Data Ingestion
What is Data Ingestion?
Before data can be processed or analyzed, it must be ingested by an application program, data integration platform or database management system. All applications operate in three phases: data ingestion, processing, and output.
Data Ingestion in Data Warehousing and Data Science
Data warehouses and machine learning perform data analysis using data that must be extracted from one or more source systems. Getting the data to the analytics database uses data preparation and ETL processes. Data preparation pipelines ingest data before moving it to target analytic systems. Similarly, ETL, which stands for Extraction, Transformation and Load processing, includes data ingestion when extracting data from source data systems and loading transformed data into an analytics database.
Examples of Data Ingestion
Ingestion of Parameters by Application Programs
Application programs, functions and microservices get data passed to them when invoked or called. The SUM function may have a string of numbers passed to it, which it adds together to return a total value. More modern application programming interfaces (APIs) employed by web applications can be interrogated to ease data ingestion. JSON and XML allow variable numbers of elements to be passed along with a declared delimiter string.
Data Entry
Data can be validated as humans enter it in forms before an application program accepts it. Manual data entry is commonly used today to collect survey data, for careers to record medical data and for online forms.
Ingesting Transaction Records
ERP systems such as Oracle and SAP create journal records to record transactions. Batch systems ingest this data to summarize daily transactions for reporting and end-of-day reconciliation.
Log Data
IT systems like websites record visits by logging URLs and cookie data. Marketing and Sales automation systems such as HubSpot ingest this data and use it to map these URLs to corporations and match cookie data to existing prospect lists.
Cloud-Based Data Ingestion
Cloud-based storage such as AWS S3 buckets emulate on-prem operating system file access paradigms and present familiar APIs so applications can transparently ingest cloud data as if it were locally resident.
Real-Time Data
Gaming and stock trading systems tend to bypass file stem APIs, preferring to ingest data directly from streamed in-memory message queues.
Ingesting Database Records
Database systems operate by accepting and parsing queries written in SQL or using key values and returning a result set of records that match the selection criteria. Records are then processed one at a time by the calling application.
Loading Data into a Database
Most database vendors provide fast loaders to bulk load data using multiple parallel streams or bypassing SQL to get the best throughput.
Streaming Data Ingestion
A popular alternative to traditional file-based data ingestions is streaming data sources such as AWS SNS, IBM MQ, Apache Flink and Kafka. As new records are created, they are immediately made available to applications that subscribe to the data stream.
Edge Data Ingestion
IoT devices generate masses of data that would overwhelm corporate networks and central server capacity. Gateway or edge servers ingest sensor data, for example, discard the less interesting data, and compress the interesting data before transmitting it to central servers. This is a form of pre-ingestion to optimize resource utilization and increase data throughput over busy networks.
Actian and Data Ingestion
Actian Data Intelligence Platform is purpose-built to help organizations unify, manage, and understand their data across hybrid environments. It brings together metadata management, governance, lineage, quality monitoring, and automation in a single platform. This enables teams to see where data comes from, how it’s used, and whether it meets internal and external requirements.
Through its centralized interface, Actian supports real-time insight into data structures and flows, making it easier to apply policies, resolve issues, and collaborate across departments. The platform also helps connect data to business context, enabling teams to use data more effectively and responsibly. Actian’s platform is designed to scale with evolving data ecosystems, supporting consistent, intelligent, and secure data use across the enterprise. Request your personalized demo.
FAQ
Data ingestion is the process of collecting data from various sources and moving it into a storage system, database, data lake, or analytics platform for processing and analysis.
The two primary methods are batch ingestion, which moves data in scheduled intervals, and streaming ingestion, which moves data continuously in real time as new events occur.
Sources include databases, SaaS applications, APIs, IoT devices, log files, event streams, on-prem systems, cloud platforms, and change data capture (CDC) outputs.
Reliable ingestion ensures that downstream analytics, dashboards, and machine learning models receive accurate, timely data. It enables real-time insights, reduces latency, and supports scalable data engineering architectures.
Common tools include Apache Kafka, Apache NiFi, Amazon Kinesis, Google Pub/Sub, Fivetran, Airbyte, streaming ETL systems, and CDC frameworks that capture database change events.
Challenges include handling high data volumes, schema drift, data quality issues, real-time scalability, maintaining consistency across distributed systems, and ensuring secure, compliant movement of sensitive data.
