be done in batches or in real-time, depending on the specific needs. Data loading signifies the completion of the data ingestion pipeline, where the data is prepped and ready for informed decision-making and generating valuable business intelligence.

Data quality is important to ingestion because errors, inconsistencies, incomplete records and other issues can affect outcomes. As part of the ingestion process, organizations often cleanse data by standardizing formats, removing inaccuracies, addressing missing or duplicate values and resolving other data quality issues. 

Data governance can help maintain data quality during ingestion by establishing policies and standards for data handling. It ensures that there is accountability through defined roles and responsibilities. It could also involve implementing metrics and monitoring systems to track and address issues, facilitating compliance with regulations such as GDPR or HIPAA and promoting consistency by standardizing data definitions and formats.

One of the benefits of data ingestion is that it can help make data from disparate sources more accessible and usable across the organization. Other benefits are as follows:

Data ingestion tools can simplify the often-complex task of collecting and moving data from various sources with diverse formats and structures. Some platforms can also validate, transform or standardize data as part of the pipeline.

An effective low latency data ingestion pipeline can handle large amounts of data at high speeds, including real-time ingestion. This way, organizations can process and act on rapidly changing information.

Businesses might reduce the time and resources traditionally required for manual data aggregation processes by automating data collection and cleansing through data ingestion. Also, as-a-service data ingestion solutions can offer further cost benefits by eliminating the need for upfront infrastructure investment.

A well-designed data ingestion process can help businesses scale as data volumes, sources and business requirements grow. Scalability enables organizations to support new workloads and use cases while continuing to access and analyze data effectively as their data landscape evolves.

Many modern data ingestion tools are designed to work across cloud, on-premises and hybrid environments, helping organizations move and integrate data where it is needed. Cloud-based ingestion services can reduce infrastructure management overhead and make it easier to scale as data volumes grow, while supporting access to data across distributed systems and teams.

Data ingestion encompasses various methods for bringing data from diverse sources into a designated system.

This ingestion method involves accumulating data over a specific period (daily sales reports, monthly financial statements) before processing it in its entirety. Batch processing is known for its simplicity, reliability and minimal impact on system performance, as it can be scheduled for off-peak hours. However, it’s not ideal for real-time applications.

This method ingests data as it is generated, making it available for processing and analysis with minimal delay. Real-time data ingestion can support faster decision-making and is well suited for time-sensitive applications such as fraud detection or stock trading platforms where quick responses to changing conditions are paramount.

CDC is an ingestion method that identifies and captures changes made to data in a source system, such as inserts, updates and deletions. It then delivers those changes to downstream systems. Because it transfers incremental changes rather than entire datasets, CDC can help reduce data movement and support near real-time data availability more efficiently than periodic full data loads.

Streaming ingestion and stream processing are closely related concepts. Streaming ingestion continuously captures and delivers data as it is generated. Stream processing continuously analyzes data as it arrives. Both real-time and stream processing can require significant computing power and network bandwidth resources.

API-based ingestion collects data from applications, services and platforms through application programming interfaces (APIs). This method is commonly used to ingest data from cloud applications, SaaS platforms, social media services and business systems. API-based ingestion can provide access to current data on demand, but factors such as rate limits, authentication requirements and API availability can affect ingestion performance and reliability.

The microbatching method strikes a balance between batch and real-time processing. It ingests data in small, frequent batches, providing near real-time updates without the resource constraints of full-scale real-time processing. Careful planning and management are necessary to optimize the tradeoff between data freshness and system performance.

Lambda architecture is a data processing architecture that uses separate batch and real-time processing layers. The batch layer processes complete historical datasets, while the streaming layer processes newly generated data as it arrives. A serving layer combines the outputs of both layers, providing a unified view of historical data and current data.

Data ingestion tools offer a range of solutions to cater to various needs and technical expertise.

Open source tools: Tools that provide free access to the software’s source code, giving users complete control and the ability to customize the tool. Apache Kafka, for example, is a widely used open source platform for streaming data ingestion and integration.

Proprietary tools: Solutions that are developed and licensed by software vendors, they offer prebuilt functions and varied pricing plans but might come with vendor lock-in and ongoing licensing costs.

Cloud-based tools: Ingestion tools delivered as cloud services, simplifying deployment and maintenance while offering scalability without the need for upfront infrastructure investment. Examples include managed services such as AWS Glue and Amazon Kinesis as well as cloud data platforms such as Snowflake, which provide capabilities for ingesting, processing and managing data at scale.

On-premises tools: These tools are installed and managed on a local or private cloud network, providing greater control over data security but requiring investment in hardware and ongoing IT support.

In balancing needs and expertise, several approaches exist for building data ingestion pipelines:

Hand-coded pipelines: These bespoke pipelines offer maximum control but require significant development expertise.

Prebuilt connector and transformation tools: This approach provides a easy way to build data pipelines by using preconfigured connectors and transformation capabilities. For example, IBM Confluent® offers tools and managed services that help organizations build and manage data ingestion and streaming pipelines.

Data integration platforms: These platforms provide a unified environment for tasks such as data ingestion, transformation, orchestration and movement, but might require development expertise for setup and maintenance.

While foundational for data pipelines, the data ingestion process is not without its complexities. Six common challenges include: 

Data security: Increased exposure elevates the risk of security breaches for sensitive data. Adhering to data security regulations adds complexity and cost.

Scale and variety: Performance bottlenecks can arise due to the ever-growing volume, velocity and variety of data.

Data ingestion sprawl and cost management: Fluctuations in data flows and growth in ingestion workloads can affect infrastructure requirements and operational costs, making cost monitoring and optimization important considerations.

Data fragmentation: Data that is spread across multiple applications, databases, platforms and other sources can make it difficult to create a unified view. Fragmented data might lead to inconsistencies, duplicate records and challenges in data integration and analysis.

Schema drift: Changes to the structure of source data, such as added, removed or modified fields, can create mismatches between source and target systems. If ingestion pipelines are not updated to accommodate these changes, schema drift can disrupt workflows and create other challenges.

Data quality assurance: The intricate nature of data ingestion processes can compromise data reliability.

Data ingestion, extract, transform, load (ETL) and extract, load, transform (ELT) serve a common goal—making data available and usable—but differ in their approaches.

Data ingestion refers to the broad process of collecting data from source systems and making it available in a destination environment for storage, processing or use. ETL and ELT are specific approaches that define how and when data is transformed as part of that process.

Extract, transform and load is the process of extracting data from source systems, transforming it to meet the requirements of the target system and then loading it into its destination.

Extract, load and transform (ELT) is an approach in which data is extracted from source systems and loaded into a target system before being transformed. Raw data is stored in the target environment and transformed as needed.

Data ingestion and data integration serve distinct purposes within the data pipeline.

Data ingestion is often one component of a broader data integration process. It acts as the entry point for data from one or more sources. The primary goal is to acquire and deliver data efficiently, though ingestion might also include tasks such as validation, filtering, formatting or quality checks.

The practice of combining and harmonizing data from multiple sources into a unified, coherent format. Data integration might involve data cleansing, standardization and enrichment to ensure consistency and accuracy across the entire dataset.

As organizations generate and consume more data, data ingestion plays an important role in ensuring that information can be accessed and used. Common use cases include:

Organizations use data ingestion to collect data from databases, applications, APIs, files and connected devices and make it available in cloud data lakes, data warehouses and data lakehouses. By bringing together information from multiple sources, organizations can create a more complete view of their operations, customers and business performance.

For example, retailers might ingest point-of-sale, e-commerce and inventory data to improve demand forecasting, while healthcare organizations might combine clinical, laboratory and patient-monitoring data to support care delivery and operational planning.

As organizations modernize their data infrastructure, data ingestion plays a key role in moving data from legacy systems, on-premises databases and data warehouses to cloud environments. Ingestion tools can automate large-scale migrations and synchronize data between systems during the transition.

Many platforms support change data capture (CDC), allowing organizations to continuously replicate updates from source systems so cloud-based environments remain current without requiring full data reloads. This approach can help financial institutions, government agencies and other organizations with large volumes of legacy data modernize their analytics environments while minimizing disruption to business operations.

Many organizations rely on data ingestion to support real-time analytics and event-driven applications. Ingestion tools can continuously collect and stream data from sources such as website clickstreams, application logs, social media feeds and IoT sensors.

By making this information available as events occur, organizations can monitor operations, detect emerging issues and respond more quickly to changing conditions. Telecommunications providers, for example, can use streaming data to monitor network performance and service quality. Meanwhile, manufacturers can analyze equipment telemetry to identify production bottlenecks and potential maintenance issues before they cause downtime.

Organizations can use data ingestion to make data available for data observability platforms, which help teams assess the reliability, freshness, completeness and consistency of data across systems.

By continuously ingesting data from operational databases, real-time streaming platforms and other sources, healthcare companies and other enterprises can build the foundation to identify anomalies, detect schema changes and track data movement across pipelines. This practice can also improve trust in the data used for analytics, reporting and AI applications.

AI and machine learning systems depend on timely access to large volumes of data. Data ingestion helps organizations collect, integrate and prepare data from multiple sources so it can be used for model training, inference and monitoring. It also supports generative AI applications, retrieval-augmented generation (RAG) systems and AI agents.

For example, banks might ingest transaction and account activity data to support fraud detection models and AI-powered customer service applications. Healthcare and life sciences organizations might combine clinical and research data to support predictive analytics or use imaging data to enhance AI-assisted decision-making. Organizations deploying RAG systems and AI agents can use data ingestion to continuously supply documents, knowledge bases and operational records that help retrieve relevant context and act on current information.

Businesses use data ingestion to collect customer information from websites, mobile applications, CRM systems, marketing platforms and customer support channels and make it available for analysis. Combining these data sources enables a more complete understanding of customer behavior and preferences. Businesses can use the resulting insights to personalize experiences, improve customer service, optimize marketing campaigns, support their sales force and identify opportunities for growth.

Data ingestion supports business intelligence (BI) and reporting by making data from multiple operational systems available for analysis and improving data availability. Organizations can use ingested data to create dashboards, track key performance indicators (KPIs), identify trends and support data-driven decision-making.

Whether monitoring sales performance, measuring supply chain efficiency or tracking financial results, organizations depend on reliable data ingestion pipelines to ensure that reports and analytics reflect current business conditions.