In recent years, we have not been heavily involved with warehouse projects. Most of the time, we are involved with integrating different systems from different vendors. It is not possible to use one enterprise system to solve all of a company's problems, so they usually have three or four big systems and have difficulty integrating them. We spend most of our time creating integrations and transferring small chunks of information from one system to the other. When it comes to our house and business intelligence projects, they are usually limited to reporting; people just want to collect data, aggregate it, and view it from some reports. These days, Microsoft SQL Server Parallel Data Warehouse is the most commonly used tool for our clients with Power BI. However, the cost of Power BI is expensive, so the project often does not take off. I believe the biggest problem is that these business intelligence tools are usually too expensive.

This solution helps the higher levels of the organization because they have better visibility of the whole company. This helps with decision making in terms of what should be improved or implemented.

For large scale big data analytics, we have been using this product for two years. It has inherent Hadoop integration that can refer HDFS by means of external tables. Thus, large scale historic data retention for business function improvement is quite easy, thus boosting customer confidence.

We are able to monitor daily jobs, so if there is anything that needs to be done then we can do it. If there are discussions with our clients about changes that need to be made then it is us who implements them.

It helped, initially, as a replacement for our DW DB, but later on faced issues due to concurrency, which forced the customer to use the DB as a secondary solution.

We delivered a data warehouse for Contactless and Oyster at TFL.

It has allowed fast daily loads and analysis of millions of rows of data, which eventually moved to near real-time.