Recognize the Importance of Integrations
Learning Objectives
After completing this unit, you’ll be able to:
- Describe the role of integrations in facilitating data movement and business processes.
- Describe the challenges and approaches of integrating data at different stages of the data lifecycle.
How Integration Strategy Impacts Your Data Strategy
In the previous unit, you learned that data should be the backbone of every business decision. But sometimes data streams through various departments—sales, marketing, finance, and operations—each functioning as an isolated “organ.” Without a solid integration strategy, these departments operate in silos, unable to communicate or share critical information. In this analogy, integration acts as the nervous system, connecting these isolated parts. By ensuring seamless integration of data, your businesses can unlock the true potential of their AI and data analytics strategies, allowing for better decision-making and more accurate predictions.
Integrating Data in the Modern Era
Data integration has been an essential function in distributed systems for decades, facilitating data movement between various applications and systems. However, as the characteristics of data have evolved, expanding in variety, volume, and velocity, it requires carefully planned and well-architected integration solutions.
Modern data environments deal with a wide range of data types, from structured databases to unstructured logs and real-time telemetry streams. Applications also produce and consume data at different granularity and frequency. The ability to bridge the gap between data producing systems and data consuming systems is becoming ever important. Because of this, integration strategies must evolve to meet these demands, supporting higher data complexity and newer technologies.
Before diving into typical challenges faced during data integrations, you need to learn about the data integration layer. A data integration layer combines data from different sources in a meaningful way before it can be passed to other applications. Think of it like preparing a recipe. You bring the raw ingredients together so the dish is fit and ready for consumption. And there are three key steps—sourcing of ingredients, combining the ingredients as required by the recipe, and plating the dish so it’s ready to consume.
Similarly, a data integration layer brings together raw data produced by different applications, applies transformations as needed, and makes the data available for consumption by other applications.
Now that you understand the meaning of the integration layer, let’s review the challenges that arise in the integration layer.
Challenges Faced in the Data Integration Layer
The data integration layer is the bridge that connects data generated in one application and consumed in another application. This section focuses on the integration layer, the challenges that arise in the data integration layer, and approaches to address those challenges.
Data Integration Layer |
Challenges |
Solution Approach |
---|---|---|
Data Ingestion |
Data is available in structured and unstructured formats and at unprecedented volumes across multiple applications. Also, data ingestion needs to handle data that can be available in a variety of timelines, ranging from batch to streaming data sources. The challenge lies in establishing seamless connectivity and an ingestion framework that can handle various formats of data available from different sources at varying frequencies. |
Consider developing a catalog of integration patterns that can cater to streaming and batch data, and different frequencies in between. Having a catalog of approved integration patterns for data ingestion ensures that standards are followed throughout the company—thus providing long-term maintainability of ingestion jobs. Consider developing a common data model that standardizes the representation of data across multiple applications and systems, thus making it simpler to map all incoming data. Mapping different data sources, including structured and unstructured data, to a common data model ensures consistency in understanding the data. |
Data Transformation |
Data often requires significant transformation to be useful in the target application. This might include converting data types, enriching records, or validating data and applying corrective patterns. A lot of transformations in the integration layer can lead to time-consuming integration jobs to the point where the integration could fail. The challenge lies in maintaining a balance between applying complex transformations and having a performant integration. A few examples of transformations that can lead to issues in integrations are navigating through layers of data (think account hierarchy), complicated data joins, and mappings and traversing through multiple levels of hierarchy in data. Data roll-up via transformation also complicates drill-down capabilities later. |
Consider narrowing down the set of transformations based on the requirements of the use case. If some transformations can be reused, look for opportunities to apply them in the source system, thus minimizing the complexity of the integration layer. Similarly, if there are transformations very specific to target application, consider delegating those transformations to target application. Consider simplifying your data model and having a flat structure that minimizes data hierarchy and relationships to the extent possible. |
Data Sharing |
Once data is ingested and transformed, it has to be made available for sharing with other integrations and applications. This can include pushing data to receiving applications as well as making it available to be consumed on-demand. There are several challenges that come up when sharing data with other integrations and applications, such as:
|
To communicate that data is available for consumption, use the publish-subscribe integration pattern where all subscribers are notified. For dedicated notifications, the integration layer can directly send the data or call a webhook to send a trigger that data is ready for consumption. For handling dependencies on multiple integrations, consider implementing composite integration patterns such as, orchestration and choreography SAGA patterns where integrations can operate with predefined dependencies. Learn more about SAGA patterns here. |
Keep in mind that the above table isn’t a comprehensive list to be used as a checklist. Instead, treat it as a guideline to segregate integration problems to identify challenges and possible solutions as you develop your integration solution for a given use-case.
Wrap Up
The evolution of data and its use across different applications has brought new challenges to previous integration strategies. To keep pace with modern requirements, you must adopt integration strategies that support real-time data flow, scalability, and flexibility. By integrating systems efficiently at each stage of the data lifecycle, you ensure that your data strategy remains aligned with your business goals. This ultimately enables better decision-making, enhanced AI capabilities, and the ability to adapt to an ever-changing digital landscape.