Data Engineering
Unlock meaningful insights by tapping into millions of data sources — structured and unstructured — in real time. Our advanced data aggregation and analytics solutions empower businesses to collect, process, and analyze massive datasets from web platforms, APIs, enterprise systems, IoT devices, and third-party sources. This data-driven approach uncovers trends, patterns, and opportunities that fuel smarter decision-making, optimize operations, and drive growth.
Data Aggregation Trends
90%
of the world’s data was generated in the last two years, highlighting the growing need for advanced data aggregation and analysis.
74%
of businesses say that leveraging external and unstructured data provides a significant competitive advantage.
26%
Organizations that use real-time data analytics experience an average 26% increase in operational efficiency and faster decision-making.
69%
of enterprises struggle to unify data from multiple sources, making intelligent data aggregation a top priority.
Why Pulling Valuable Insights from Millions of Data Sources Matters
In a data-driven world, businesses that can quickly harness, process, and interpret vast amounts of information hold a clear competitive advantage. With data scattered across websites, APIs, customer touchpoints, IoT devices, and enterprise systems, pulling valuable insights at scale is crucial for smarter decisions, better strategies, and faster innovation.
Insight Catalyst
83%
of business leaders say data-driven insights are critical for accelerating innovation, improving customer experiences, and driving growth.
Case studies and proof
Data engineering turns raw signals into reliable, auditable, and actionable datasets that power analytics, models, and business decisions. Our implementations span agritech phenotypic/DNA pipelines, high-velocity IoT telemetry, insurance claims repositories, marketing analytics, and financial aggregation. These case studies show how careful ingestion, schema design, quality enforcement, and lineage enable reproducible ML, near-real-time insights, and compliant reporting—reducing time-to-insight, improving model quality, and lowering operational risk.
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Insuranext
Curated claims datasets with standardized fields, provenance, and audit trails to support estimators, fraud detection, and compliance reporting.
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Planto
End-to-end pipelines that ingest device imagery and DNA reads, normalize formats, and produce validated datasets for downstream ML and QA reporting.
PaisaOnClick
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Reliable aggregation of bank feeds, application metadata, and matching signals that power lender-selection logic and reporting.
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1000X
Event pipelines and OLAP-ready marts that unify click, delivery, and conversion signals for campaign optimization and attribution.
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Fleetnext
Scalable streaming pipelines that collect telematics, perform feature extraction, and feed predictive maintenance models and dashboards.
Thought leadership
High-quality data engineering is the foundation of trustworthy analytics and reliable machine learning. Too often teams treat data pipelines as ad-hoc plumbing; instead, build them as productized capabilities with clear SLAs, contract-first schemas, and automated quality gates. This means shifting left on data quality (validation at ingestion), versioning schemas and datasets, and instrumenting lineage and freshness so consumers can trust what they query. When data is treated as a first-class product, analysts, data scientists, and product teams spend time extracting insight instead of firefighting pipeline breaks.
Real-time and batch must coexist — the choice isn’t binary. Use event-driven streaming for latency-sensitive use cases (fraud signals, telemetry triggers) and batch/ELT for canonical, auditable reporting needs. Invest in a small platform team that owns ingestion templates, schema registries, feature stores, and data contracts; they enable teams to ship analytics and models faster while enforcing governance and reproducibility. Finally, tie data engineering metrics (pipeline latency, data quality score, downstream failure rate) to business KPIs so platform investments map directly to measurable outcomes like fewer manual reconciliations, faster model retrains, and improved campaign ROI.
Product ideas
Product ideas below are practical, developer-friendly offerings that convert data engineering best practices into reusable capabilities: ingestion & validation templates, feature stores, stream processors, governance hubs, and data product marketplaces that accelerate time-to-insight and reduce operational risk.
The Unified Data Ingestion & Validation Platform standardizes how datasets enter your ecosystem—supporting files (CSV/Parquet), streaming (Kafka/pub-sub), APIs, device uploads, and third-party connectors. It provides schema-on-ingest templates (with OpenAPI/JSON-Schema/Avro support), pluggable parsers, automated normalization, and a validation layer that enforces field types, ranges, referential integrity, and business rules at the edge. Validation failures are recorded in structured error queues with context (source, raw payload, failure reason) so downstream teams can triage quickly. The platform also includes replayable ingestion runs, immutable raw stores for reproducibility, and lightweight transform DSLs so teams can express common transformations without full engineering projects.
Operational features include SLA-driven ingestion windows, monitoring for freshness/latency, and automated alerts when data falls outside confidence thresholds. It integrates with a schema registry and data catalog, registers dataset versions automatically, and emits lineage metadata so consumers always know where values came from. For regulated domains (finance, healthcare, agritech) the platform supports provenance capture, signed artifacts, and exportable audit logs. The net result: faster, safer onboarding of new sources, fewer downstream surprises, and a single, governed path from raw signals to production datasets.
The Real-time Feature Pipeline & Feature Store is designed to close the gap between raw data streams and ML model consumption by providing a unified way to ingest, transform, and serve features. It can process both streaming and batch inputs, ensuring that features are consistently derived regardless of data latency. Features are materialized into online views for low-latency inference as well as offline stores for training, making it possible to maintain parity across environments. Built-in lineage and versioning ensures that every feature is traceable from its raw source to its final state, allowing teams to reproduce models and explain outcomes.
This system dramatically reduces the friction that data scientists face when moving from experimentation to production. Instead of writing ad-hoc pipelines, teams can leverage a managed layer that enforces schema contracts, freshness guarantees, and feature consistency across training and serving. This approach shortens feedback loops for personalization, fraud detection, or operational decision-making. By integrating with orchestration and cataloging tools, the feature store provides observability on usage, popularity, and drift, allowing organizations to retire unused features and optimize costs while ensuring high reliability in real-time inference scenarios.
The Telemetry Stream Processor & Anomaly Detector offers pre-built templates that make it easier to handle high-volume, high-velocity telemetry data. It can compute rolling aggregations, sessionization, and real-time metrics while simultaneously running statistical or ML-based anomaly detection models. This ensures that unusual patterns—like spikes in latency, sudden drops in sensor readings, or unexpected user behaviors—are detected and flagged within seconds. Its modular design allows teams to adapt processing logic quickly, reducing the need for heavy engineering when business requirements evolve.
The platform goes beyond detection by automatically enriching alerts with contextual metadata, linking anomalies back to devices, user cohorts, or infrastructure components. This enrichment enables faster triage and reduces noise in operations dashboards. By plugging directly into monitoring systems, teams can set automated escalation paths, triggering workflows such as rollback, service throttling, or deeper forensic analysis. The combination of ready-made processing pipelines and integrated anomaly detection empowers teams to move from reactive firefighting to proactive reliability management.
The Data Lineage, Catalog & Governance Hub serves as the authoritative system of record for all datasets across an organization. It captures critical metadata such as dataset owners, schema versions, lineage graphs, access policies, and data quality scores. Every transformation—from ingestion to final reporting—is automatically logged, enabling teams to trace data back to its origin. This traceability is essential for compliance, reproducibility, and trust in analytics and machine learning applications. Audit logs and automated certification workflows ensure datasets meet organizational and regulatory standards before being widely consumed.
Beyond governance, the catalog creates a culture of discoverability and reuse. Teams can easily search for datasets, review quality metrics, and understand usage patterns, reducing redundant data work. PII detection and classification provide safeguards for sensitive data, while access control policies integrate directly with identity management systems to enforce security. By offering a single hub for lineage, governance, and discovery, organizations can accelerate data-driven innovation while meeting compliance and risk management obligations.
Solution ideas
Solution Ideas represent the practical, implementable approaches we design to address business challenges and technical gaps. Unlike broad strategies, they are targeted, structured concepts that can be integrated directly into products, workflows, or systems. Each solution idea is rooted in real-world use cases, built to balance innovation with feasibility, and designed to accelerate time-to-value.
Solution Idea | Detailed Description |
|---|---|
Catalog, Lineage & Access Controls | Central metadata store with dataset owners, SLAs, PII classifications, and lineage graphs. Integrates with IAM for role-based access and exports audit reports for compliance. |
Data Quality & Validation Pipelines | Automated tests (schema, null-rate, drift, referential integrity) run in CI and production; quality scores emitted to catalog and gating policies that can block downstream model retraining or reports when thresholds fail. |
Feature Store (online + offline) | Materialize feature views for training (batch) and online serving (low-latency store), ensure consistent feature join keys, version features, and provide SDKs for retrieval with TTLs and fallback logic. |
Batch ELT with Lakehouse Patterns | Orchestrated ELT that writes immutable raw layers, transforms into cleaned/curated bronze→silver→gold tables, and exposes tables via query engines (Presto/Trino, Snowflake) with partitioning and compaction strategies. |
Streaming ETL & Processing Pipeline | Event ingestion → stream enrichment → lightweight stateful transforms → sink to OLAP/feature-store; includes exactly-once semantics, partitioning guidance, and backpressure strategies. Targets: Kafka/ksql/Fluentd + Flink/Beam/Streamlit patterns. |
Schema-First Ingestion Templates | Provide source-specific templates (device JSON, bank CSV, telemetry Avro) with schema registration, automated parsing, and built-in validation rules. Templates generate ingestion jobs, sample payload validators, and CI hooks to prevent contract drift. |