Hello everyone and welcome back to the Cognixia podcast!
Every week, we bring to you some amazing new content about emerging technologies, hoping to help our listeners discover something they didn’t know before, inspiring them to achieve their goals. So, thank you for tuning in today!
In today’s episode, we talk about two terms that get used interchangeably but don’t mean the same thing in reality – Data migration and Data integration. Both processes play very different roles in the data management and preparation lifecycle. While there are a few similarities between the two terms, there are also some significant differences that set the two apart.
So, let us try to understand both these terms one by one.
What is Data Migration?
As the name suggests, data migration involves moving data from one location to another and would involve a change in the database, the application, or the storage. Data migration is usually undertaken when one needs to modernize the databases or the data warehouses need to be modernized or there is new data from new or old sources. There could be other reasons and causes too, but these are the most common ones.
The most common tools used for carrying out data migration are:
- CloverDX
- Microsoft SQL Server Migration Assistant
- IBM Informix
- AWS Cloud Data Migration
- Amazon DocumentDB
- IBM Cloud Migration Services
- Talend Open Studio
A good data migration tool should be able to let users schedule jobs, organize workflows, and map and profile data, while also letting one carry out post-migration audits. The tool should be compatible with your data sources and data types, helping you accomplish your goals with the infrastructure you intend to use. A good data migration tool would be able to migrate your data in a time and resource-efficient manner without compromising the data quality. And above all, it should be easy to use and offer good technical support.
That’s all about data migration and the tools to use. Now let us talk about the other term of interest today – Data Integration.
What is Data Integration?
Data integration, as the name suggests involves integration or merging. Data integration involves merging data from different sources into one single database or a single data warehouse. Data integration plays an important role in helping organizations make better, more informed decisions while having access to better data quality and better data analysis.
Data integration is a commonly used process for building data warehouses, and improving reporting, querying, and analytics.
The most common tools used for data integration include:
- Integrate.io
- Azure Data Factory
- Oracle Data Integrator
- Dataddo
- Informatica
- Talend
A good data integration tool would enable users to write data to target systems, services, and/or applications that one aims to use. Your data integration tool should help you integrate data from a diverse range of sources and deliver it to your targeted service or system in a standardized version. The tool should be capable of letting you transform the data and build efficient data pipelines.
Now that we have a fair understanding of what is data migration and what is data integration, let us dig deeper into the differences between these two processes and why these two terms cannot be used interchangeably.
First, let us talk about the frequency at which both processes occur. When a new application is being implemented, the data migration process will occur only once, it is a one-off process. Due to this, the volume of data involved will be significantly large. When data migration takes place, everything required during the process and for the process would be prepared and kept ready to use in advance.
Compared to this, data integration is a continuous process that is part of the daily processes in an enterprise as it deals with incremental data changes. Since data integration is a continuous process, it is easier to carry out compared to data migration and it is also more flexible.
The next point of difference is the purpose these two processes are carried out. Data integration aims to help users consolidate the applications or combine the applications to simplify the reporting process and give the enterprise access to better analytics and better business intelligence, consequently improving the efficiency and effectiveness of operations.
Compared to this, data migration aims to help enterprises upgrade their existing systems or even replace them as a whole. So, if an enterprise needs to expand its system and storage capacity or they want to move its applications to the cloud if wants to take a leap into a digital transformation and break down the inhibiting data silos, then data migration would be an essential step.
In essence, data migration would involve tasks like selecting, priming, extracting, transforming, and transferring data from system A to system B. Data integration, in contrast, involves tasks like bringing together data from multiple sources to give the users a unified view.
Here’s something interesting to note. While data migration and data integration are two different processes and the two terms must not be used interchangeably because they do not mean the same thing, it is important to understand that the two processes often work together too. So, while both these processes carry out different functions, both of them at their core involve the transfer of data, though for totally different reasons and purposes.
Suppose you are carrying out a cloud data migration, then you would most likely be carrying out both a data migration process as well as a data integration process. Not just this, in a lot of cases, the data migration process acts as a foundation for the data integration process.
Moreover, when data integration and data migration work hand-in-hand it also yields more actionable insights for the enterprise and the decision-makers, while also helping improve the productivity and efficiency of operations.
So, this should give some idea of how data integration is different from data migration, how the two processes function, what the two processes are used for, and when which process gets used. You now also know how data migration and data integration can work in sync or be used together to achieve the desired outcomes.
And with that, we come to the end of today’s podcast episode. Data integration and migration are two very large topics to cover in the short time that one podcast episode permits, but if you are an aspiring cloud professional, data professional, or even an aspiring DevOps professional, you will delve deeper into it as you train and prepare to be a skilled professional in your field of interest. You can explore Cognixia’s range of live online instructor-led courses on our website and get in touch with us to know more.
We will be back next week with yet another episode on the Cognixia podcast, until then, happy learning everyone!
