Data Ingestion Services That Turn Scattered Sources Into Analytics-Ready Data
We design, build, and operate batch and streaming data ingestion pipelines that move data from APIs, databases, files, SaaS apps, and event streams into your warehouse, lake, or lakehouse, with monitoring, schema evolution, and governance built in from day one. Reduce integration cost, shorten time-to-insight, and keep full control over data quality, lineage, and compliance.
One Reliable Ingestion Layer for Analytics, AI, and Automation
- Batch, micro-batch, and real-time streaming ingestion (Kafka, Kinesis, Pub/Sub, CDC)
- Connectors for 200+ sources: databases, SaaS APIs, flat files, IoT, event buses
- Schema drift detection, deduplication, and automated data quality checks
- Cloud-native on AWS, Azure, GCP, Snowflake, Databricks, BigQuery
- Observability, lineage, and SLA-backed operations from pilot to production
Why Does Your Data Ingestion Keep Breaking at the Worst Possible Moment?
Most data teams hit the same wall: dozens of fragile scripts, inconsistent connectors, undocumented schema changes, and no single view of what is flowing where. The result is late dashboards, broken AI models, manual firefighting, and analysts who stop trusting the data they query.
Building Blocks of a Reliable Data Ingestion Platform
CDC agents (Debezium), API pollers, file watchers, and Kafka, Kinesis, and Pub/Sub consumers pull from every source.
A durable event bus with partitioning, replay, and exactly-once semantics moves data without loss.
Raw/bronze zone in object storage using open table formats: Iceberg, Delta, or Hudi.
Spark, Flink, dbt, or warehouse-native SQL handle typing, deduplication, and SCD logic.
Great Expectations or Soda checks, dead-letter queues, and quarantine tables stop bad records spreading.
Metrics, logs, lineage via OpenLineage, and per-pipeline SLA dashboards make failures visible early.
Catalog (Unity, Collibra, DataHub), PII classification, access policies, and audit trails.
From First Source to Production Ingestion in Weeks, Not Quarters
We catalog sources, volumes, update frequencies, SLAs, and compliance constraints. Output: prioritized source list, target architecture sketch, and a shortlist of tools that fit your stack. (1 week)
We set up the cloud foundation, event bus, storage, orchestration, and observability. Output: one end-to-end pipeline in production for the top-priority source, with CI/CD and monitoring. (2-3 weeks)
We onboard the next 10-30 sources in parallel tracks using the reference pattern. Output: production ingestion for critical databases, SaaS systems, and event streams, with documented contracts and quality checks. (4-8 weeks)
We run the platform under SLA, tune cost and latency, add sources on demand, and transfer knowledge to your team. Output: a stable, observable platform with predictable cost per source. (ongoing)
Measurable Impact of a Modern Data Ingestion Pipeline
50-70% reduction in time to onboard a new data source
60-80% fewer pipeline incidents after moving to CDC and schema-evolution-aware ingestion
Hours to seconds data freshness on priority streams
30-40% lower total cost of ownership vs. fragmented, hand-rolled ingestion
Who This Is For
What We Deliver
We turn fragile scripts into a production-grade data ingestion platform: one pipeline for batch and streaming, connectors for 200+ sources, quality and schema handling built in, cloud-native infrastructure as code, and governance from the first release.
Frequently Asked Questions
Data ingestion is the process of moving data from source systems (databases, APIs, files, event streams) into a destination like a data warehouse, lake, or lakehouse where it can be queried and analyzed. It covers both batch loads and real-time streaming, and usually includes light validation, typing, and deduplication before the data lands.
Data ingestion is a subset of ETL focused on the extract and load steps. Ingestion moves raw data into a landing zone as-is or with minimal transformation; ETL/ELT then turns that data into modeled, business-ready tables. Modern architectures separate the two so ingestion can be standardized and reused across many downstream transformations.
Use both, chosen per source and use case. Batch suits reference data, large historical loads, and systems with infrequent changes. Streaming (via Kafka, Kinesis, Pub/Sub, or CDC) is needed when freshness matters: fraud detection, personalization, operational dashboards, and real-time AI. A good platform supports both with consistent contracts.
We work with the full landscape: managed tools like Fivetran, Stitch, and Matillion; open-source platforms like Airbyte, Debezium, Kafka Connect, and Meltano; streaming engines like Apache Flink and Spark Structured Streaming; and warehouse-native features in Snowflake, Databricks, and BigQuery. Tool choice follows your stack, cost profile, and compliance needs, not the other way around.
We detect schema drift automatically at ingestion time, version every schema, and route breaking changes to a review workflow instead of failing silently. Additive changes such as new columns propagate automatically; destructive changes such as dropped or retyped columns require explicit approval. Downstream consumers are notified through the data catalog before anything reaches production.
Typically 2-3 weeks for the first end-to-end production pipeline on a new platform, and 4-8 weeks to onboard the next 10-30 priority sources in parallel. Timelines depend on source complexity, access to systems, and compliance scope. We deliver in iterations, so you see working production ingestion early rather than after a six-month build.
Yes. We are cloud- and warehouse-agnostic and deploy on AWS, Azure, or GCP, landing data into Snowflake, Databricks, BigQuery, Redshift, or an open lakehouse on Iceberg or Delta. We integrate with your existing IaC, CI/CD, identity, and observability stack so the platform fits your engineering standards.
Ready to Replace Fragile Scripts With a Reliable Data Ingestion Platform?
Book a free, no-obligation 30-minute consultation. We review your current sources, pain points, and target stack, then walk you through a reference data ingestion architecture for your environment, with realistic timelines and cost ranges.
Discovery call
A 30-minute review of your current sources, pain points, and target stack.
Reference architecture
We map a data ingestion architecture for your environment, with realistic timelines and cost ranges.
Pilot pipeline
We ship one end-to-end pipeline in production for your highest-priority source.