Everything You Need To Know About Data Migration
Every business owner will have to transfer or move their data at some point even though the entire process is tedious. Data migration can be defined as ‘the process of moving data from a source system to a destination or target system’. Usually, the migration of data is carried out when a business owner purchases another company or when they wish to integrate their company’s data with another company. Otherwise, they wish to integrate the data within different departments of their company to make the data available all across the business.
Furthermore, you might sometimes need to transfer your company data from on premise platforms to a cloud storage or you might have to move the data from an outdated system to a new data storage. There can be multiple reasons for data migration. Even though the concept is simple, the process is likely to be a bit daunting. Below are some of the important details you should keep in mind before transferring the data.
Data Migration Obstacles
As mentioned earlier, every company will have to migrate their data at some point. Below are some of the major challenges that you are likely to face during data migration.
Note every data will be in the same format or come from the same source or stream. Some of the common data sources include S3 buckets, RDBMS, CSVs, etc. Since different data reaches from multiple sources, it has to be normalized, cleansed, or transformed so that the user will be able to analyze all of it along with other data from different sources.
Planning the model of your data
As part of the data migration, you may have to alter the model of your data. It’s always better to perform this task regardless of whether you are transferring the data from a premise storage to a cloud database, a relational data to a combination of data that is structured and unstructured, or even simply transferring data from one relational database to the other.
One of the most critical points that you must consider during and after the data migration process is security. On a related note, when you are migrating confidential data, it will be subjected to compliance necessities that will be challenging to support during the data migration process.
Data Migration Methods
You may rely on multiple methods to carry out the data migration process. Some of those potential methods are stated below.
Exporting and importing
When you import or export data, you will need to export it in a format that is neutral; a CSV (comma separated value) file for instance. You can then modify the files in such a way that they are converted to the required format before importing them to the destination database. Note that this is the slowest form of migrating data as the data type alteration and structure modifications are carried out manually by the experts.
Using traditional ETL (Extract Transform Load) tool
You can transfer the data using a third-party Extract Transform Load tool as well. Note that the ETL tools are created to extract, transform, and store the data from multiple sources to different types of targets efficiently. You can easily process larger data volumes using ETL tools. However, not all the ETL tools are created alike.
While some tools require transferring data in batches, some are created for relational databases. Note that most data sources are unstructured these days. On top of that, the rules associated with the data are very strict in this case. You will have to reprocess the entire data if there are any changes in the source, target, or scheme.
In this type of migration method, you will have to write a code of script in order to change the data into a format that compliments the destination data storage. While this process is way faster than the former one, it will still be a tedious affair since you will have to write a script code for each target and source.
Modern ETL tools
For simple and faster migration, use modern ETL tools that are capable of processing data in real-time instead of batches. The modern ETL tools are created in a flexible way so that they can handle a wide range of targets and sources. Plus, you can easily modify your data to meet the requirements of your company. They can also upscale or downscale data based on varying throughput.