Storage that grows without chaos
We organize raw, curated, and serving layers so large datasets remain discoverable and cost-aware.
Big Data Platform
Scalable Data Intelligence for Massive Workloads
Process high-volume, high-velocity data with distributed systems built for real-time analytics, reliable storage, and business impact.
TB-PB
Data Volume
Real-Time
Stream + Batch
Elastic
Compute Scale
Overview
Built for Scale, Variety, and Speed
Sachak helps teams design big data platforms that store diverse datasets, process massive workloads, and serve analytics without slowing down the business. The result is a foundation for real-time decisions, advanced analytics, and future AI initiatives.
What We Solve
Make Large Data Practical, Governed, and Fast
Batch and streaming in one platform
High-volume events, logs, transactions, and telemetry can support both historical analysis and live monitoring.
Query paths designed for users
Analysts, applications, and data science teams get the right serving layer instead of waiting on slow raw storage.
Delivery Blueprint
How We Shape the Platform
Size Workloads
Profile data volume, concurrency, latency, retention, and query behavior.
Design Lakehouse
Define storage layers, table formats, partitions, catalog strategy, and governance.
Build Processing
Implement batch, streaming, orchestration, and serving jobs for target use cases.
Optimize Cost
Tune compute, retention, compaction, query patterns, and monitoring over time.
Core Capabilities
Built for Distributed Processing and Lakehouse Analytics
Distributed Processing
Run parallel computation across scalable clusters for large datasets.
Streaming Analytics
Process high-velocity events with low-latency pipelines and monitoring.
Lakehouse Storage
Unify raw, structured, and curated data for analytics and AI workloads.
Architecture
Big Data Platform Architecture
Workflow
From Raw Data to Scalable Intelligence
Ingest at Scale
Load structured, semi-structured, and unstructured data from many sources.
Store Efficiently
Organize data in lakehouse layers with scalable object storage and table formats.
Process Fast
Run batch and streaming jobs across distributed compute engines.
Serve Insights
Enable high-speed query, dashboards, APIs, and machine learning workloads.
What You Receive
A scalable platform for heavy data workloads
We help you move from isolated storage and slow jobs to a platform that supports analytics, AI, and live operations.
- Lakehouse architecture and storage layers
- Batch and streaming processing patterns
- Query acceleration and serving design
- Cost, retention, and operations guidance
Lakehouse Design
Raw, refined, curated, and serving layers with governance and lifecycle rules.
Processing Jobs
Distributed batch and streaming jobs designed for volume, latency, and reliability.
Query Layer
SQL access patterns, performance tuning, and data products for business users.
Cost Controls
Retention, compaction, cluster sizing, monitoring, and workload management guidance.
Solutions
Big Data Use Cases
Lakehouse Platforms
Build unified storage and compute layers for analytics and AI teams.
Real-Time Analytics
Analyze events, logs, transactions, and telemetry as they arrive.
High-Speed Query
Make large datasets explorable with interactive query engines.
Cost Optimization
Right-size storage, compute, orchestration, and retention strategies.
Expected Outcomes
What a Better Big Data Platform Enables
Datasets become manageable across storage, processing, and analytics layers.
Queries and pipelines are aligned with how teams actually consume data.
Compute and storage spend becomes visible, tunable, and easier to control.
The same foundation can support BI, AI, data science, and real-time products.
Technology Stack
Enterprise-Ready Building Blocks
Ready to scale your data platform?