Application Integration is the integration of business data and workflows from disparate applications in an organization. It is the exertion to effectuate seamless interoperability and data orchestration required for generating real-time insights. Data integration is the practice of retrieving data from heterogeneous sources and combining the retrieved data to form a unified structure and view.
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As more and more businesses turn to digital transformation to streamline their technology, rapid application development (RAD) platform, HokuApps, simplifies the process with rapid turnaround time.
One of the pillars of the digital transformation movement is in technological automation, allowing tasks to be completed without human assistance.
According to a report by the McKinsey Global Institute, automation is estimated to increase productivity in the workplace by 0.8% to 1.4% by 2065.
This, in turn, will help businesses reduce costs, improve operational efficiency, reduce errors, and reduce the time required to complete a given task.
International Data Corporation estimates “that, by 2020, 30% of G2000 companies will have allocated capital budget to at least 10% of revenue to fuel their digital strategies.” This means that more and more companies are recognizing the importance of digital transformation over the coming years.
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At a very basic level, ETL is a process in which data from disparate sources is extracted from a source database, transformed (or processed), and then loaded onto a target store or data warehouse.
Over the years, the way ETL works, and the business functions it serves have gone through fundamental changes to reach the current stage of its evolution – a highly effective technique to process and integrate real-time data stored across multiple entities.
Traditional ETL systems were developed in the early 1970s, when large organizations needed to integrate data generated by their different departments like sales, inventory, and marketing, and use this data to gain insights into their business and operations.
Another challenge was that data could only be moved in batches, which meant that large volumes of data could be moved only when network traffic was low.
It is now possible to query and process voluminous data in real-time from multiple sources that need to be tracked.
Modern ETL also supports most cloud-based operations which fetch and process data – today’s ETL solutions are flexible enough to handle multiple data sources along with both structured and unstructured data.
