Data Architecture

Finding right data storage, modeling and processing architecture is never easy and off the shelf solution won’t work in most of the situations. Technology is changing at such a fast pace that finding right combination of tools that work together are getting extremely complex. And not to mention all the performance, scaling, maintenance issue comes in any software solution, especially in big data solution. We can help you create robust modern data architecture, tailored to fit your industry and you while optimizing productivity of development team and minimizing overall cost of solution.

Keeping an eye of price

Data architecture is as important as defining end goal. Imagine a race where there is no finish line or it is unknown, so how a racer would validate how much his efforts are transforming into success or toward winning the race. Same way every big data solutions should clearly identify what would be end result of the project, and not just end goal, also the problem needs to be solved. Veloxcore can help you not only develop solutions but also assess business goals. We will identify measurement metrics, including end result validation plans and how it should be tested. From very beginning of the project we take end result into account and re-validate them after each milestones reach to keep a check on solution developed vs actual end result.

Data capture strategy

We talked about the architecture part, but what about the data part of the Data Architecture. Don’t worry. We go through your today’s data storage strategy, current pool of data available and identify what additional data you need to capture in order to fully leverage data driven organization goals and with today's technology we are absolutely certain that we can capture any type of data for current or future data analytical challenges.

Technology can’t be ignored

After identifying requirement, existing data stores and new data capture scenarios, we will take decision on the technology stack suitable for your solution. We bring our experience and expertise to the table and identify/divide large chunk of work to smaller, easily manageable pieces of projects, leading to better understanding of each component.

Creating roadmap beforehand in coordination with client is most productive as we discuss, prioritize and eliminate functionality which will make it through the product. Here we don’t just decide yes or no answers but decide best pattern and practices used in industry, and we also try to make client understand all these to see implications of it.

To wrap it up we develop a plan of action along with scope, resource planning, timeline. Also we will keep refining our requirement backlog as we progress through iterative Agile development approach.

• Where to store your data? Data Warehouse? Data Lake? SQL or No-SQL Database? HBase? MongoDB?
• On-premise or Cloud? Distribution?
• Data processing? Map-reduce? Spark?
• Data ingestion? Native HDFS commands? Flume? Storm?
• What about existing data stores Oracle, MS SQL Server or DB2?
• Should you go all in Hadoop or hybrid model?