Going over projects and progress. Cory, Steve, and I all working together. Cory schedules these meetings and runs things past Steve and I. We bounce around a lot but get things going. Kinda fun.

One of the topics up for discussion today was a client that wanted a complete copy or clone of their entire production (live) environment. We talked about logistics of what would be needed, expected, and how to do and service those requests.

We also talked a lot about moving into aggregated totals and aggregated data (sums, averages, counts, totals, and stats). Some times these bigger projects need a plan, seed monies, and then time and resources pushed at them to really make them work. Along those same lines, we spent a good portion of our meeting today talking about what is needed to help these projects get off the ground. We talked about some basics for aggregated data and data warehousing such as - ETL - Extract, Transform, and Load - dealing with processes to work with large amounts of data. The extract option means get the raw data from the transactional database. The transform means take that data and change it or manipulate it into what you want it to be. You then load it or store it in it's transformed state. This helps make it faster for the next stage and you can get at the info quicker. This whole process is coming into play and will be part of where we are headed. We will for sure need it for our future fracture project. Good stuff.

Our plan is start with sales and inventory tracking. We will then get into aggregated data for the other main player groups (all twelve - expense/receipts, deposits, invoice, quotes, stock/units, inventory/parts, elements of time, customers, vendors, users, balance sheet items, and PO's). Some examples of where we are going are taking transactions or transactional data and going up to daily records and sums, weekly records, monthly records, quarterly records, and even annual records. All of these time based tables would get and hold sums, totals, and counts from the transactional data. It is almost like a reverse pyramid with transactional data on the bottom, then up to daily, weekly, monthly, quarterly, and annually. This starts getting into data warehousing and doing the basics of extracting, transforming, and loading into the data warehouse the data that we want. Almost the business intelligence level (BI) once you can display it in consumable manner. We will keep heading in that direction.