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From Data Ingestion to Data Integration

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Data ingestion and Data integration are often used interchangeably. Although both terms deal with effective data management, they have distinct meanings and objectives.

This article discusses how Data Ingestion and Integration are related and how they can help businesses manage their data efficiently.

What is Data Ingestion?

Data Ingestion is collecting raw data from different sources and transferring them to a destination so teams can access them easily.

Usually, the sources may include simple spreadsheets, consumer and business applications, external sensors, or the internet. Destinations may include a database, a data warehouse, or a data lake. 

Data ingestion doesn't apply transformations or verification protocols to the data it collects. As such, it is commonly the first step in a data pipeline.

Batch vs. Streaming Data Ingestion

There are three main types of data ingestion processes – batch, streaming, and hybrid. Organizations should select the one that aligns with the type and volume of data they collect and the business needs. 

They should also consider how quickly they require new data for operating their product or service. 

Batch Data Ingestion: Data ingestion process runs at regular intervals to fetch groups of data from several sources batch-wise. Users can define trigger events or a specific schedule to start the process.

Streaming or Real-time Data Ingestion: With streaming data ingestion, users can fetch data the moment it gets created. It is a real-time process that constantly loads data to specified destinations.

Hybrid: As the name suggests, hybrid data processing mixes batch and real-time techniques. Hybrid ingestion takes data in smaller batches and processes them at very short intervals of time.

Businesses should either use real-time or hybrid ingestion techniques for time-sensitive products or services,

Data Ingestion Challenges

One major challenge is the ever-growing volume and variety of data that can come from several different sources. For instance, Internet-of-Things (IoT) devices, social media, utility and transaction apps, etc., are some of the many data sources available today.

However, building and maintaining architectures that provide low-latency data delivery at a minimal cost is challenging.

The following section briefly reviews some ingestion tools that can help with these issues.

Tools for Data Ingestion


Improvado is a tool for collecting marketing data. It performs several collection operations automatically and supports over 200 marketing data sources, including Google and Facebook Ads, Google Ad Manager, Amazon Advertising, etc.

Apache Kafka

Apache Kafka is an open-source, high-performance platform that can ingest big data at low latency. It is suitable for organizations that want to build real-time processes for streaming analytics.

Apache NiFi

Apache NiFi is a feature-rich tool with low latency, high throughput, and scalability. It has an intuitive browser-based user interface that lets users quickly design, control, and monitor data ingestion processes.

What is Data Integration?

The process of data integration unifies data from several sources to provide an integrated view that allows for more insightful analysis and better decision-making.

Data integration is a step-wise procedure. The first step performs data ingestion, taking both structured and unstructured data from multiple sources, such as Internet of Things (IoT) sensors, Customer Relationship Management (CRM) systems, consumer applications, etc. 

Next, it applies various transformations to clean, filter, validate, aggregate, and merge data to build a consolidated dataset. And finally, it sends the updated data to a specified destination, such as a data lake or a data warehouse, for direct use and analysis.

Why is Data Integration Important?

Organizations can save a lot of time through automated data integration procedures that clean, filter, verify, merge, aggregate, and perform several other repetitive tasks. 

Such practices increase the productivity of the data team as they spend more time working on more worthwhile projects.

Also, data integration processes help maintain the quality of products or services that rely on Machine Learning (ML) algorithms to deliver value to the customer. Since ML algorithms require clean and the latest data, integration systems can help by providing real-time and accurate data feeds.

For example, stock market apps require constant data feeds with high accuracy so investors can make timely decisions. Automated data integration pipelines ensure that such data is quickly delivered without errors.

Types of Data Integration

Like data ingestion, data integration has two types – batch and real-time integration. Batch data integration takes groups of data at regular intervals and applies transformation and validation protocols.

Real-time data integration, in contrast, applies data integration processes continuously whenever new data becomes available. 

Data Integration Challenges

Since data integration combines data from different sources into a single and clean dataset, the most common challenge involves varying data formats. 

Duplicate data is one major challenge where duplication occurs while combining data from multiple sources. For example, data in the CRM may be the same as that from social media feeds. Such duplication occupies more disk space and reduces the quality of analysis reports. 

Also, data integration is as good as the quality of incoming data. For example, the integration pipeline may break if users manually enter data in the source system, as the data is likely to have numerous errors.

However, like data ingestion, companies can use some integration tools discussed in the following section to help them with the process.

Data Integration Tools


Talend is a popular open-source data integration tool with several data quality management features. It helps users with data preparation and change data capture (CDC). It also lets them quickly move data into cloud data warehouses.


Zapier is a powerful no-code solution that can integrate with several business intelligence applications. Users can easily create trigger events that lead to certain actions. A trigger event may be a lead generation and an action may be to contact the leads through email. 


Jitterbit is a versatile low-code integration solution that lets users create automated workflows through the Cloud Studio, an interactive graphical interface. Also, it allows users to build apps with minimal code to manage business processes.

Making Data Work For You

Organizations must build new pathways so that their data works for them instead of the other way around. While a robust data ingestion process is the first step, a flexible and scalable data integration system is the right solution.

It is, therefore, no surprise that integration and ingestion are among some of the most popular emerging trends in today's digital era.

To learn more about data, AI, and other such trends in technology, head onto to get valuable insights on several topics.


Haziqa is a Data Scientist with extensive experience in writing technical content for AI and SaaS companies.