Data Engineering & Analytics
Accelerate Insight to Action: Intelligent Data Engineering and Analytics Powered by AI and Automation.
Introduce Order and Intelligence to the Entire Data Lifecycle.
Mushroom Solutions’ Intelligent Data Engineering and Analytics (IDEA) offerings is a unified, commercial technology solution that brings order and intelligence to the entire data lifecycle. We simplify complexity by injecting AI and automation into every stage of your data journey, from ingestion to decision-making.
Intelligent Data Engineering
Reliably bringing data into the platform from various sources, regardless of volume or velocity.
- Multi-source ingestion (data lakes, data warehouses, real-time data streams)
- Real-time capture or batch processing
- Data federation & virtualization
- Integration with various APIs and proprietary systems (data warehouse, Cloud storage)
- Unstructured data to structured data transformation (via DocuGenX)
- Data quality Validation, Cleansing, and Normalization.
- AI-Driven data quality check
Design robust, scalable pipelines for loading data into the analytics engines, AI/ML models, or downstream applications.
- Processing high-volume batch data at scheduled intervals
- Capturing, processing, and acting upon high-velocity, low-latency data streams (e.g., IoT, sensor data, web clicks)
- Pushdown transformation capabilities (optimizing compute)
- Automated generation of complex SQL or Spark code
- Implementation of advanced data architectures like Medallion, Lambda, and Kappa
- Data quality Validation, Cleansing, and Normalization.
- Real-time monitoring & alerts for pipeline health.
Architecting the persistent data layer to be scalable, efficient, and cost-optimized.
- Implementation of modern Data Warehouses & scalable data lakes.
- Break data silos and manage end-to-end data lifecycle over Snowflake AI Data Cloud
- Designing Hybrid Lakehouse Architecture (managing raw, curated, and optimized data layers).
- Automated tiering, storage optimization, and cost control mechanisms.
- Implement of Active Governance to automate policy enforcement, track data usage, and simplify compliance
- Automating the testing, deployment, and monitoring of data pipelines and code
DataOps (Data Operations) is the intersection of Data Engineering, Development, and Operations, ensuring the fast, reliable, and secure delivery of analytics.
Data profiling and accuracy checks.
Automated lineage & versioning (via FlowGenX)
AI-driven data quality monitoring (identifying anomalies and drifts)
Integrating DataSecOps practices into every stage of the data lifecycle
CI/CD: Automatic testing, deployment, and monitoring of data pipelines and code
Advanced Data Analytics
Move beyond simple reporting to utilize the full spectrum of analytics—from understanding what happened to knowing what to do next.
The Business Question: What happened?
IDEA Solution: BI Consolidation & Standardization
Single Source of Truth: Unify all disparate BI tools and data sources onto the single, governed IDEA platform.
Standard Dashboarding: Automate the creation and deployment of standardized, high-performance dashboards, ensuring accurate reporting on all Key Performance Indicators (KPIs).
The Business Question: Why did it happen?
IDEA Solution: Root Cause Analysis
Automated Drill-Down: Quickly correlate various data points to identify the underlying drivers of business outcomes (e.g., pinpointing the cause of a sales drop).
Trend and Pattern Discovery: Use diagnostic tools to analyze historical trends and data segments for a comprehensive understanding of past events.
The Business Question: Can I answer my own questions quickly and safely?
IDEA Solution: Empowering the Business User
Democratized Access: Provide intuitive, no-code/low-code exploration tools directly over governed data products.
Data Exploration: Enable business teams to rapidly query, visualize, and analyze data safely and independently, minimizing dependency on the core engineering team.
The Business Question: What will happen next?
IDEA Solution: Integrated AI/ML Capabilities
Accurate Forecasting: Leverage advanced machine learning models, trained on IDEA’s high-quality data assets, to forecast future trends (e.g., market demand, equipment failure).
Model Building Support: Support for key model types:
- Supervised Learning: Classification (e.g., fraud detection) and Regression (e.g., demand forecasting).
- Unsupervised Learning: Segmentation (e.g., customer clusters) and Anomaly Detection.
The Business Question: What is the optimal action I should take?
IDEA Solution: Actionable Recommendations
Optimization Engines: Move beyond mere prediction by analyzing forecasted outcomes and calculating the specific, best course of business action.
Decision Automation: Deliver actionable recommendations, such as suggesting dynamic pricing adjustments, optimizing supply chain routes, or personalizing marketing spend for maximum ROI.
Integrated MLOps: Automation for Model Trust
MLOps Automation
Achieve seamless integration and automation of the entire model lifecycle—from experimentation and training to production deployment and continuous monitoring.
Model Trust
Ensure model reliability through automated experiment tracking, one-click deployment, and continuous monitoring for model drift and performance degradation.
The Unified Stack: Integrating Best-in-Class Cloud, Data, and Open Source Tools
Connectors & Streams: Azure Data Factory, Event Hubs / Kafka, REST (FHIR, HL7, X12), SFTP / File Drops |
Methods: Streaming, Batch, Change Data Capture (CDC), SFF / File Fros Capture |
Lakehouse Platforms: Delta Lake, Snowflake, Azure Synapse, Amazon Redshift, BigQuery |
Cloud Storage: ADLS Gen2, Amazon S3, GCP Storage |
Databases: Cosmos DB, PostgreSQL, Cutcold |
Engine & Languages: Apache Spark, PySpark, SQL / Delta Live Tables, Snowpark, Snowflake |
Tools: Databricks Workflows, UDFs, Notebooks |
Outputs: Reusable Data Products |
Orchestration Tools: ADF Pipelines, Databricks Jobs, Airflow |
DevOps: GitHub Actions, Azure DevOps |
Security: Secrets Mgmt |
Quality Tools: DQOps / Great Expectations, Profiling, Validations |
Monitoring: SLAs – Alerts – KPIs, Cost Monitoring |
Traceability: Automated Lineage |
MLOps & Tracking: MLflow |
Modeling: Feature Engineering, AutoML |
Deployment: Batch & Real-time Scoring |
Security: Secrets Mgmt |
BI Tools: Power BI, Tableau, Looker / BigQuery |
Data Platforms: Databricks SQL, Snowsight |
Discovery & Control: Purview / Catalog, Business Glossary |
Traceability: Lineage & Impact |
Key Benefits
Our Data Engineering and Analytics services transforms data into business value.
- Self-service analytics and interactive dashboards
- Real-time data processing and monitoring
- Predictive analytics and ML-driven forecasting
- Automated data pipelines and workflows
- Unified data architecture across systems
- Reduced manual processes and errors
- Faster time-to-market with data-driven decisions
- Competitive advantage through differentiated insights
- Tangible ROI from streamlined operations and smarter investments
- Complete audit trails and data lineage
- Industry-standard security and governance
- Automated compliance reporting
70%
FASTER INSIGHTS
50%
COST REDUCTION
99.9%
DATA RELIABILITY
24/7
MONITORING