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With the increasing amount of data being produced, businesses need better ways to handle and use the information they collect. Data integration and data ingestion are essential components of a successful data strategy and help organizations make the most 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 easier access and analysis. The process merges data from disparate sources, such as databases, APIs, applications, files, spreadsheets and websites.

SEE: Cloud data warehouse guide and checklist (TechRepublic Premium)

Data integration is typically achieved by an extract, transform, 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 the data to be queried, analyzed and used in other applications.

How does data integration work?

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

Transformed data is then loaded into a unified target system, like a data warehouse or a single file. Once the data is combined and processed, data practitioners can use it to build 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 various types of data integration that businesses can use. They include:

Manual data integration

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

Middleware data integration

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

Application-based integration

Application-based integration software locates, retrieves and integrates data from disparate sources into destination systems. This can involve using a custom-built or pre-packaged application designed to integrate data.

Uniform access integration

This data integration method allows users to access data from multiple sources in a consistent format while ensuring the source data remains intact and secure. This strategy enables 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 comprehensive analytics and insights.

What is data ingestion?

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

SEE: Helpful strategies for improving 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 destination system. Instead, it is simply transferring data from one system to another. This means data is transferred in its raw form with no modification or filtering applied.

How does data ingestion work?

Data ingestion collects data from multiple sources and loads it into a data repository or warehouse. The data can be collected in real-time or in batches.

SEE: Job description: ETL/data warehouse 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 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 streaming layer for complete 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 that organizations face when dealing with these two data management tasks.

Data quality

Data quality issues can arise due to the different data formats that come together from various sources. This can lead to data discrepancies, delays in data integration and incorrect results. Poor data quality may result from incorrect formatting, entry or coding, leading to inaccurate insights and bad decisions.

Data volume

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

Security challenges

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

Scalability challenges

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

Cost

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

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

Data integration tools

SnapLogic

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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 create 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 assistant to help users quickly find and connect the right applications and data sources.

Oracle Data Integrator 12C

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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 build 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 batch and real-time 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 AI platform that helps organizations make better decisions faster. It is built on open source technology and provides powerful tools to help businesses 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

Apache NiFi

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Image: Apache

Apache NiFi is an open-source software project that provides a data flow platform for managing and automating data movement between different systems. It is designed to automate data flow 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.

Talent

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Image: Talent

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

Read next: Top data integration tools (TechRepublic)

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