Impetus Technologies

Enterprises all across the globe have been dealing with large volumes of data for solving complex business problems. The traditional migration platforms developed years ago aren’t capable of handling current business issues. Yet, most of the data analytics tasks are still carried out with the help of traditional data warehouses. There has been a massive demand for high-speed business analytics, and legacy data warehouses do not fit the bill. Traditional ETL tools can only process relational data. They often fail to process semi-structured and unstructured data formats resulting in data loss, which, in turn, causes integrity issues. Also, they require manual efforts to build and manage data pipeline, which is why enterprises are resorting to cloud migration services for transforming their ETL workloads from legacy data warehouses. Many enterprises have been using Ab Initio to solve complex data processing problems. However, = the advent of advanced platforms such as oracle datab...

The Impetus Workload Transformation Solution helps simplify the intimidating task of data warehouse modernization. It accomplishes this simplification through immense experience that it has gained from large scale enterprise transformation projects. The solution boasts of strong automation capabilities aimed at delivering the fastest and most reliable transformation by migrating Ab Initio ETL graphs to the cloud data lake automatically. Any obstacles to Spark adoption can easily be removed using the solution in 3 easy steps: 1.       Automated legacy code transformation in which the legacy graphs are automatically transformed with the industry’s first intelligent transformation engine. 2.       Automated validation framework to validate transformed ETL graphs. 3.       Execution that helps customers execute their Ab Initio in their standard cloud environment. The solution helps convert, assess, va...

One of the most crucial aspects of the data present in any data warehouse is its quality. Since data warehousing services have been used extensively to amp up an enterprise’s decision-making process, the quality check of data becomes necessary. Data quality issues can hamper the project anytime, anywhere. That’s why it becomes extra important to keep the quality of data in check. Now the question arises, how to accomplish this? Our below-mentioned comprehensive guide is the answer. Have a look: 1.        Data collection: A lot of enterprises are dependent on specific ETL tools. They help in readying their transactional data for OLAP. The effectiveness of these tools is directly proportional to the quality of data that is currently present in the system. This makes it important to use data quality checks at the beginning itself i.e., starting from the data collection process. This can be understood from the following example: When the customer f...