Big data analysis and processes help to sift through large datasets that are growing by the day. Organizations undertake data migration operations for numerous reasons. These range from replacing or upgrading legacy applications, expanding the system and storage capabilities, introducing an additional system, moving the IT infrastructure to cloud or merger and acquisition instances when the IT systems are integrated into a unified single system.
The fastest and the most efficient way to move large volumes of data is to have a standard pipeline. Big data pipelines let the data flow from the source to the destination whilst calculations and transformations are processed simultaneously. Let’s see how data migration can aid big data pipelines be more efficient:
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Why Data Migration?
Data migration is a straightforward process where data is moved from one system to another. A typical data migration process includes Extract, Transform and Load (ETL). This simply means that any extracted data needs to go through a particular set of functions in preparation so it can be loaded onto a different, database or application.
There requires a proper vision and planning process before selecting the right data migration strategy. The plan should include the data sources and destinations, budget and security. Picking a data migration tool is integral to making sure that the strategy adopted is tailor-made to the organization’s business requirements or use case(s). Tracking and reporting on the quality of data is paramount to knowing exactly what tools to use to provide the right information.
Most of the times, SaaS tools do not have any kind of limitations on the operation system; hence vendors usually upgrade them to support more recent versions of both the source and destination automatically.
Having understood about data migration, let’s look at some of the desired characteristics of a big data pipeline-
Monitoring: There needs to be systemic and automatic alerts on the health of the data so potential business risks can be avoided.
Scalability: There needs to be an ability to scale up or down the amount of ingested data whilst keeping the costs low.
Efficiency: Data, human and machine learning results need to keep up with each other in terms of latency so as to effectively achieve the required business objectives
Accessibility: Data needs to be made easily understandable to data scientists through the use of query language.
Now let’s look at where data migration comes into the picture in a big data pipeline
The Different Stages of a Big Data Pipeline
A typical data pipeline comprises of five stages that is spread across the entire data engineering workflow. Those five stages in a big data pipeline are as follows:
Collection: Data sources like websites, applications, microservices and from IoT devices are used to collect the required and relevant data to be processed.
Ingestion: This step moves the streaming data and batched data from already existing repositories and data warehouses to a data lake.
Preparation: This step is where the significant part of the data migration occurs where the ETL operation takes place to shape and transform the data blobs and streams. The ready-to-be-ingested ML data is then sent to the data warehouse.
Computation: This is where most of the data science and analytics happen with the aid of machine learning. Insights and models both are stored in data warehouses after this step.
Presentation: The end results are delivered through a system of e-mails, SMSs, microservices and push notifications
Data migration in big data pipelines can take place in a couple ways depending on the business’ needs and requirements. There are two main categories of data migration strategies:
1. Big Bang Migration is done when the entire transfer is done in a limited window of time. Live systems usually go through a downtime whilst the ETL process happens. This is when the data is transitioned to a new database. There is a risk of compromised implementation, but as it is a time restricted event, it takes little time to complete.
2. Trickle Migration on the contrary, completes the migration process in different phases. During the implementation, the older and new the systems are run parallelly so as to ensure there in no downtime or operational breaks. Processes usually run in real-time that makes implementation a bit more complicated than the big bang method. But if this is done right, it reduces the risk of compromised implementation or results.
Best Practices for Data Migration
Listed down are some best practices that will help you migrate your data with desired results:
1. Backing Up Data
There are instances while migrating data that things will not always go according to plan. Things can go missing or potential data losses can occur if files get corrupted or are incomplete. Creating a backup helps to restore data to its primary state.
2. Verify Data Complexity and Standards
There arises a need to asses and check what kind of different data an organisation requires to be transferred. After finding out what the data format is and where it is stored, it can be easier to detect the quality of legacy data. This ultimately leads to being able to implement comprehensive firewalls to delineate useful data from duplicates.
3. Determine Data and Project Scope
The data migration strategy must be compliant with regulatory guidelines which means that there comes a need to specify the current and future business needs. These business rules must be cooperative with business and validation rules so as to make sure that the data is transferred consistently and efficiently.
4. Communicate and Create a Data Migration Strategy
The overall data migration process will most likely require hands-on engagement from multiple teams. Making sure there is a successful data migration strategy in check requires the team to be delegated with different tasks and responsibilities. This alongside of picking the right data migration strategy for your unique business requirements will give you the edge that you are looking for in an age of digital transformation.
Breeze through your Big Data Pipelines
Data pipelines as-a-service helps developers assembling an architecture that can help for easy upgrade of their data pipeline. There are a number of things such as being very meticulous with cataloguing that can help with bytes not being lost in transit.
Starting simple is the answer, alongside which there needs to be a careful evaluation of your business goals, the contributions to the business outcome and what kind of insights will actually turn out to be actionable.