The Challenges of ETL Oracle to SQL or Any Other Data Warehouse


ETL is the process of extracting, transforming, and loading data from various RDBMS source systems into the Data Warehouse system, like MS SQL Server. Handling the business information efficiently is a challenge, and with ETL Oracle to SQL, the process becomes a lot simpler.
MS SQL Server plays a great role in improving your business’ data platform. It not only allows you to reinforce your company’s data processing performance but also allows for easy and quick data analysis. This is the reason why ETL Oracle to SQL is so important considering the fact that in the modern computing business, data exist in multiple locations and in many incompatible formats. That is, your business data might be stored on the file system in multiple formats like plain text, spreadsheets, PDF, Word docs, or kept under a database server like Oracle or even stored as email files.
Extracting, transforming, and loading your data from Oracle to SQL Server can enhance your everyday operations. In simple terms, you can aggregate Oracle’s insights to your data platform, whether it is SQL Server or any other Data Warehouse in minutes through the ETL process. 

Overview of ETL in Data Warehouses
It is important that you load your data warehouse frequently to achieve efficient business analysis. In order to do this, data from Oracle or any other operational system(s) needs to be saved and copied into your preferred data warehouse, like SQL. However, the biggest challenge that comes with using data warehouse environments is that it is difficult to integrate, consolidate, and rearrange large amounts of data over a database server or many systems. If done correctly, it provides you a new cohesive information base for business intelligence.     
ETL is a broad process that involves extracting data from source systems and saving it into the data warehouse. The method and approach used in ETL are well known for many years and are not essentially distinct to data warehouse environments.
During the extraction of data from the Oracle database, the desired data is identified. Most often, it is a bit challenging to determine the exact subset of interest. Thus, more data than required has to be extracted. This means that the identification of the relevant data is done later on during the ETL process.   
Depending on the capabilities of the source system (oracle, in this case), some transformations are taken place during the extraction process. Since we are talking about the Oracle database, the size of the data extracted may vary from hundreds of kilobytes to gigabytes. On the other hand, they are the logically identical extractions (ELT Oracle to SQL); so the time dealt between the two may vary between real-time to near minutes and hours/days. For example, the web server log files can easily grow to hundreds and thousands of megabytes in a very short period. 

Transportation of Data
Once the data is extracted, it is either physically transported directly to the SQL server or to an intermediate system for retrieving the relevant information and further processing. Transformations can be easily done during the transportation process. Users of Oracle Database can easily program complex data transformation logic using SQL. You can take advantage of Oracle Database’s new SQL functionality for ETL. 

What are the concerns with ETL Oracle to SQL
One of the biggest challenges when extracting, transforming, and loading the Oracle database to SQL server is to design and maintain the ETL process. Designing and maintaining are considered the most resource-intensive aspect of the whole ETL process. This is where you might need the services of a quality data integration solutions provider. Such companies can provide you with customized ETL tools and processes that make integration a lot easy process.         
Further, for a successful ETL implementation, there are some tasks beyond extraction, transformation, and loading that needs to be addressed using customized ETL tools and methodologies. Things like transporting data between different databases, transforming large volumes of data, and quick loading of new data into the data warehouse, among others, are some of the critical aspects that a quality ETL tool and customized ETL solution provides.  
 
Final Words
The sources and targets might change in the data warehouse since it is a living IT system. Thus, it is important to maintain and keep track of the system through its entire lifespan without deleting or overriding the old ETL flow information.



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