Services · Real-time Data Engineering

Data that's ready
before the business asks for it.

We build streaming pipelines, data lakehouses, and transformation layers that deliver accurate, fresh data to every consumer — analysts, APIs, and AI models alike.

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<500msEnd-to-end streaming latency
2.4BDaily events processed
90%Query time reduction, lakehouse migration

Architecture

Lambda + lakehouse streaming architecture

Unified batch and streaming on a modern lakehouse — events ingested through Kafka, processed by Flink, landed in Iceberg, transformed by dbt, and served through Snowflake to every downstream consumer.

ANAGHA REAL-TIME DATA ENGINEERING ARCHITECTURE SOURCES INGEST STREAM PROC. LAKEHOUSE TRANSFORM ANALYTICS Data Sources Postgres · MySQL Kafka Connect · CDC Kafka / Pulsar Topics · Partitions Schema Registry Flink / Spark Stateful Streaming CEP · Windowing · Joins Structured Streaming Iceberg Lakehouse S3 · GCS · ADLS Apache Iceberg · Hudi · Delta dbt / Transform dbt Core · Spark SQL Data Vault 2.0 Serve Snowflake Redshift Orchestration Airflow · Prefect Dagster · Mage Data Quality Great Expectations Monte Carlo · Soda Data Catalog Datahub · Atlan OpenMetadata · Collibra DLQ & Recovery Dead Letter Queue Replay · Alerting

Our Approach

Streaming-first, not streaming-bolted-on

01

Event Schema Governance

Confluent Schema Registry + Avro/Protobuf contracts prevent producers breaking consumers. Breaking changes require a migration — not a hotfix at 2am.

02

Medallion Architecture

Bronze (raw) → Silver (cleansed) → Gold (business-ready). dbt models version-controlled, tested on every PR, and documented in a DataHub catalog.

03

Exactly-Once Semantics

Flink transactional sinks with two-phase commit to Iceberg. No double-counting in your revenue metrics after a pod restart.

04

Data Quality Contracts

Great Expectations or Soda checks wired into every pipeline stage. Anomalies alert before bad data reaches dashboards or model training.

What We Solved

Real engagements, measurable outcomes

Telecom · Event Streaming

2.4B daily events through a unified Kafka platform for a Tier-1 telco

Network events, billing events, and CDRs flowing through three separate legacy systems — no unified schema, data 4–6 hours stale, SLA breaches invisible until customer complaints.

Confluent Cloud Kafka with Avro schemas, Flink SQL jobs for real-time SLA computation, and Iceberg sinks to S3. Single data platform replacing three legacy ETL systems.

2.4BEvents/day processed
<500msEnd-to-end latency
6hr→<1minSLA breach detection
Insurance · Lakehouse Migration

Legacy data warehouse migrated to Iceberg + Snowflake

A Teradata-based DWH with 12-year-old star schemas required 48-hour data loads. Analysts waited until Wednesday for Monday's data. ML team had no feature store.

Iceberg on S3 with Spark for historical backfill, Flink for ongoing CDC ingestion, dbt for transformation, Snowflake as the serving layer. DataHub for lineage and catalog.

90%Query time reduction
48hr→2hrData freshness
$1.8MAnnual Teradata license savings
Retail · Real-time Personalization

Customer 360 from 8 source systems for a national retailer

Customer purchase, browse, loyalty, and CRM data lived in 8 separate systems. Personalization engine ran on week-old data — promotional recommendations were frequently irrelevant.

Debezium CDC from all 8 databases into Kafka, Flink event-join to build unified customer profiles in Apache Pinot for real-time lookup, Snowflake for analytics.

23%Conversion lift from personalization
<80msProfile lookup latency

Technologies We Deploy

The bench behind the build

Apache Kafka Confluent Cloud Apache Flink Snowflake Apache Iceberg Delta Lake Apache Hudi dbt Apache Spark Apache Pinot Apache Druid Debezium AWS Kinesis Airbyte Fivetran Great Expectations DataHub Astronomer / Airflow

Ready to move from batch to real-time?

We assess your current data stack and design a streaming architecture in one session.

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