In recent years, the need for analytics on large volumes of data has become increasingly important. It turns out to be extremely useful in making strategic decisions about different applications. In this way, appropriate mechanisms must be designed to carry out data processing and integration with different platforms to take advantage of their best features. In this work, an architecture that works on cloud services is shown to migrate data stored in Big Query to an analytics engine such as Elasticsearch and take advantage of its potential in query, insert and display operations. This is accomplished through the use of Cloud Functions and Pub / Sub. The integration of these platforms through the proposed architecture showed 100% effectiveness when transferring data to another, maintaining an insertion rate of 4,138.30 documents per second, demonstrating its robustness, efficiency, and versatility when performing a data migration. This pretends to establish an architecture solution when it comes about handling a large amount of data as in the real world.