Data warehouses allow for data analysis, including data from a dozen or so years back. The collected data is arranged and archived in a central database which facilitates control over access to data, thereby ensuring the security of the stored data.
As time goes by, IT needs arise as a natural result of the company’s development, which is why Data Warehouse end-users should maintain constant contact with BI specialists to verify current business needs.
There are several ways to analyze the collected data, depending on actual business needs, however the most common ones are:
periodical, standard fixed-structured reporting, with enabled filtering,
defining reports and queries by means of SQL or generating automated reports,
static analysis of trends and predictions,
multi-dimensional and interactive analytical processing (OLAP),
data mining to predict business and market trends,
business modelling to predict the results of business operations.
Verified skills in Data Warehousing
JCommerce has been honoured with the title of Gold Microsoft Partner in the Data Platform field, which confirms our competences in one of the following areas:
Administration of SQLDatabase – configuration of data access, audits, backups and SQL Server instance management, implementation of SQL to Azure and mass memory management,
Development of SQL Database – building and the implementation of databases with functions, optimization, data management and database development with Transact-SQL,
Big Data – implementation and management of solutions for data storing, processing and security solutions based on Azure.
Online Analytical Processing (OLAP) is an approach which provides swift responses to multi-dimensional analytical (MDA) queries. It presents a multidimensional structure of the company. This technique constitutes a fast, consistent and interactive way to provide key information for the company.
enable users to interactively analyze data complexity from multiple
perspectives. Databases configured for OLAP use a multidimensional data model,
allowing for complex analytical and ad-hoc queries with a rapid execution time,
due to which the results are transparent and clear. Furthermore, OLAP tools
allow for smooth navigation through database structures as OLAP functions
enable a user to create cross-sections of particular dimensions and move them
using pivot tables. The user also benefits from the possibility of creating
queries, presentations and visualizing results in the form of diagrams and
The following operations make it possible to provide the highest quality information:
Data mining – delivery of detailed data on the feature which is being tested, from general to specific.
Aggregation – analysis of data from a wider perspective.
Selection – selection of data features for analysis.
Ranking of data – lineup of attributes.
Arithmetic, statistical, and econometric operations as well as selection of value, sorting and generating indicators.
Data warehouse – data marts
A data mart is the access layer of the data warehouse environment which is used to deliver data to the users. It is usually oriented to a specific business line or team, i.e. marketing or accounting.
The data should comply with the main warehouse, which is relevant as this makes the warehouse a trustworthy source for data marts. This is where ETL processes play a key role, since they allow for the integration of data from different sources, data transformation and loading it to the due database.
Separation of areas in the warehouse lets users:
speed up the operations without placing an additional burden on the main data warehouse,
analyze data from different sources,
store data in a more detailed way to facilitate and accelerate the work,
download the data in a more efficient manner,
comprehension of the structure of the data by enterprise users – they save time spent on understanding complex data structures.