Data Integration vs. Data Ingestion

Data Integration vs. Data Ingestion

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With the increasing amount of data being produced, companies need better ways to manage and use the information they collect. Data integration and ingestion are essential components of a successful data strategy and help organizations get the most out of their data assets.

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Data integration and data ingestion are two essential concepts in data management that are often used interchangeably, but they are two distinct processes that serve specific business purposes. By understanding the differences between data integration and data ingestion, organizations can ensure they are using the most effective data management solution for each project and business data use case.

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What is data integration?

Data integration combines data from different sources and transforms it into a unified view for easy access and analysis. The process combines data from disparate sources, such as databases, APIs, applications, files, spreadsheets, and websites.

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Data integration is typically accomplished through an extract, transform, and load process. The ETL process extracts data from different sources, transforms it into a standard format, and loads it into a data warehouse. This allows you to query, analyze, and use the data in other applications.

How does data integration work?

The data integration process begins with extracting data from disparate sources, such as databases, flat files, web services, or other applications. Once the data is extracted, it is transformed to make it consistent. This transformation can include filtering, sorting, deduplication, and even formatting of the data into a desired schema.

The transformed data is then loaded into a unified destination system, such as a data warehouse or a single file. Once the data is combined and processed, data professionals can use it to create dashboards, visualize trends, predict outcomes, or generate reports.

With data integration, companies can develop faster decision-making capabilities due to improved data governance and automated processes. They can also become more agile and respond faster to changing customer needs.

Types of data integration

There are several types of data integration that companies can use. They include:

manual data integration

This type of integration typically requires manual data entry from one system to another or the use of scripts or programs to move data between the two systems. Manual data integration is typically done for small-scale data integration projects or to maintain data integrity between two systems.

Middleware data integration

Middleware data integration involves the use of software that acts as an intermediary between two or more applications to facilitate the exchange of data from legacy systems to modern applications.

Application-based integration

Application-based integration software locates, retrieves, and integrates data from disparate sources into target systems. This may involve using a prepackaged or custom application designed to integrate data.

Uniform Access Integration

This method of data integration allows users to access data from multiple sources in a consistent format while ensuring that the source data remains intact and secure. This strategy allows users to view and interact with data from different sources without replicating or transferring it from its original location.

Common Storage Data Integration

This type of data integration makes it possible for data to be copied from source systems to a new system. This method combines data from disparate sources, allowing for more complete analysis and information.

What is data ingestion?

Data ingestion involves moving data from one source or location to another for storage in a data lake, data mart, database, or data warehouse. It consists of extracting the data from its original format, transforming it into a form suitable for storage, and then loading it into the destination system. Data is often pulled from CSV, Excel, JSON, and XML files.

SEE: Useful strategies to improve data quality in data lakes (TechRepublic)

Data ingestion differs from data integration in that it does not involve processing the data before it is loaded into the target system. Instead, it is simply transferring data from one system to another. This means that the data is transferred in its raw form with no modifications or filters applied.

How does data ingestion work?

Data ingestion collects data from multiple sources and uploads it to a repository or data warehouse. Data can be collected in real time or in batches.

SEE: Job Description: Data Warehouse/ETL Developer (TechRepublic Premium)

The data is then processed and transformed using ETL processes to prepare it for analysis. Alternatively, ETL processes can be used to load raw data as quickly as possible before transformations. After the data transformations are complete, the data is loaded into the target system, such as a database, cloud storage platform, or analytics engine.

Types of data ingestion

There are several types of data ingestion methods available, such as the following:

batch ingestion

This involves collecting and processing data in chunks or batches at regular intervals.

streaming ingestion

This type of data ingestion involves collecting and processing data in real time. Stream ingestion is often used for low latency applications that focus on tasks like real-time analytics, fraud detection, and stock market analysis.

Hybrid data ingestion

Hybrid data ingestion combines batch and streaming ingestion practices. This approach is used for data that requires a batch layer and a streaming layer for full data ingestion.

Common challenges of data integration and ingestion

Data integration and ingestion can be complex processes and present unique challenges. Here are some of the common issues organizations face when dealing with these two data management tasks.

Data quality

Data quality issues can arise due to different data formats coming from various sources. This can lead to data discrepancies, data integration delays, and incorrect results. Poor data quality can be caused by incorrect formatting, input, or coding, leading to inaccurate information and poor decisions.

Data volume

The amount of data that needs to be processed can be too large for traditional platforms, making it difficult to quickly process the data.

security challenges

Organizations must take extra precautions to ensure that their data remains secure during data integration and ingestion. This includes encrypting data before it is sent to or stored in a cloud-based system and setting up access control measures to limit who can see it.

Scalability challenges

As companies grow, they need to invest in tools and resources to scale their data ingestion and integration processes. Otherwise, they could risk losing valuable information and opportunities due to slow or outdated data processing.


Data integration and ingestion require an investment of time and money. Depending on the complexity of the project, costs can vary significantly, so it is important to consider the resources your project requires and how much they will affect your budget.

Data ingestion and integration tools are necessary for organizations that collect, store, and manage large amounts of data. These tools enable efficient retrieval, manipulation, and analysis of data from multiple sources.

Data Integration Tools


The SnapLogic logo.
Image: SnapLogic

SnapLogic is an enterprise integration platform as a service that enables organizations to integrate data, applications, and APIs across on-premises and cloud-based systems. It provides a visual, drag-and-drop interface to quickly connect cloud and on-premises applications and data sources, automate processes, and build robust data pipelines that span multiple systems.

SnapLogic’s iPaaS includes a library of more than 500 pre-built connectors, also known as Snaps, and an AI-powered wizard to help users quickly find and connect the right apps and data sources.

Oracle 12C Data Integrator

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Image: Oracle

Oracle Data Integrator 12c is an ELT platform that moves and transforms data between multiple databases and other sources. It is designed to automate data integration processes and is used to create and maintain efficient data management solutions.

ODI 12c is a platform-independent, standards-based data integration product that supports the full spectrum of data integration requirements. This includes real-time and batch data integration, as well as big data integration.

IBM Cloud Pak for data

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Image: IBM

IBM Cloud Pak for Data is an integrated data and artificial intelligence platform that helps organizations make better decisions faster. It is based on open source technology and provides powerful tools to help companies unify their data, gain insights and automate processes. It enables organizations to securely manage, analyze, and share data across multiple clouds and on-premises environments.

Data Ingestion Tools

nifi apache

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Image: Apache

Apache NiFi is an open source software project that provides a data flow platform to manage and automate the movement of data between different systems. It is designed to automate the flow of data between systems, making it easy to collect, route, and process data from source to destination. It provides low latency and high throughput, dynamic prioritization, loss tolerance, and guaranteed delivery.


The Talend logo.
Image: Talend

Talend is a unified platform for the integration and integrity of data across multiple sources and systems. It enables users to access and integrate data from on-premises and cloud-based sources, clean and govern it, and deliver trusted data to decision makers. It also allows users to create, deploy, and manage data pipelines to process data in real time.

Read Next: Top Data Integration Tools (TechRepublic)

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