Data migration vs data integration: The main differences

Data migration vs data integration, which is better? In fact, this is a question we really want our clients to ask themselves more often. Because it is the key factor to make a successful project. Besides, if you’re purchasing a new CRM or website with the goal of boosting your decision-making data, you should think about it from the beginning. With this reason, in this article, ArrowHiTech will give you the main differences you need to know between Data migration vs data integration. Hence, let’s explore with us right now! 

Overview of Data integration

When it comes to data integration solutions, a network of data sources and clients obtaining data from the Master Server are its common components. The client initiates the process by requesting data from the master server. The master server then extracts the necessary data from both external and internal sources. Data is extracted from several sources and then compiled into a single, cohesive dataset. After that, this information is sent back to the client for further processing. Now, let’s see common cases where Data Integration should be used below.

#1. Business Intelligence Simplified

#2. Taking advantage of Big Data

In general, data lakes can be large and difficult to manage. For example, big and famous companies like Google and Facebook have to process massive amounts of data from billions of users on a daily basis. And, Big Data is the term used to characterize this degree of information consumption. 

#3. Data lakes & Data warehouses 

Large firms primarily use Data Integration activities to develop Data Warehouses, which combine many data sources into a Relational Database. From that, users can not only create reports, perform queries, generate analytics, but also obtain data in a uniform format using data warehouses. 

What are types of Data Integration Strategies 

Application-based Integration 

Application-based Integration is a method of data retrieval, location, and integration that uses software applications. Besides, the software should ensure that data from various systems is interoperable during the data integration process. This will enable them to be moved from one location to another. 

Common Storage Integration 

When mentioning Data Integration, Common Storage Integration is the most common solution for storage. The integrated system keeps duplicate data from the original source and refines it for a unified perspective. Moreover, the classic Data Warehousing system is founded on this premise. 

Uniform Access Integration

The goal of this type of data integration is to create a front end that makes data appear uniform across multiple sources. However, the data remains in the original source. Best of all, object-oriented Database Management Systems can use this strategy to give the appearance of uniformity amongst databases. 

Manual Data Integration

With this data integration type, a single user manually collects data from multiple sources by directly accessing interfaces. Then, they cleanse and combine it into a single Data Warehouse for future use. Small businesses with limited data resources may find this Data Integration procedure to be the ideal option. However, if you are running a larger business, this can be inconclusive and inefficient. 

Middleware Data Integration

As its name suggests, a middleware program serves as a mediator in this integration method. The data is then normalized and added to the Master Data Pool. Because older applications have a hard time interacting with other apps, you need to ask a middleware for help. When a Data Integration system cannot access data from one or more legacy systems on its own, it can be used. 

data integration

Best practices

Figure out data sources to include

Based on the business case, you must pick which data sources to include. Most enterprises rely on IBM and other traditional mainframe systems to run their operations. Besides, most Data Integration initiatives require core transaction data from these platforms. Moreover, companies should also look at their various software systems and determine what function data from each of those systems would play in achieving the business case’s goals. 

Begin with an End in Mind

For best results, the Data Integration process should start with a defined project goal. This is due to the fact that well-executed Data Integration projects can produce measurable returns. 

Methods of Data communication to be determined

When deciding how data should be disseminated, there are several factors to consider. Most critically, you must look at both present and prospective data volumes to see whether Data Pipeline capacity will be sufficient to accommodate the load.

>>> Read more: The most considerable data integration challenges and how to overcome them 

Overview of Data migration

The process of shifting data from one system to another that involves a change in the database or application as well as storage is known as data migration. In fact, data migration comes with a wide range of applications. They may need to create a new Data Warehouse, modernize databases, merge new data from an acquisition or another source, or completely rework a system. Moreover, when deploying another system alongside current apps, you will also need to use Data Migration.

What are types of Data migration? 

Trickle Data Migration 

The Trickle Data Migration approach works in stages to complete the Data Migration process. The new and old systems run in parallel during implementation. As a result, there will be no downtime or operating interruptions. What’s more, real-time processes can keep data travelling at a constant rate. Although the implementation can be difficult, if done correctly, it can help to reduce hazards. 

data migration

Big Bang Migration

In this data migration type, the entire transfer is performed in a set amount of time. While the data undergoes ETL processing and moves to the new database, live services will experience downtime. The fact that everything happens in a one-time boxed event limits it. As a result, it takes only a short amount of time to complete. Because one of the company’s resources is down, the pressure can be tremendous, resulting in a hampered implementation. 

What are best practices for the data migration process? 

Sticking to the Strategy

The first practice for the data migration process you should know is Sticking to the strategy. Data managers frequently devise a strategy only to abandon it when the process runs too smoothly or when things go wrong. Because this process can be frustrating and confusing at times, you must plan ahead of time and stick to your plan. 

Backing up the data before executing 

You can’t afford to lose data if something goes wrong during the installation. As a result, you must ensure that backup resources exist and that they have been evaluated before proceeding. 

Extensive testing

You must test the Data Migration during the design and planning phases, as well as during maintenance and implementation, to ensure that you will reach your desired result. 

Main differences of Data migration vs data integration 

data migration vs data integration

In fact, Data Integration and Data Migration differ in a number of ways. For starters, Data Analytics requires data integration from other sources. This is because businesses are attempting to present their customers with a 360-degree view. Data Migration, on the other hand, is a procedure that is followed when new storage mediums or systems are introduced. It’s also used when businesses need to relocate their present resources to a new location. When building a new application, data migration is a one-time procedure, whereas data integration is a continual activity that keeps the business working on a daily basis. 

>>> Refer to our Integration And Data Migration service.

Wrapping up

To sum up, this article lets you know the main distinctions between Data migration vs data integration. Before delving into the distinctions between the two, we also provide a quick introduction of Data Integration and Data Migration including its benefits, use cases, and best practices. Then, in case you have any inquiries on this topic, let’s fill out our CONTACT FORM without any hesitation to get free consultancy. 

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