Data ingestion 101: Process, challenges and setting up a data ingestion pipeline

What is data ingestion?

Data ingestion is the process of moving or onboarding data from one or more data sources into an application data store. Every business in every industry undertakes some kind of data ingestion - whether a small scale instance of pulling data from one application into another, all the way to an enterprise-wide application that can take data in a continuous stream, from multiple systems; read it; transform it and write it into a target system so it’s ready for some other use.

Types of data ingestion

Data ingestion methods vary based on your specific requirements, data sources, and business needs. Understanding the different approaches helps you choose the right strategy for your use case.

Batch ingestion

Batch ingestion involves collecting and processing data in scheduled groups or "batches" at regular intervals. Batch ingestion is typically done at a much lower cadence but with much higher efficiency.

Common examples: Nightly sales reports, monthly financial consolidations, weekly customer behavior analysis, or quarterly regulatory reporting.

Real-time (streaming) ingestion

Real-time or streaming ingestion processes data continuously as it's generated, with minimal latency between data creation and availability. 

Common examples: Credit card transaction monitoring, stock trading platforms, real-time website personalization, or continuous equipment monitoring in manufacturing.

Micro-batch ingestion

Micro-batch ingestion strikes a balance between batch and streaming approaches by processing small batches of data at very short intervals (seconds to minutes). This hybrid method provides near-real-time insights while maintaining the efficiency and manageability of batch processing.

Common examples: Social media sentiment monitoring, clickstream analysis for web analytics, inventory updates in e-commerce systems, or log aggregation for system monitoring.

Change Data Capture (CDC)

Change Data Capture monitors and captures only the changes made to source data - inserts, updates, and deletes - rather than reprocessing entire datasets. 

Common examples: Synchronizing customer records between CRM and data warehouse, maintaining backup databases, updating product catalogs across multiple systems, or feeding data lakes with operational changes.

Lambda architecture

Lambda architecture combines both batch and streaming processing layers to handle data ingestion at scale while maintaining accuracy. The batch layer processes complete datasets for accuracy, while the speed layer handles real-time data for immediate insights. 

Common examples: Large-scale recommendation engines, complex financial risk analysis systems, comprehensive customer 360 platforms, or enterprise-wide analytics platforms.

Push vs. pull ingestion

Beyond timing considerations, data ingestion can also be categorized by how data moves from source to target:

Push-based ingestion: Source systems actively send data to the target as it becomes available. The source controls when and how data is transferred, often using webhooks, APIs, or message queues.

Pull-based ingestion: The target system periodically queries or polls source systems to retrieve new data. The target controls the ingestion schedule and determines when to fetch updates.

Choosing between push and pull:

• Use push when sources can easily notify targets of changes (event-driven architectures)
• Use pull when you need centralized control over ingestion timing and resource usage
• Consider hybrid approaches where some sources push while others are polled

Choosing the right ingestion method

The optimal ingestion approach depends on several factors:

• Latency requirements: How quickly do you need data available after it's created?
• Data volume: How much data are you processing, and how frequently?
• Cost considerations: Real-time streaming typically costs more than batch processing
• Source system capabilities: What methods do your data sources support?
• Complexity tolerance: Can your team manage sophisticated streaming infrastructure?
• Data consistency needs: Do you require transactional guarantees or can you accept eventual consistency?

Many organizations use multiple ingestion methods simultaneously—batch processing for historical reports, CDC for database synchronization, and streaming for real-time monitoring. The key is matching each data flow to the method that best serves its specific requirements while keeping infrastructure complexity manageable.

Data ingestion vs ETL vs data integration: What's the difference?

These terms are often used interchangeably, but they have distinct meanings that are helpful to understand when designing your data architecture. In practice, these processes often overlap.

Data ingestion is the process of moving data from one or more sources into a target system, adapting it to the required format and quality along the way. It's typically a repeated process - think onboarding new customers to your SaaS platform, or regularly pulling data from external sources into your system. 

ETL (Extract, Transform, Load) is traditionally focused on moving data into data warehouses or data lakes for analytics and business intelligence. The emphasis is on transforming data into well-defined, rigid structures optimized for reporting. 

Data integration involves combining data from multiple different sources to create a unified view. Rather than simply moving data from one place to another, you're merging datasets together—for example, combining customer data from your CRM with order data from your e-commerce platform to create comprehensive customer profiles. 

Read more

• What's the difference between data ingestion and ETL?
• Data ingestion vs data integration: What's the difference?

Steps of a data ingestion process

The process of data ingestion consists of several steps. 

• Detect that new data is available to onboard: Whether files are sent via email, or dropped into an FTP site, your ingestion pipeline should detect these automatically and move them for processing.
• Inspect the layout and format of that data: There can be several layers to data validation in data ingestion, from checking that all necessary files are present, to quality checks on the data itself (is it the right format? Are there too many null values?) 
• Read the data
• Map and transform data: The pipeline needs to know how to map the data to the target, but also what transformations are needed, such as combining or splitting up fields. 
• Assess data quality: Validating data again helps keep quality high, and data can be validated against specific business rules to ensure that details are correct.
• Load data to target: Once files are validated, transformed and ready to go, the next stage is pushing them to their destination, whether that's storage such as a data warehouse, or on to further processing.
• Detect issues and log progress through each step: Finally, your pipeline needs to create a log detailing how the process went, and any errors that occurred.

Steps of a data ingestion process

To streamline processes, save time on manual work and reduce errors, as much of your data ingestion process as possible should be automated.

Data validation in CloverDX

Discover how CloverDX helps you automate validation of incoming data at every stage.

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Data quality and validation in data ingestion pipelines

Data quality issues are one of the biggest challenges in data ingestion. Bad data can cause pipeline failures, corrupt downstream systems, and erode trust in your platform. The key is building validation into multiple stages of your pipeline - catching problems early and handling errors systematically.

Two types of validation

Input validation checks data as it enters your pipeline. This might include verifying file formats are correct, checking that required fields are present, and ensuring data types match expectations. The more robust your input validation, the fewer surprises you'll encounter downstream.

Output validation is your final check before data reaches its destination. This catches any transformation errors, ensures data meets target system requirements, and validates that business rules have been applied correctly.

Data profiling: Understanding your data at scale

Beyond validation of individual records, data profiling examines entire batches to assess overall quality. For example, you might check that the total number of records matches expectations, that key fields aren't mostly null, or that value distributions look reasonable. Profiling helps you spot systemic issues - like a client changing their file format - before they cause bigger problems.

Handling rejected records

When data fails validation, what happens next? In a well-designed ingestion pipeline, rejected records are captured with detailed error information and routed to a process for correction.

Importantly, error handling should be accessible to the people who can actually fix the data - often business users or customer success teams, not just IT staff. They're the ones who understand the context and have the authority to correct issues.

Building error handling that scales

An effective data quality approach includes:

• Automated detection: The pipeline automatically identifies and flags problematic records
• Actionable error reporting: Error messages that clearly explain what's wrong and what needs fixing
• Audit trails: A complete record of what was rejected, why, and how it was corrected
• Reprocessing workflows: A clear path to fix data and push it back through the pipeline

As well as fixing immediate problems, this systemic approach can provide valuable insights about your processes, training needs or upstream systems.

Read more: Building data pipelines to handle bad data

Data ingestion use cases

Data ingestion can take a wide variety of forms. These are just a few real-world examples:

Data ingestion for SaaS and customer onboarding

Why SaaS companies need robust data ingestion

SaaS platforms face a standard data ingestion challenge: they need to onboard data from many different sources (clients), which often arrives in different formats via different methods. The SaaS business needs to onboard data quickly so that new customers can get up and running fast, and so the data that customers are seeing is up-to-date.

The challenge of accepting data 'as-is' from multiple clients is a common one - clients often won't (or can't) format data to exactly the right specification, so incoming data needs to be standardized to the SaaS company's requirements. This often involves lots of manual, error-prone work that takes up developer time. And it becomes impossible to scale as client volumes increase. 

Enabling non-technical teams to manage data onboarding

Often, the domain experts who understand the data best can't work on it because the ingestion pipeline lives in a technical system that only developers can use. Sending Excel files back and forth for manual corrections is insecure and error-prone. The best way to be able to offload onboarding work from technical to business users is to have a data platform that offers separate interfaces for each audience, enabling collaboration and more efficient onboarding.

Case study: Freeing up engineering time by a third for Zywave

Other data ingestion use cases

• Taking data from various in-house systems into a business-wide reporting or analytics platform - a data lake, data warehouse or some standardized repository format
• A business providing an application or data platform to customers that needs to ingest and aggregate data from other systems or sources
• Ingesting a constant stream of marketing data from various places in order to analyze results and maximize campaign effectiveness
• Taking in product data from various suppliers to create a consolidated in-house product line or provide data to feed an e-commerce site
• Consolidating research data from multiple researchers to provide comprehensive results and analysis

Data ingest for faster client onboarding

Accelerate setup time for new data by sharing reusable building blocks and creating automated data ingestion and transformation frameworks.

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Data ingestion case studies

• Automating and centralizing data processes and reducing manual, ad-hoc data tasks
• Importing large amounts of data in various formats into a proprietary suite of tools, with customer-specific logic and transformations
• Scaling data onboarding for a growing Fintech company, by enabling collaboration between the engineering and non-developer teams
• Automatically ingesting and processing customer order data for a logistics company

Data ingest challenges

Setting up a data ingestion pipeline is rarely as simple as you’d think. Often, you’re consuming data managed and understood by third parties and trying to bend it to your own needs. This can be especially challenging if the source data is inadequately documented and managed. Typical challenges include:

• Poor data quality from the source
• Connecting to different source systems to pull data from
• Multiple different data formats that all need to be consolidated
• Extensive transformation requirements that take a lot of manual work
• Engineering team as a bottleneck when only technical users can handle onboarding
• Domain experts or business users not being technical enough to be part of the ingestion process
• Lack of visibility with no audit trails or transparency
• Needing to scale capacity without growing headcount
• Difficulty identifying and fixing errors that occur either in the source data or during ingestion

Data ingestion frameworks for scale

One of the biggest challenges growing companies face is the need to scale up data ingestion without just increasing headcount to handle more manual work. The answer is building an automated data ingestion framework to handle data coming from multiple sources, in different formats, automatically.

Benefits of a data ingestion framework

• Reusability: No need to build a new pipeline for each new data source. Build the core process once, and just configure the client-specific piece for new sources.
• Reduction of manual work: Handle steps from detecting incoming files, to transformation, validation and loading, without requiring human input
• Consistency: Data flows through the same process, with the same validation steps, each time
• Easier maintenance: A single core process means one pipeline to maintain and manage, and changes only need to be made in one place
• Improved visibility: Easily see any errors that occur with any data, and establish a master error-handling process to handle corrections and fixes

Setting up a data ingestion pipeline

Automating data ingest

When you’re dealing with a constant flow of data, you don’t want to have to manually supervise it, or initiate a process every time you need your target system updated. You really want to plan for this from the very beginning otherwise you'll end up wasting lots of time on repetitive tasks.

Human error can lead to data integrations failing, so eliminating as much human interaction as possible can help keep your data ingest trouble-free. (This is even more important if the ingestion occurs frequently). 

Both these points can be addressed by automating your ingest process. 

You’ll also need to consider other potential complexities, such as:

• A need to guarantee data availability with fail-overs, data recovery plans, standby servers and operations continuity
• Setting automated data quality thresholds
• Providing an ingest alert mechanism with associated logs and reports
• Ensuring minimum data quality criteria are met at the batch, rather than record, level (data profiling)

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The data ingestion blueprint

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Data ingest can also be used as a part of a larger data pipeline. Other events or actions can be triggered by data arriving in a certain location. For example - a system that monitors a particular directory or folder, and when new data appears there, a process is triggered.

Data ingestion parameters

There are typically 4 primary considerations when setting up new data pipelines:

• Format – what format is your data in: structured, semi-structured, unstructured? Your solution design should account for all of your formats.
• Frequency – do you need to process in real-time or can you batch the loads?
• Velocity – at what speed  does the data flow into your system and what is your timeframe to process it?
• Size – what is the volume of data that needs to be loaded?

It’s also very important to consider the future of the ingestion pipeline. For example, growing data volumes or increasing demands of the end users, who typically want data faster. 

Governance and safeguards

Another important aspect of the planning phase of your data ingest is to decide how to expose the data to users. Typical questions asked in this phase of pipeline design can include:

• Will this be used internally?
• Will this be used externally?
• Who will have access to the data and what kind of access will they have?
• Do you have sensitive data that will need to be protected and regulated?

These considerations are often not planned properly and result in delays, cost overruns and increased end user frustration.

Signs you need to automate data ingestion

• Increasing data volumes are leading to more inconsistent data: When you need to ingest varied data, and perform often complex validation and transformation, it's hard to do manually, and some data importing tools aren't customizable enough
• Basic data importing tools aren't enough: If your data ingestion is simple, you can handle pieces of the process with basic importer tools. But when you need more complex transformations or validation, its time to look at a robust automation tool.
• Engineering team is spending too much time on onboarding: When the technical team becomes a bottleneck to the data ingestion process, you need to free up time by offloading some of the work to other users.
• There's too much manual work involved: Spending days, weeks or longer manually editing Excel files isn't scalable, and leads to data delays
• You're unable to scale your customer base without adding headcount: Relying on manual work prevents growth. Only by automating processes can you bring on more customers and more revenue without corresponding increases in resource.
• High error rates and constant firefighting: Dealing with inconsistent data manually means lots of errors and pipeline failures. Automating consistent processes, giving domain experts more input, and increasing transparency all help avoid errors. 
• Data delays are impacting customer satisfaction and revenue recognition: As volumes increase, existing processes can struggle to keep up. If data takes days to process, delays mean unreliable data and dissatisfied customers. 

Read more: Data ingestion tools: 7 features you should look for

Data ingestion with CloverDX

Your data ingestion process should be efficient and intuitive, and CloverDX’s automation capabilities can play a crucial role in this, giving you:

• Automated, transparent data pipelines for quicker customer data ingestion
• A simplified onboarding process, reducing reliance on development teams
• The capability to handle diverse data formats and sources, easing customer data preparation
• Efficient engineering, preventing bottlenecks in onboarding - as well as the ability to move work away from the engineering team to less technical colleagues
• Scalability without additional headcount, and the ability to handle large data volumes
• Automated error handling and validation for accurate data processing

Our demos are the best way to see how CloverDX works up close. Get in touch for a personalized demo and see how you could streamline your data ingestion process with CloverDX.