PrettyWhale.ai
Solving Data Engineering’s
hardest problems
Use Cases - Immediate Results
CLOUD MIGRATION
Questions:
– Need to accelerate Cloud migration?
– Automate the conversion and adaptation of existing Ingestion Code?
– Maintain performance & traceability during migration?
– Simplify Data flow orchestration in the Cloud?
Problem: Legacy pipelines and technical debt block Cloud migration.
Solution : PrettyWhale generates Cloud-compatible ingestion code and ensures interoperability across platforms.
Results : 4X faster, zero manual recoding
SYSTEM INTEGRATORS INDUSTRIALIZATION
Questions:
– How can we reduce the technical debt that keeps growing from project to project?
– How can we deliver faster without sacrificing quality?
– How do we align our ingestion practices with modern orchestration standards (Airflow, Prefect, Dagster)?
– How do we increase our operational margins when delivery costs keep rising?
Problem: Deliverables vary across teams and projects, leading to inconsistent code quality, increased technical debt, and difficult maintenance.
Solution : PrettyWhale.ai generates standardized and industrialized ingestion pipelines, aligned with best orchestration practices (Airflow, Prefect, Dagster), integrating dependency management, testing, and documentation.
Results: Consistent code quality, streamlined maintenance, enhanced traceability, and higher operational margins through reduced delivery costs.
DATA LAKE PROJECT
Questions:
– How can I ingest multiple heterogeneous sources (ERP, CRM, IoT) without spending weeks on manual pipeline creation?
– How do I build a reliable target data model when my sources have inconsistent structures and quality?
– How can I validate data flows end-to-end without writing complex integration tests myself?
– How do I ensure type conformity, schema accuracy, and traceability across my entire Data Lake project?
– How can I industrialize my Data Lake ingestion process and reduce the delivery timeline from weeks to days?
Problem: The ingestion of heterogeneous sources (ERP, CRM, IoT) complicates pipeline creation, target schema modeling, and data flow validation.
Solution : PrettyWhale.ai automatically generates ingestion pipelines, the target data model, and integration tests — ensuring type conformity, full traceability, and data quality.
Results: Complete industrialization of the Data Lake build process — a project delivered in three days instead of six weeks.
MLOps RELIABILITY
Questions:
– How can we industrialize our training, validation, and scoring workflows without rebuilding everything manually?
– How do we eliminate the fragmentation of our pipelines across different tools, environments, and teams?
– How can we generate ingestion and preparation pipelines that are natively compatible with our MLOps stack (CI/CD, Docker, Kubernetes)?
– How do we ensure dependency management, model versioning, and dataset traceability across our entire workflow?
– How can we move from experimentation to production faster, without sacrificing reproducibility or quality?
– How do we reduce the time, effort, and risk involved in operationalizing Data Science models?
– How do we seamlessly integrate our pipelines into existing industrialization workflows?
Problem: Data Science teams struggle to industrialize training, validation, and scoring workflows, often fragmented across heterogeneous environments and tools.
Solution : PrettyWhale.ai automatically generates ingestion and preparation pipelines ready for seamless integration into MLOps frameworks (CI/CD, Docker, Kubernetes), including dependency management, model versioning, and dataset traceability.
Results: Transition from experimentation to production reduced by a factor of three, full model reproducibility, and smooth integration into existing industrialization workflows.
SUPERVISION AND OPTIMIZATION OF EXISTING PIPELINES
Questions:
– How can we maintain and monitor hundreds of ingestion pipelines built with different frameworks?
– Why is troubleshooting so complex and expensive across our heterogeneous pipeline landscape?
– How do we identify redundancies, unnecessary dependencies, and bottlenecks in our existing ingestion code?
– How can we automatically analyze and optimize all our pipelines without manually rewriting them?
– How do we standardize pipelines that were built by different teams at different times with different tools?
– How can we improve stability, observability, and performance across all our ingestion workflows?
– How do we reduce the long-term maintenance cost of our ingestion pipelines?
– How can we ensure continuous compliance with DataOps best practices across our entire pipeline ecosystem?
Problem: Companies accumulate hundreds of heterogeneous ingestion pipelines, often built with different frameworks, making maintenance, monitoring, and troubleshooting complex and costly.
Solution: PrettyWhale.ai analyzes the existing ingestion code, automatically identifies redundancies, unnecessary dependencies, and bottlenecks using performance metrics and dependency graphs, then regenerates optimized, standardized, and fully documented pipelines.
Results: 50% reduction in maintenance costs, significantly improved observability, execution performance, and pipeline stability, while ensuring continuous compliance with DataOps best practices.
Experience the Power of PrettyWhale.ai
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