Data Migration And Data Integration
Bitpardaz data migration experts help enterprises successfully move their data to better navigate the digital landscape and create strategic solutions that deliver tangible business results.
Data migration and can be a risky proposition to undertake, but is most definitely a critical part of any program that involves change. The need to migrate data happens all the time, whether due to storage upgrades, vendor changes or storage transformation projects, so its imperative enterprise level entities are sure about their chosen solutions provider. Our vast experience has shown that a smooth migration is an important part to the success of any project, usually requiring collaboration among different stakeholders.Bitpardaz takes data import into the future. We have a range of next generation tools, solutions and services that can transform your data import capabilities – solving the painstaking process of importing complex datasets onto your software systems. Whether it is importing customer/vendor data or complex datasets from legacy systems into a data warehouse – we save you significant time and money while enhancing your customer’s experience.
Data Migration
Data Migration is a process where data is transferred between storage types, formats, data architectures and enterprise systems. Whereas Data Integration involves collecting data from sources outside of an organization for analysis, migration refers to the movement of data already stored internally to different systems. Companies will typically migrate data when implementing a new system or merging to a new environment. Migration techniques are often performed by a set of programs or automated scripts that automatically transfer data.
Data Integration
Data Integration is a combination of technical and business processes used to combine different data from disparate sources in order to turn it into valuable business insight. This process generally supports the analytical processing of data by aligning, combining, and presenting each data store to an end-user, and is usually executed in a data warehouse via specialized integration software. ETL (extract, transform, load) is the most common form of Data Integration in practice, but other techniques including replication and virtualization can also help to move the needle in some scenarios.
Data Migration Process
When deploying a new program, organizations need to migrate data from their legacy system to keep their historical information. Such migration is a daunting process where any errors can corrupt data, set the project off track and disrupt day-to-day operations.
Discovery: RefinePro analysts review the legacy system and perform a gap analysis with the new data model. They map each data point and identify the required transformation steps.
Normalization: Based on the discovery results, developers write the script to normalize the data. It is the opportunity to clean and remove duplicate records. The additional effort ensures the new system go live with reliable data.
Test: Analysts import the data in a sandbox or test environment to review the system configuration and data mapping.
Final Import: RefinePro analysts perform the final export from the current system, apply the normalization script and load the data into the new application. They ensure that the latest updates in the legacy system are captured and processed.
Data Integration Process
With the multiplication of cloud-based products and internal application, organizations must ensure that programs speak to each other. They must define and implement the data flow connecting their systems, so data remains current across the organization.
Strategy: RefinePro data architect map how data move across the organization. To determine the integration strategy, they review the granularity of the information, the update frequency, the location master data and how other systems refer to it.
Implementation: When available, RefinePro developers leverage API to implement real-time integration between application. Otherwise, a custom script extracts and process the latest changes from the source database.
Data Validation: RefinePro recommends to include validation scripts to ensures data quality over time. The control step rejects low-quality data to prevent them from corrupting downstream systems.
Deployment: RefinePro can schedule scripts on its platform which provide monitoring and administration capabilities. RefinePro’s managed services monitors alerts and update the integration script accordingly.
Our Main Clients
There are our major clients which have long-term contract with us.
