Services
Cloud Computing
Key Industry Players who once only developed Software have realized that by integrating the Software, Platform and Infrastructure combined together, they can offer their customers superior value and technology finesse to ensure the Software performs it’s intended deliverable in the ideal conditions. While several cloud players exist in the market, the key goals to consider Cloud is to not just reduce dependency on internal servers / patching / keeping up to the ever demands of the Software Vendor, but Cloud Systems offer resilience, backup strategy, most up to date Software Version and above all, an integrated rich user experience that helps business achieve their strategic goals. The recommendation is to consider Cloud as an all-pervasive center of excellence platform and need to be part of the overall road-map towards BI excellence.
Data Processing
Our approach to data structure and analytics are different than traditional information architectures. A traditional data warehouse approach expects the data to undergo standardized ETL processes and eventually map into pre-defined schema, also known as “schema on write”. A criticism of the traditional approach is the lengthy process to make changes to the pre-defined schema. One aspect of the appeal of Big Data is that the data can be captured without requiring a ‘defined’ data structure. Rather, the structure will be derived either from the data itself or through other algorithmic process, also known as “schema on read”. This approach is supported by new low-cost, in-memory parallel processing hardware/software architectures, such as HDFS/Hadoop and Spark.
Develop Future Road-maps
Once there is an understanding about how the data is being used, both in terms of overall workload and data warehouse usage, the next step is to record that information in the context of user activity. When aspects have been identified, the development of the road-map for moving the data can begin. A holistic approach to creating the road-map for data movement would be to:
- Identify the workload(s) that need to be moved
- Scope out how to move that workload
- Understand how the different applications and databases are connected to each other
- Determine architectural investments that need to be made
It is necessary to understand that optimizing data warehouse needs to be done iteratively – again and again – because data is dynamic and changes over time. Data that is identified as hot one month/year may be considered warm or cold next month/year.