What is data ingestion? A complete guide for data cravers and finance leaders

Table of content

• What is data ingestion?
• Real-time vs. batch data ingestion in accounting and finance
• Key components of data ingestion
• 8 Data ingestion tools - A deep dive into finance applications
• Conclusion: The power of efficient data ingestion

What is data ingestion?

Data ingestion is the process of collecting and importing data from various sources into a centralized system for storage, processing, and analysis.

This process is critical for businesses that rely on large datasets to drive decision-making, enhance operations, and gain competitive insights.

Organizations collect data from multiple sources, such as databases, APIs, IoT devices, and third-party platforms. However, this data is often unstructured, spread across different formats, and resides in multiple locations. Data ingestion helps streamline and automate the process of gathering and preparing this data, ensuring it is available for analytics, machine learning, and business intelligence applications.

Data ingestion can be performed in real-time, where data is continuously streamed and processed as it arrives, or in batches, where data is collected and transferred at scheduled intervals. The choice of ingestion method depends on the business’s specific needs, including data volume, processing speed, and the level of accuracy required.

This foundational step in data management is essential for creating reliable, scalable, and efficient data pipelines. 

Real-time vs. batch data ingestion in accounting and finance

There are various types of data ingestion, yet, as mentioned before, we can identify two main methods: real-time ingestion and batch ingestion. 

These two methodologies can impact efficiency and acuracy, so it’s important to distinguish the level of applications and benefits involved in them.

Also, they’re both  fundamental in financial and accounting systems, as data ingestion enables organizations to process transactions, analyze financial reports, and ensure regulatory compliance.

Let’s explore the differences between them and examine how they apply in an accounting/finance business scenario.

Real-time data ingestion: instant processing for immediate insights

Real-time data ingestion involves continuously collecting, processing, and integrating data as soon as it is generated. This approach ensures minimal latency, making it ideal for scenarios where up-to-the-minute accuracy is required.

Key benefits

• Immediate insights for critical decision-making
• Enhanced fraud prevention and compliance monitoring
• Faster response to customer transactions

Example of real-time data ingestion in accounting and finance

A fraud detection system in a bank monitors transactions in real time, checking for suspicious activity such as unusual spending patterns or transactions from high-risk locations.

• When a customer swipes their credit card in a foreign country, the transaction data is immediately ingested into the bank’s fraud detection system.
• AI algorithms analyze the transaction in milliseconds, comparing it to the customer’s historical spending behavior.
• If the system detects an anomaly, it triggers an automatic alert to the bank and potentially freezes the account to prevent unauthorized activity.
• The customer receives a real-time notification requesting verification.

This instantaneous data ingestion and analysis reduce the risk of fraud, ensuring financial security for customers and compliance with anti-money laundering (AML) regulations.

Other use cases in finance/accounting

• Stock trading platforms – real-time data ingestion ensures that financial markets receive immediate price updates, enabling traders to make split-second decisions.
• Automated invoice processing – when an invoice is received via email, an AI-based OCR tool like Procys extracts relevant data in real time, updating the accounting system without human intervention.
• Loan approval systems – lenders assess applicants’ financial history instantly to approve or reject loan applications within seconds.

Batch data ingestion: structured and scheduled processing

Batch ingestion processes data at predefined intervals, such as hourly, daily, or weekly. It is suitable for handling large volumes of data that do not require immediate processing.

Key benefits of batch ingestion

• More efficient for processing large datasets
• Reduces system overload by running during non-peak hours
• Ensures structured, validated data for compliance

Example of batch data ingestion in accounting and finance

Let’s consider a corporate tax reporting system that consolidates financial data from multiple departments at the end of each business day.

• Throughout the day, financial transactions occur across different departments (e.g., sales, payroll, accounts payable).
• Instead of updating records in real time, the system collects and stores these transactions in temporary data storage.
• At midnight, the system ingests all transactions in a batch process, compiling financial statements and tax reports.
• Accountants receive a consolidated report the next morning, ready for auditing and tax filing.

This approach ensures data integrity and accuracy, particularly for regulatory reporting, where companies must verify all transactions before submission.

Other use cases in finance/accounting

• Month-end financial reconciliation – at the end of each month, companies process all invoices, expenses, and revenues in a batch to close financial statements.
• Payroll processing – employee salaries, tax deductions, and benefits are typically calculated and processed in a batch at the end of a payroll cycle.

Annual auditing and compliance checks – regulatory bodies require companies to submit financial data periodically, making batch processing an efficient method for aggregating and validating reports.

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Key components of data ingestion

Data ingestion is a multi-step process that involves various components: each one plays a role in ensuring data integrity, speed, and accuracy. 

We can distinguish among:

1. Data sources
2. Ingestion layers
3. Processing and transformation
4. Storage
5. Monitoring and security
6. Integration

1. Data sources: where the data comes from

The first step in data ingestion is identifying and connecting to data sources. These can be structured, semi-structured, or unstructured sources, depending on the business use case.

Examples in finance and accounting:

• ERP systems (SAP, Oracle) – provide transaction data, financial reports, and sales data.
• Accounting software (QuickBooks, Xero, FreshBooks) – supplies invoices, payment records, and expense reports.
• Banking APIs – fetch real-time transaction data for fraud detection and financial analysis.
• Emails and PDFs – extract invoice and receipt data using OCR tools like Procys.

Ensuring that the data sources are reliable and well-integrated is the first step and primary component of the process, as it reduces errors and improves efficiency.

2. Data ingestion layer: collecting and transferring data

The ingestion layer is responsible for fetching data from sources and sending it to a storage or processing system. This can be done in real-time or batch mode, depending on the use case.

Common ingestion methods:

• ETL (Extract, Transform, Load) – data is extracted, transformed, and then loaded into a data warehouse (e.g., for tax reporting).
• ELT (Extract, Load, Transform) – data is first loaded into storage and transformed later (useful for big data analytics).
• Streaming ingestion – uses real-time pipelines for instant transaction monitoring.

Selecting the right ingestion method ensures data is transferred efficiently, whether in real-time for stock trading or in batches for payroll processing.

3. Data processing and transformation: making data usable

Raw data is often incomplete, inconsistent, or unstructured, making it difficult to analyze. The processing layer cleans, transforms, and structures the data before storing it.

How processing helps in finance and accounting:

• Currency conversions – standardizing multi-currency transactions for global financial reports.
• Data deduplication – removing duplicate transactions to prevent accounting errors.
• Format standardization – converting different invoice formats into a common structure.
• Validation checks – ensuring transactions comply with regulations such as SOX and GDPR.

Cleaning and standardizing data prevents errors in financial statements, compliance reports, and audit logs.

4. Data storage: where data is kept for analysis

Once ingested and processed, data must be stored in a scalable and secure system for analysis and reporting.

Common storage solutions

• Data warehouses (Google BigQuery, Snowflake), which store structured financial data for long-term analysis.
• Data lakes (Amazon S3, Azure Data Lake), meant to hold large volumes of structured and unstructured data.
• Cloud storage (Google Drive, OneDrive), which we can use to store invoice PDFs and bank statements.

Choosing the right storage ensures compliance, scalability, and fast retrieval of data for financial forecasting.

5. Data monitoring and security: ensuring compliance and integrity

Data ingestion must adhere to security regulations and data governance policies, especially in finance and accounting, where sensitive financial records are processed.

Key security and monitoring aspects

• Access control – limiting data access to authorized users only.
• Encryption – protecting sensitive data in transit and at rest.
• Regulatory compliance – ensuring data meets standards such as GDPR, SOX, and PCI DSS.
• Real-time monitoring – detecting anomalies, such as unauthorized data access or missing transactions.

Security breaches or compliance failures can lead to financial penalties and reputational damage.

6. Data integration: enabling seamless connectivity

To maximize its value, ingested data must be integrated with other business systems for analysis, reporting, and automation.

Examples of financial data integration

• Connecting an accounting tool like Microsoft Business Central to Procys, AI-powered OCR tool to automate invoice processing.
• Syncing transaction data with ERP systems for automated reconciliation.
• Integrating data sets with cloud storages to access it from a centralized point of control.

Integration enhances operational efficiency, reporting accuracy, and decision-making.

8 Data ingestion tools - A deep dive into finance applications

Procys – AI-powered OCR and document automation

Best for

Automated document ingestion, OCR-based data extraction, and seamless integration with accounting and ERP systems.

Why Procys for data ingestion

Procys is an AI-driven document automation and OCR-based data extraction platform designed for finance, accounting, and administrative workflows. Unlike traditional ingestion tools that focus on database or API connections, Procys specializes in extracting structured data from invoices, receipts, contracts, and financial documents, integrating them seamlessly into accounting systems.

Key features

• Automated document ingestion, to extract data from PDFs, emails, scanned invoices, and receipts.
• AI-powered OCR (Optical Character Recognition), which converts unstructured text into machine-readable data.
• Seamless integrations, to connect with other accounting and ERP systems.
• Batch and real-time processing for bulk document uploads and real-time ingestion for on-the-fly document capture.
• Smart validation and compliance, ensuring data accuracy, reducing errors in financial records and guaranteeing compliance with GDPR, ISO certifications and other regulations. 

Quick use case in finance

A CFO at a mid-sized accounting firm can use Procys to automatically ingest and extract data from hundreds of client invoices daily, syncing it directly into QuickBooks and Microsoft Dynamics CRM, eliminating manual entry and reducing processing time by over 80%.

Apache Kafka

Apache Kafka is an open-source event streaming platform designed for high-throughput, real-time data ingestion. It allows businesses to process continuous data streams with minimal latency.

Best for

Real-time data streaming and event-driven architectures.

Key features

• Supports high-speed real-time data ingestion
• Distributed and scalable architecture
• Can handle millions of events per second
• Integrates with banking systems, fraud detection tools, and stock trading platforms

AWS Glue

AWS Glue is a managed cloud-based ETL service that automates data extraction, transformation, and loading into data lakes or warehouses. It is widely used for batch processing but also supports streaming ingestion.

Best for

Serverless ETL and data integration.

Key features

• Supports batch and micro-batch ingestion
• Automates schema discovery and transformation
• Integrates with Amazon S3, Redshift, and Athena
• No infrastructure management required

Google Cloud Dataflow

Google Cloud Dataflow is a cloud-based tool built on Apache Beam that supports real-time and batch ingestion for large-scale data pipelines.

Best for

Real-time and batch data processing with a serverless architecture.

Key features

• Fully managed and serverless
• Handles real-time and batch ingestion efficiently
• Ideal for fraud detection and risk management systems
• Scales automatically based on workload

Fivetran

Fivetran is a fully managed data ingestion tool designed to sync data from various sources into a data warehouse. It requires minimal setup and maintenance.

Best for

Automated data pipeline management.

Key features

• Supports batch data ingestion
• Prebuilt connectors for over 300+ data sources
• Automatic schema updates
• Works with QuickBooks, SAP, and NetSuite

Talend

Talend is a versatile data ingestion and ETL tool that supports real-time, batch, and cloud-based data ingestion. It is widely used in financial reporting and regulatory compliance.

Best for

Open-source and enterprise data integration.

Key features

• Drag-and-drop interface for designing data pipelines
• Real-time, batch, and micro-batch ingestion capabilities
• Strong security and compliance features (GDPR, SOX)
• Works with ERP, CRM, and cloud storage systems

Airbyte

Airbyte is an open-source alternative to commercial ELT tools like Fivetran. It allows businesses to ingest and replicate data from various sources into cloud data warehouses.

Best for

Open-source ELT with strong integration capabilities.

Key features

• Supports batch and micro-batch ingestion
• 300+ prebuilt connectors for cloud services and databases
• Customizable and open-source
• Works well with Google BigQuery, Snowflake, and Redshift

IBM DataStage

IBM DataStage is an enterprise-grade ETL solution that provides robust data ingestion, transformation, and governance capabilities. It is commonly used in regulated industries like banking and insurance.

Best for

Enterprise-grade ETL and data transformation

Key features

• Batch, real-time, and micro-batch ingestion support
• Enterprise-grade data security and compliance features
• Scalable architecture for handling large datasets
• Integrates with IBM Cloud, Oracle, and SAP

Conclusion: the power of efficient data ingestion

Data ingestion is the backbone of modern financial and business operations, enabling organizations to collect, process, and analyze data from multiple sources efficiently.

Whether through batch processing for structured financial reports, real-time ingestion for fraud detection, or hybrid approaches for optimized workflows, businesses that implement robust data ingestion strategies gain a competitive advantage.

By leveraging the right tools, companies can reduce manual workload, improve data accuracy, ensure compliance with financial regulations, and accelerate decision-making. From large enterprises managing vast transaction volumes to small businesses automating invoice processing, the ability to efficiently ingest data is key to driving productivity and operational excellence.

For organizations looking to streamline document-based data ingestion and financial automation, Procys offers an AI-powered solution that transforms unstructured data into structured insights.

With automated OCR, real-time extraction, and seamless integration with accounting and ERP systems, Procys helps businesses eliminate manual data entry, reduce errors, and improve efficiency.

Explore how by trying it for free.