Modern data architecture overview

With the evolution of technology, the data generated from various geospatial sources is increasingly diverse and growing exponentially. Data of any type is captured and stored across various data stores. Companies want to collect, store, and analyze geospatial data as quickly as possible to derive insights from it for business operation improvements and better customer experience, which will help them stay ahead of their competitors. With the diverse types of geospatial data, a one-size-fits-all data strategy would have many challenges in geospatial data management. You should be able to capture and store any volume of geospatial data at any velocity using flexible data formats. This requires a highly scalable, available, secure, and centrally governable data store or a data lake that can handle huge geospatial datasets. You also need the right tools to run analytics services against this data. This requires moving the data between the data lake and...