Introduction to Modern Data Integration

The following article provides an outline for Modern Data Integration. A business faces huge competition these days due to the advancement in technology. Organizations should be able to utilize information and create new knowledge to lead their business. Innovations in technology and the interconnectivity of things are generating an enormous amount of data every day. Data integration helps to move the data from the source to the target.

Many organizations are using data integration talent to create value for their business. Data integration becomes more important when two companies are merging or when consolidating applications within a company to provide a view of the company’s data assets. The data integration applications do not work simply. They need codes to do the process. A lot of problems will arise related to its maintenance, documentation, and transference.

Data Integration Areas

It is a term that covers many distinct areas like:

Data Warehousing
Data Migration
Enterprise Application
Master Data Management

Data Integration Process

There are three phases in Data Integration:

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1. Design

This process should involve all the departments in a company. In this process, you should ask yourself a few questions.

What are the objectives of data integration?
What are the sources from which the data can be derived?
Is the available data enough to meet the requirements?
Does the data integration go with the business rules?
What is the support model?
What are the SLA requirements?
What are the ways to extract the data from the sources?
What is the quality of the data?
Is there a need for other non-functional requirements such as data processing need, security policy, backup policy, and others?
Who will be the owner of the system, and what will be the total amount of expenses?

The answers to all the above questions should be documented and signed by all the members who are involved in the data integration project.

2. Implementation

Based on the analysis of the requirements and the SRS document, a detailed study is performed to select the appropriate tools to implement the data integration system. There are a lot of tools available in the market, and selecting the best tool for your business is the biggest challenge. Companies that are new to data integration need to implement a new tool, and companies who have already used or used such projects can just extend their existing system. Selecting the best tool that suits your business needs will be more effective and will help for future growth and expansion of the business.

3. Testing

Testing is an important phase of data. Proper testing is needed to make sure that the unified data are complete and correct. The IT department and the entire business need to take part in the testing process. The different testing methods that can be used are Performance Stress Test (PST), Technical Acceptance Testing (TAT) and User Acceptance Testing (UAT ), PST, TAT (Technical Acceptance Testing), UAT (User Acceptance Testing).

Techniques

There are several data integration techniques performed by companies:

Manual Integration or Common User Interface -It provides the users access to all the source systems or web page interface.
Application Based Integration – This is used only where there is a limited number of applications.
Middleware Data Integration – Helps to transfer the logic from one application to a new middleware layer.
Uniform Data Access or Virtual Integration – Defines a set of views to provide access to the users to the unified view of the data.
Common Data Storage or Physical Data Integration – Has a copy of the data from the source and stores and manages it independently in the original system.

Products of Data Integration

Primarily there are three products of data integration:

Extract, Transformation, and Load (ETL) – These products help to move the huge amount of data which also enables robust transformations.
Enterprise Application Integration Products (EAI or EII) – These products helps to move smaller quantities of data with different frequency patterns.
Enterprise Data Replication (EDR) – These products give information about the data sets and when they have to be changed or modified.

Principles

We live in an era of data. Modern data is more complex, and it has different types, sources, volumes, and locations of data. Modern data has a lot of advantages. There are a lot of principles for modern data integration to meet today’s data environment.

The major five principles of data integration are listed below:

1. Take the processing to where the data lives

There is a lot of data, and you need to blend the data every time before you transfer it. Appoint an agent in the host platform so that all the process is done locally before the data is transferred. By moving the processing system to where the data lives will reduce the movement of the data through the network, which in turn will save time.

2. Leverage all the platforms based on their design

If you are making investments in modern data platforms, every platform will have a default set of functions and workloads. It uses those default functions within the platform. In this way, the performance of the data blending can be improved, and the data movement can be reduced.

3. Move data point to point

Modern data platforms should be centrally engaged with all the business rules and data logic. It is possible only when the local processing system is kept separate from the central design studio. Managing the business rules and data logic can help to maximize their usability and provides transparency.

4. Make changes using existing rules

If the management is handled centrally, all the business rules and data logic templates can be kept under a single roof. If there is any change to be made to the data or platforms, then such changes can be made quickly and efficiently.

Benefits

It will eliminate all the challenges raised by legacy technology.

Few of the benefits are listed below:

Designed once and can be used many times.
Gain in-depth knowledge about the data.
Manage the complex data environments.
Optimize the actions.
Ability to change, extend and migrate existing business data.
Responds quickly to business needs.

Top 10 New Requirements

The business world becomes more competitive; they depend more on data. More accurate data lets the business people to make smart decisions. Data volumes are growing bigger every day, and the business needs their data to be processed faster to gain better results. The data sources and IT environments change frequently, and it becomes more complicated to get detailed knowledge about the data. As a result, new requirements have evolved for modern data integration.

Listed here are 10 new requirements for modern data integration:

1. Application Integration is done through REST and SOAP services

Today’s software applications are implemented as cloud-based services that will expose the SOAP/REST API’s for data and metadata management based on business goals. In today’s scenario, SaaS applications restrict access to the database behind their services. Modern data integration offers an easy and quick way to use REST and SOAP. They should provide an easy way to use these APIs in business.

2. Huge volume data integration is available to a Hadoop based data lake

IT departments in an organization are moving apart from data warehouses and moving towards data lakes that are the central location of data based on the Hadoop cluster. Spark is the most recent technology used to transform data of large amounts in this environment. Cloud data warehousing technologies act as a substitute for expensive data warehouse applications. Data integration tools have to be easy to access and understand, and it should possess large-scale distributed frameworks such as Spark.

3. Integration must support the data speed

The data velocity is relatively high these days. Any change in the data velocity or data size should not expect you to change the data integration tools as a whole. Previous data integration tools were designed to handle either a large volume of data or less volume of data.

Modern data integration should have the ability to handle the data at whatever the size it is. The data integration tools should be able to process the huge amount of data easily and deliver proper responses to improve business actions such as adding new products or adding new customers.

4. It should be event-based

Data integration tools should respond to a business event quickly as it happens. If the stock of an inventory has to be increased because of the demand, then the data integration should process this information quickly.

5. Integration should be document-centric

The previous generation of data integrating tools sends and receives hierarchical documents as such instead of transforming it into row sets or into compressed payloads. It was established to make the internal engines work efficiently as the biggest impediment against the old generation of data integration tools.

6. Integration should be hybrid

These days software packages are cloud-based. Still some organizations invest a lot in on-premise applications that take a lot of time to migrate into the cloud-based applications of the enterprise. Modern data integration technology is able to handle the on-premise as well as cloud-based applications easily and effectively.

7. It should be accessible through SOAP/REST API’s

It should go well with the other functions of the organization like monitoring, security, and others. For instance, if a company wants to monitor the success of integration flow through its own tool then the modern data integration should allow the company to add more users automatically with ease.

8. It is all about connectivity

It mainly depends on connectivity. It is about connecting different systems with an API set. The integration tool should also need a proper framework to process the data without wasting time and effort. There are also a large number of pre-built connectors available which will help in the easy and quick implementation. It also helps to respond to queries quickly under new scenarios.

9. It has to be elastic

The demands of data integration differ every day based on business events. In a single day there may be a lot of integrations taking place under a scenario and the next day there will be the normal amount of integrations taking place. Modern data integration should have the capacity to handle the worst as well the good situation. The integration tool should be able to adapt itself to different situations.

10. Integration should be provided as a service

Today’s world is cloud-based and data-driven. Modern data integration technology has to be rendered as a service that can be easily accessible by anyone who needs to use it. Modern data integration should be elastic to meet the demands of the business. The traditional data integration technology is more complex and takes a long time to implement. The updates are also expensive. The modern data integration technology is directed towards a new class of users who are called “citizen integrators”. It offers a simple design and easy management system. It can meet all the requirements of the users.

In simple words, the major requirements for this are summed up in the following points:

It must be able to integrate any data from any source.
It should be stored either in the cloud or in the premise.
Should provide maximum performance.
Give all-time support and offer trusted information.
Provide quality data.

Conclusion

New trends in the industry are making data integration more important than ever. Businesses today are facing problems dealing with a large amount of data. To improve the business results they need to convert their data into an asset. More powerful and flexible data integration technologies have to be used to transform the data into a usable form. All the new requirements have resulted in the development of a new field of integration called integration platform as a service.

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