INFOSTRUX IS A CERTIFIED SNOWPARK ACCELERATED PARTNER
Let us bring you to the top of the data mountain
Apache Spark is a significant force multiplier in the data landscape. Its complexity, however, requires a high operational overhead, which leads to cost, challenging performance optimization, and inconsistent governance and security policies.
To address those limitations while further enhancing the overall capabilities of the execution model enabled by Spark, Snowflake has introduced Snowpark, which provides Spark-like DataFrame functionality within a fully managed, secure, and scalable environment.
Infostrux brings its prescriptive approach to help you move your Spark workloads to Snowpark swiftly and reliably.
Spark Challenges
- High cost and complexity of maintaining multiple environments
- Inconsistent performance and reliability
- Lack of cluster scalability
- Rigid environment disallowing experimentation
Key outcomes
30 - 50% platform cost savings through reduction in data movement and more efficient compute within Snowflake
30 - 40% decrease in engineering effort
1.5 - 2x faster project delivery through simpler, more reliable, and easier to maintain computing environment
70 - 80% SLA shortfalls, incidents, and failures reduction by relying on Snowpark
Enhanced security, traceability, and audit capacity of data through Snowflake’s native data governance controls
Assessment and Planning
Goals
- Cost savings validation
- Validation of performance and scalability improvements
Activities
- Discovery and scoping
- Solution architecture
- Code conversion readiness evaluation
- Automated and manual code conversion scoped to PoC
- Cost savings, performance, and scalability evaluation
- Detailed end-to-end full migration plan & pricing
Delivered at no cost by Snowflake Sales Engineering and Infostrux
Full Scope Migration
Platform Readiness
- Environment provisioning
- Technology deployment
- CI/CD setup
Transformations Migration
- Automated and manual schema and code conversion and redesign
- Re-engineering of ingest and egest data flows
- Automated unit testing
- Exception handling
- Performance optimization
Data Migration
- Historical data migration
- Setup of continuous parallel run and verification
Integration
- Integration testing
- Further performance optimization
- Business Acceptance testing
Production and Operations
- Parallel runs
- Cut-over
- Services decommissioning
- Transition and Support