Big Data Integration

January 10, 2019

Big data has been a buzzword for many years already. It’s not a surprise that you’ve been reading about big data everywhere as data could have enormous business value for organizations. The insights derived from the data could help companies to improve their processes or even improve customer relationships among other benefits. While some companies only use big data analytics as their supplementary source of information, others are using it as a primary source for reporting and analytics.

 

Nevertheless, most organizations lack a strategic plan concerning big data integration that is essential for receiving all information from multiple sources. There is a need for state-of-the-art big data integration solutions to ensure that the big data analytics tools are supplied with all necessary information from a variety of sources. Having a big data integration solution in place is just as important as having good analytics tools for creating insights.

 

In this articles, we will explain what big data integration is, what are the challenges of big data integration, why you should craft a big data integration strategy, and how an iPaaS can support your work around big data.

 

Big Data Integration

 

Big data means gathering and storing large volumes of information, and it is typically defined by the three V’s model: volume, velocity, and variety. We assume that you are familiar with big data, its definition, and concepts around the phenomenon. However, the three V’s idea is essential from a big data integration point of view as well, so we base our explanation of big data integration on this notion.

 

Enterprises today may use hundreds if not thousands of systems or applications. This means that to receive all relevant data, the data must be extracted from many different sources, and the volumes could be overflowing. Besides the volume, also the variety of sources need to be considered for integration purposes. While APIs are becoming popular for getting data for example from software to your analytics tool, you will likely need to connect legacy IT systems, cloud, even internet of things devices, sensors, or human-generated content such as social media feedback.

 

Also, there is a variety of data. You will need to be able to receive both structured, semi-structured, and unstructured data. You will need to be able to deal with heterogeneous data formats coming from the data sources. To simplify how your data scientists deal with the data, you will need to be able to unify the message types into a single data format that your analytics tools can understand. Important to note that different analytics tools operate on different data formats.

 

Also, the timeliness of the data is crucial. You need to ensure that the right data is available for all the right stakeholders at the right time. The right time very often means real-time in a competitive global environment.

 

This short, simplified introduction to big data and big data integration will set us up for diving deeper into this topic.

 

Big Data Integration Challenges

 

While we have briefly mentioned a few roadblocks above, we want to compile a full list of the challenges you may face when you need to transfer big data from a variety of sources. (If you're in the logistics industry, make sure you don't miss our previous articles on the big data challenges of logistics companies!)

 

You will need to work with extremely diverse systems. Legacy proprietary systems are still a thing, and by architecture, it’s difficult to connect them with modern applications to extract the data from them. You will need to find integration tools that can work with hybrid environments. Many has been opting for enterprise integration platform as a service (EiPaaS) that can also function as a hybrid integration platform (HIP). Before committing to an iPaaS, make sure to pilot it to ensure that it would suit your big data integration needs.

 

The mixture of the systems and applications will have another challenge you need to tackle: you will need to work with heterogeneous data. This means that a legacy IT system may use a proprietary data format developed decades ago, but a cloud-based software could use JSON which is a lightweight data format that is easy to work with. Most likely you will also need to handle structured, semi-structured, and unstructured data. Also, different data analytics methods require different data format – this is something you should think through when you are planning your big data integration strategy.

 

In addition to these challenges, there could be problems with data quality. Therefore, you would need to ensure that your integration solution can set up rules and processes to ensure that data are validated and enriched when required. Maintaining data integrity is crucial to provide that you will derive accurate insights from the information you receive from all the sources that are important for your business.

 

These points mentioned above validate that traditional relational database management systems (RDBMS) just won’t work when you have big data. You need to ensure that you have a high-speed network available, you have a high-performance data integration tool for connectivity and data management, data capture, data replication, or loading the data to your backup systems (by the way, you also need to have a low-cost data storage).

 

Data integration is complicated, but it is strategically important to plan how you will ensure the availability of your big data.

  

Big Data Integration Strategy

 

When you need to deal with big data, you should strategically plan how you’re going to work with it. Your data integration strategy should consider big data integration to ensure that your business intelligence tools always get adequate information from all possible sources.

 

When you prepare your data integration strategy, you should consider evaluating a variety of data management and data integration tools to ensure that the tools you start working with are aligned with your vision for utilizing the big data at your disposal. When you create a strategic plan, you should consider the viability and feasibility of the solutions you are about to employ.

 

This will help you to select the best tools and vendors for your strategic plan. Typically, you would need to consider critical challenges, such as setting up a standard process, specify your data quality requirements, ensure that the data environment is ready for scalability if data volume would increase, or how you plan to work around data reuse.

 

 

Big Data Integration Example – A Challenging Scenario

 

Let’s look at the example. One of the most common industry in case of big data is the healthcare industry. One patient can visit multiple institutes, and all will store their data, however, upon agreement, one institute should be able to share the data with other ones. This requires an enormous amount of system integration, and in this case, you often need to deal with legacy systems, so you’d need to utilize hybrid integration solutions to receive legacy data and data also from software or the cloud. The integration solution should transfer the data to the right place, so data analysts or doctors can access it. The user interface where the data is stored should also ensure that the data can be browsed and easily found – nevertheless, this is not something the integration vendor would take care of.

 

Big Data Integration Process

 

Once you’ve understood all the challenges you’re going to face regarding big data integration, and you’ve created your big data integration strategy, you should shortlist the vendors you want to work with. It’s likely that you will need to consider different data integration vendors, iPaaS vendors, API management platform providers, data storage providers, and if you’re not yet using the cloud, you should also research your options to start exploiting the benefits of the cloud.

 

Once you have chosen the companies you want to work with, make sure that you take them for a test drive by selecting an important use case so you can pilot their solutions and services. When the pilot has been successful, you can move forward.

 

At this point, it is good to mention that you can simply purchase the tools for integration that your IT team can use, or you can invest in managed services, so you won’t need to deal with the complexity of integrations yourself. For individual projects, even large enterprises opt for managed services, while managed services are ideal for small and medium-sized enterprises that wouldn’t have the resources and skills to create integrations. When you choose to work with a provider of managed integration services, they will design your solution, develop it, end-to-end test it together with your team, and finally deploy the implementation into production. Most managed providers will also take care of maintenance and change management.

 

Before we would get into more details, a big data integration solution could be explained like this in a simplified way:

 

- Consolidate data from a variety of distributed data sources

- Handle a variety of heterogeneous data formats

- Maintain data integrity

- In some instances, help with process automation

 

An excellent big data integration solution should reduce the complexity of the task at hand. It would connect all the applications and would act as an ETL (extract, transform, load). This means that the solution gets the data from system A, it converts the message format to the one that your system understands, and the data is cleansed and mapped before it will load to the final destination – whether it is a data storage or the analytics software.

 

In the case of big data integration, new sources of data may need to be added rapidly. The integration solution should be ready to accommodate any additional connection request, so you can start utilizing data from new sources. REST APIs are popular for creating connectivity between two endpoints, but in other cases, you will need adapters to enable connections between the data storage, data analytics tool, and the data sources.

 

 

iPaaS for Big Data Integration 

 

Considering all the challenges you may face concerning data integration and the solutions that you need, iPaaS can be a reliable tool for enabling data transfer to a variety of sources to a data storage or a data analytics software you use. 

With iPaaS, you can manage the connectivity regardless of the type of the data source. As Youredi has an enterprise scale tool with hybrid integration platform capabilities, it will connect both legacy on-premise systems and modern cloud-based applications. Connecting all your data sources will enable data transfer while it will also take care of other side processes, such as data mappings, data validation, and enrichment. Finally, it can be used for process orchestration.

Youredi Big Data Integration

Youredi Big Data Integration. Source: Youredi.

In Youredi’s case, you receive a fully managed integration service: this means that you define what sources you need to extract data from and what is the destination of the big data and we’ll handle the rest from development to maintenance.

As Youredi’s iPaaS runs on Microsoft Azure cloud, it has unlimited scalability available regardless of the size of the solution. It always has the typical 99,98% availability of the cloud. It enables the real-time transfer of information; however, it is also possible to transfer the data in batches if you prefer.

While big data integration can sound like an overwhelming task with a good plan, the right approach, and technology vendors that support your journey, it shouldn't be a headache.

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