ETL systems ( Extraction , Transform, Load ) allow, among many other advantages, to integrate data from different sources and source systems in data warehouses, in order to unify criteria for data analysis and management , and facilitate sufficient material for the tools that meet this objective (analyze and manage large volumes of data) to provide broad and comprehensive information about the organization . There is no doubt that in the transformation of ETL systems, the emergence of new data sources has played a crucial role. From the enormous proliferation of mobile applications, to the constant and exponentially growing enrichment of open data databases , fed with data from the use of technological tools of all kinds, the source systems of which data to extract are increasingly large, complex and diverse , something that inevitably forces ETL systems to evolve. ETL Social networks: great challenges for ETL systems Obviously, the emergence and expansion of social networks has provided a horizon full of new business opportunities. 

However, and in parallel, it has also established a scenario in which the improvement of data extraction, transformation and integration (ETL) systems has become a constant , a need as compelling as it is urgent. There is no doubt that the improvement and optimization of data extraction tools has been a top priority for developers, and a no less pressing Phone Number List need for their corporate consumers ( the correct performance of data management strategies of companies and organizations of all kinds depend directly on it). In this aspect, enormous progress has been made, managing to [b][url=https://lastdatabase.com/phone-number-list/]Phone Number List[/url][/b] overcome one of the biggest drawbacks presented by the extraction of large volumes and variety of data, coming from a great diversity of source systems: saturation, or what has been called "neck bottle. Today, data extraction under the conditions we have described does not pose great challenges from a technical point of view, thanks to the incredible evolution experienced by ETL systems. However, the transformation process is where we currently find the main technological barriers , and where the tools designed for this have a greater room for improvement ahead. 

Among the main challenges posed by the transformation and processing of data extracted from sources as diverse as social networks, key architects of the evolution of ETL systems, the possibility of automating personalized transformation rules and protocols stands out , given the material impossibility of creating them manually for each of the origin sources, the data typologies (extremely varied) and the requirements of the different destination systems in which they must be integrated. Each new data source brings with it great difficulties in terms of processing and transforming this data, especially when it is intended to integrate it into target systems in which it will converge with previously transformed and loaded data . It will not take long for us to talk about new developments in this matter, clearly crucial to getting the most out of the data management strategy adopted by any organization. In this sense, and to understand to what extent it is convenient to have a corporate data management strategy that is solid and versatile enough to take advantage of the opportunities offered by social networks, among other notable data sources, reading 10 keys is especially indicated. to define your corporate data management strategy , available completely free. Related posts: Be careful with company decisions based on social media analytics.