Big Data Engineering
Big Data engineering has a huge impact on the success of any business solution. Big Data engineering is a complex and constantly changing ecosystem, and choice of infrastructure impacts the business value derived from your data. Noah Data understands that even Top Enterprises would find it challenging, to identify the right combination of tools & technologies from the explosion of choices available in the Big Data ecosystem.
We help clients understand this ecosystem and the myriad technology choices available and devises strategic road map, to help them leverage and implement the right “best fit” solution. Through a well-phased Agile Managed Services Framework covering inception through roll-out services – Strategic road map recommendations, Architecture Advisory, Pilot/Prototype and Implementation, Noah Data accelerates the delivery of strategic Big Data initiatives.
At Noah Data, we leverage our end-to-end Big Data Engineering and Analytics solutions delivery expertise, to identify the right Big Data Infrastructure that meets your business objective, time and IT budget constraints. Our Big Data Infrastructure services accelerate the time to value from your Big Data deployments, without disrupting your legacy infrastructure and maintaining TCO. We offer vendor-neutral recommendations, and have expertise in both ISV and open source products.
Native and distributed versions of Hadoop – Cloudera, HortonWorks and MapR
Wide Column – HBase, Cassandra | Document – MongoDB | Key Value – Amazon Redis | Graph – Neo4j
Big Data Ecosystem components
Apache Spark (Streaming & Batch), Apache Storm, Apache Kafka, Apache SOLR, Elastic Search, Hive, Pig, Sqoop, Flume, Yarn, Oozie, ZooKeeper, Hue
AWS (RDS, Redshift, EC2, S3), Google, Azure
Agile Managed Services Framework
Note: Based on the quantum of enhancement/ business alignment required, only implementation phase or both Pilot/Prototype and Implementation phases are iterated in every sprint
Work closely with business stakeholders and understand key business requirements. Demonstrate business value through Big Data use cases (improve customer experience, optimize operations, reduce churn, manage risks etc.) that aligns with the business requirements. Devise a strategic road map, evaluate and recommend technology choices, after analyzing existing infrastructure and performing readiness assessment.
Conceptualize the Big Data Solution through solution architecture augmented with technologies and infrastructure that aligns with the business strategy.
Based on the quantum of enhancement/ business alignment required, only implementation phase or both Pilot/Prototype and Implementation phases are iterated in every sprint
Deliver Proof of Concept (PoC) and design and develop an end-to-end pilot/prototype application that demonstrates business value, technology capability, and gives a fair idea about the time-to-value. Test the pilot/prototype and then obtain sign-off from business.
Develop the low-level design and database design, and then extend the pilot/prototype application developed by writing additional program as required. The existing systems would then be integrated and data would be migrated, before deployment and testing. Sign off from QA team is obtained.
Cut over activities would be performed and production environment would be setup in parallel. The QA signed off application would then be moved to production. The application is maintained and administered for the warranty/maintenance period duration.
- Early risk reduction approach
- Iterative and Incremental delivery of valuable chunks within weeks
- Closer alignment with business needs
- More clarity and control over requirements