Data management system for Real Estate investment management company
Our client provides professional services in real estate and investment management. Included in the Fortune 500 list.
Purpose: collection of real estate and organizations data from various sources (own data, Land Registry, Valuation Office Agency etc.) for further analysis and visualization.
Goal: Using information about the value of property for sale/rent, build a price heat map. Add detailed filters to provide an ability of an effective search. Search for properties with a price below the market for future investment/rental. Display of professional specification of organizations (shops, restaurants, car services, banks, etc.) to find the best location for the new business. Using data from mobile operators on the movement of tourists/citizens to determine the most visited places and display them on the map.
Technologies: Azure Data Factory, U-SQL and SSIS are used for import and data processing purposes providing an ability to handle constantly growing volume of data. Large amount of data is coming in csv and txt formats.
Created solution to handle data management using REST-APIs, Azure Blobs. Manual changes to the data are done through the web interface. The Google services and the commercial version of Address Doctor were used to standardize the addresses and restore them from the UPRN and UARN codes.
Google’s service was also used to get coordinates. Employees were involved to manually enter data through the web interface on the perimeter of buildings (polygons) to make images more detailed and realistic.
Scalability: Solution allows to gather the data in different countries and add more countries as per business needs. On the first stage all the data is coming into the Data Lake to be utilized in the search system without any country limitations.
Results: current solution let the Company
- Increase the efficiency of best investment objects search process
- Increase the quality of property selection service provided to clients
- Open additional stream of income by charging third-party companies for display and advertising of their real estate objects.
Utilizing given approach each client get his own database. In such case every client`s data stays isolated from others. It simplifies backing up the data, installing new clients but causes higher cost of maintenance.
MoreSubject recursion is well covered in the literature, but, nevertheless, the problem of output “tree” does not mean the client and SQL Server many baffled. So, put the problem: there is a table with the name and record id field indicating the parent identifier. Immediately fill in this table, some sort of test data:
MoreIn our time of greatest prevalence of databases were relational databases, which are the main targets of the tables and the relationships between them. Tables can solve most problems for data storage and manipulation. But in the real world entity requiring storage is not always presented in a tabular form. One of these very common types of data structures other than the table is a tree structure, where each data element is the parent and the offspring. An example of such a structure may be the structure of state enterprises, which is headed by the director (the root of the tree), his deputies, heads of departments from which are subject to certain deputies, employees of departments, which are subject to the rulers.
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Utilizing given approach each client get his own database. In such case every client`s data stays isolated from others. It simplifies backing up the data, installing new clients but causes higher cost of maintenance.
Subject recursion is well covered in the literature, but, nevertheless, the problem of output “tree” does not mean the client and SQL Server many baffled. So, put the problem: there is a table with the name and record id field indicating the parent identifier. Immediately fill in this table, some sort of test data:
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