Need a data architecture that solves today's (and tomorrow's) challenges?
If your current data warehouse is struggling under the weight of increased volume, inordinate load times, and inadequate query performance, then you've found the right web page. We'll get into the nitty-gritty, identify the issues and define a modern and enduring data analytics architecture that is best-fit for you.
Inquidia can help.
Our senior architects have experience with data architecture and planning in a variety of industries and domains. We understand both the options and uses of data, big data, or really really big data and can show you the best fit technologies for your future data analytics architecture.
We'll work with your team to evaluate options through a pragmatic lens, helping you to decide how best to leverage your existing architectural investments while paving the way for tomorrow's must-have technologies.
Sort through the technologies and options.
We'll provide a little education along the way by helping:
- Establish your architectural goals
- Look at the tiers of data in your environment, and identifying dependencies.
- Depict how and what kind of environments you need to be in...from on-premise to off-premise to cloud.
- Design a data flow, from ingestion, through processing and storage, all the way to analysis.
- Compare different methods of ingesting data into your environment, and select the ones that are right for you.
- Look at how, when, and where you'll need to process and store data, from raw files to S3 to HDFS to Analytic Databases or Relational Databases.
- Identify data processing technologies helping you choose the right tools for the job
- Identify how and where analytic access is made available...and how data science plugs in.
We'll work with you to get a plan in place.
Once we know the architectural requirements and help you understand the options and their implications, we'll define an architecture and a plan to get there. You'll get more than a simple schematic...
- Recommended data ingestion, management, processing and analytics platforms and purpose
- Capacity planning and sizing
- Critical data design considerations
- Administration and resource requirements
- Legacy integration and/or migration plans
- Implementation dependencies and roadmap