INTRODUCTION
Database migration is the transportation of data from existing database to new database. With the advancement in technologies, upgrade in database data migration has become frequent and change in legal regularity is also a reason. To cope with better performance of the application and better outcome to the organization it is critical to achieve the migration in time, budget and allocated resources. Johny Morris, in [1] related data migration as “the selection, preparation, extraction, transformation, and permanent movement of appropriate data that is of the right quality to the right place at the right time and the decommissioning of legacy data stores.” Migrating data from source to destination is not just copy pasting the
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The best practice for data migration is to use a tool or process that involves iterative approach and the answer is ETL, which is used during migration to extract data from legacy system then transform the data in required format and finally load in the target database. Some literatures verify the saying that ETL is more preferred tool for data migration. 41% of projects use ETL, on the second spot of most popular techniques being hand coded solutions with 27% [5]ETL has consolidated tools support[6]. The success of ETL tools is also due to the fact that Business Intelligence and Data Warehousing makes use of this paradigm [7].
[8] Discussed a substantial escalation in data quality based on the observation of statistical analysis before and after using automated ETL.
After successful migration, the main issue lies with the integrity of quality data at target database which is done by data validation testing. A few of the existing methodologies of data validation testing is discussed in brief here in which some follows manual approach and other uses automated approach with their limitations.
Some ongoing methodologies include:
1. Lines of code
This is the simplest process for validation data. It uses COUNT operator and counts number of lines in both legacy and target databases and then compares them. If the rows in both databases are equal then it is considered as successful migration.
Step 2. Data Analysis: The data will be analyzed to determine database modeling. Step 3. Database normalization: Fields and
By filling the database with test data, we can begin to determine if the tables are normalized correctly, need changes to the keys and foreign keys, or new bridge tables are needed to correctly output the data. It is also at this point that we test to ensure that data types match and all entities are of the proper format.
Following are the steps which would be adopted by the company to minimize the downtime during migration.
New applications will utilize off-the-shelf software components that have been customized per Riordan’s specifications and further messaged to ensure that each application will integrate smoothly with all the others in order to create a single cohesive whole. Great effort will be made to ensure that the data structures used in each are consistent in order to simplify the creation of the enterprise’s database. To help facilitate this, we will create an umbrella application that will integrate each other system as a module. This umbrella application will be extendable as needed and will act as a single-launch point for the various systems utilized by Riordan. We will also be working closely with Riordan’s IT department to develop a bridge that will enable them to easily port their existing databases into the new one automatically.
Abstract: Data Quality mostly mention about the quality of the data. Mainly the data considers to be of high quality. This research paper explains about how the Data Quality has the control over the large observation data which has brought many challenges to researchers. It also explains about how Data Quality Monitors data through the user defined algorithms and gives the analysis how the data is being processed. It clearly describes about the six features which ensure strategy planning for the data quality.
As you can see from this chart we are looking at a very large migration that involves everything from
A prestigious university has recently implemented a consolidation strategy that will require it to centralize their student records. In order to move forward, the local university will need to develop a data model that will retain student records and perform various data extract transform and load (ETL) processes. As the database consultant it will be necessary to assist with the development of a design strategy for student records. The following has been determined after meeting with various university subject matter experts:
Our Internet based company with the gross revenues of more than $35 million dollars per year. We are planning to merge with multinational company of equal size. Our company currently uses operational systems and relational databases but desire to expand into data warehousing. We will be integrating different technologies from different solution providers and incorporate industry best practices in connection with the development of technological system.
In conclusion, the report proposes that any migration path is applicable for migration. The only element that must get considered becomes the merits associated with each case. The resource availability is another factor that determines the path that must get followed. The complexity and scope of the cases must get dealt with during the migration
Tony’s Chips is a medium sized enterprise that aims to enhance its operations through e commerce. The company aims to migrate from its current externally hosted website to a new e commerce system that will be hosted internally. The new system will provide data storage, retrieval, security and recovery solutions for the enterprise. It will also enable the company to perform commercial transactions through the website. The new system is expected to improve on the operational reliability provided by the existing system. The company also aims to adopt a system that will provide better data integrity facilities. The firm’s management views a redundant system as offering the most ideal
What was the impact of data quality problems on the companies described in this case study? What management, organization, and technology factors caused these problems?
In today 's organizations, basic descision making procedures and day by day operations frequently rely upon information that is put away in an assortment of information stockpiling frameworks, arrangements, and areas. To transform this information into helpful business data, the information commonly should be consolidated, purified, institutionalized, and compressed. For example, data may be changed over to an alternate information sort or different database servers may store the vital information utilizing diverse patterns. Dissimilarities like these must be settled before the information can be effectively stacked to an objective target. After the plan and improvement of data warehouse as per the business prerequisites, the way toward combining the information into the information stockroom from different sources is to be thought of. Extract Transform Load (ETL) procedures are basic in the achievement of the Data Warehousing ventures. The way toward extricating information from one source (extract), changing it as per the outline of the data warehouse(transform) and stacking it into data warehouse (load) constitute ETL. As it were, ETL is the way toward extracting information from different information sources, changes it according to the prerequisites of the target data warehouse and effectively stacking it into the data warehouse (database). In the transformation procedure data is institutionalized to make it perfect with the target database along with data purifying
The verification of the data was very essential because in order to assure the correct output of the new system which filters out the current information system was needed.
Since the beginning of big data, there has always been an extensive amount of work required of the database administrators that build and maintain their systems. A trending new technology is making great strides as of lately in reducing the repetitive or mundane tasks that take up the employees’ valuable time. The advent of automation in database maintenance has a promising outlook in making regular procedures more efficient.
Data are raw facts of the block of information. To be reminded that all the data will not useful information. Useful information is fulfilled from processed data. Specially, data is to be explained in order to gain information.