Big Data · Stream Processing · Real-Time APIs · Cloud-Native

Engineering
data at
planetary scale Where every stream
becomes signal

We architect and operate data pipelines that move billions of events per day, transforming raw, high-velocity streams into the intelligence that drives your business forward.

INGEST TRANSFORM SERVE EMR SPARK HIVE
Throughput
4.2B
events / day
AWS
GCP
Latency P99
38ms
stream processing
Pipeline Health
● NOMINAL
3 regions · 12 clusters
10PB
Data processed monthly
3
Major cloud platforms
<40ms
Median stream latency
Apache Spark AWS EMR Google Dataproc Azure HDInsight Apache Kafka Apache Flink Delta Lake Real-Time APIs Snowflake Databricks GraphQL WebSockets Apache Superset Grafana Databricks BigQuery Redshift Snowflake Apache Spark AWS EMR Google Dataproc Azure HDInsight Apache Kafka Real-Time APIs GraphQL Apache Superset Grafana Databricks BigQuery Snowflake
What we build

Core
Capabilities

From raw ingestion at the edge to curated analytical layers, we architect the full data stack, built on the leading managed platforms from AWS, Google Cloud, and Microsoft Azure.

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01 / Stream Processing

Real-time Stream Processing

Sub-second ingestion and transformation of high-velocity data streams. We design topologies in Apache Kafka and Apache Flink that absorb millions of events per second without flinching.

KafkaFlinkKinesisPub/Sub
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02 / Batch Computation

Petabyte-Scale Batch Processing

Massively parallel computation on structured and unstructured datasets. We orchestrate Spark workloads on EMR, Dataproc, and HDInsight that reduce days of computation to hours.

SparkAWS EMRDataprocHDFS
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03 / Lake Architecture

Data Lake & Lakehouse Design

Architecturally sound storage layers using Delta Lake, Apache Iceberg, and Hudi. We design medallion architectures, bronze through gold, that make your data queryable, governable, and reliable.

Delta LakeIcebergS3GCS
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04 / Orchestration

Pipeline Orchestration & Monitoring

End-to-end DAG orchestration with Apache Airflow and AWS Step Functions. We instrument every pipeline for observability, lineage, SLA tracking, anomaly detection, and automated recovery.

AirflowStep FunctionsdbtOpenLineage
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05 / Analytics Engineering

Analytics Engineering & Data Modelling

Semantic layers and dimensional models that turn raw tables into business logic. dbt at the core, deployed against Redshift, BigQuery, Snowflake, or Synapse, wherever your analysts live.

dbtBigQueryRedshiftSynapse
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06 / Custom Engineering

Custom Data Platform Engineering

When off-the-shelf isn't enough. We build custom connectors, ingestion frameworks, and processing engines, deeply integrated with your existing infrastructure and tailored to your data contracts.

PythonJavaScalaRust
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07 / Data Access APIs

High-Availability Real-Time APIs

Always-on, horizontally scalable APIs that serve your data at millisecond latency. We design and operate REST and GraphQL data APIs with multi-region failover, zero-downtime deployments, circuit breakers, and SLA-backed uptime, built directly on top of your streaming and batch infrastructure.

REST / GraphQLgRPCAPI GatewayRedisElastiCache
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08 / Data Applications

Data Access & Visualisation Applications

Purpose-built internal tools, dashboards, and data portals that put your processed data directly in the hands of the people who need it. From self-serve exploration interfaces to embedded analytics and real-time operational displays, we engineer the full stack from pipeline to pixel.

ReactApache SupersetGrafanaMetabaseD3.js
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09 / Cloud Data Warehouse

Snowflake Engineering

Full-lifecycle Snowflake practice, from account architecture and virtual warehouse sizing to zero-copy cloning, dynamic tables, and Snowpark-powered ML pipelines. We design the data sharing and data mesh patterns that make Snowflake a true platform, not just a warehouse.

SnowflakeSnowparkDynamic TablesData Sharingdbt
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10 / Lakehouse Platform

Databricks Lakehouse

End-to-end Databricks platform engineering, Unity Catalog governance, Delta Live Tables for declarative streaming pipelines, MLflow for experiment tracking, and Databricks SQL for high-concurrency analytics. We run Databricks at production scale across AWS, Azure, and GCP.

DatabricksDelta Live TablesUnity CatalogMLflowDatabricks SQL
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11 / WeVi

WeVi — Visitor Intelligence

Dapter's own WeVi product turns anonymous website traffic into identifiable pipeline-ready data. It links every session directly to GA4 and enriches visitor profiles through a curated partner deanonymisation network, revealing the companies and decision-makers behind your traffic so sales and marketing can act on intent the moment it occurs.

GA4DeanonymisationPartner NetworkVisitor IDIntent DataB2B Analytics
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12 / AI Integration

AI & LLM Integration

We integrate large language models and AI capabilities directly into your data applications and pipelines by selecting and tuning across providers (AWS Bedrock, OpenAI, Anthropic, Google Vertex) to optimise for capability, latency, and cost. From RAG architectures over your data lake to real-time inference APIs and semantic search, we build AI features that perform reliably at production scale and don't blow your inference budget.

AWS BedrockOpenAIAnthropic ClaudeLangChainRAGVector DBsPrompt Engineering
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Why Dapter

Built for
enormous scale

We don't prototype pipelines, we engineer them for production at orders of magnitude you can't outgrow. Every architecture decision is made with horizontal scale, fault tolerance, and cost efficiency in mind from day one.

  • 01
    Multi-region, multi-cloud pipeline architectures that eliminate single points of failure across billions of daily events
  • 02
    Adaptive cluster autoscaling on AWS EMR, Dataproc, and HDInsight, compute follows the data, not the calendar
  • 03
    Cost engineering baked in, spot instance strategies, storage tiering, and query optimisation that cuts cloud bills without cutting corners
  • 04
    Schema evolution and backward-compatible data contracts using Avro, Protobuf, and Confluent Schema Registry
Live cluster metrics, reference architecture
4.2B
Events processed daily
10PB
Monthly data volume
38ms
P99 stream latency
99.97%
Pipeline uptime SLA
AWS EMR
78%
Dataproc
54%
HDInsight
38%
Databricks
62%
Spark 3.5 Kafka 3.7 Flink 1.19 Hive 3.1 Hudi 0.14 Iceberg 1.5 Airflow 2.9 Snowflake Databricks REST / GraphQL WebSockets Superset Grafana
Data flow, schematic representation
INGESTPROCESS TRANSFORMSERVEACTIVATE
Data Access Layer

From pipeline
to product

Your data infrastructure is only as valuable as your ability to access and act on it. We close the gap by building the APIs and applications that turn processed data into real-time intelligence your teams and systems can consume.

Always-On API Infrastructure
Multi-region active-active deployments with automatic failover, health checks, and zero-downtime rolling updates. Your data APIs never go down.
Real-Time Data Serving
WebSocket and Server-Sent Events endpoints that push live data from your streams directly to consumers dashboards, apps, and downstream systems without polling.
Self-Serve Data Applications
Internal data portals, operational dashboards, and embedded analytics that let business users explore and act on data without writing a single SQL query.
Scalable Under Any Load
Horizontally scalable API tiers backed by Redis caching and CDN edge layers, handling millions of daily requests at sub-50ms response times regardless of upstream data volume.
Live API with data access endpoints
GET /v2/streams/events/latest 200 12ms
WS /v2/streams/events/live open 4ms
POST /v2/query/aggregate 200 38ms
GET /v2/metrics/pipeline/health 200 8ms
Live data feed
2.4M
events / sec
99.99%
SLA uptime
18ms
pipeline lag
How we work

Deep engagement
is our
differentiator

We don't deploy a team and disappear. Dapter embeds at the architectural level by becoming the data engineering backbone your organisation actually relies on.

01
Embedded Partnership
Our engineers work inside your organisation by attending standups, owning OKRs, and acting as indistinguishable members of your data platform team.
02
Architecture Advisory
Quarterly deep-dives on your data architecture. We challenge assumptions, introduce emerging patterns, and keep your stack ahead of your data growth curve.
03
Build & Transfer
We design and build your production data platform end-to-end, then run intensive knowledge transfer to leave your internal team fully capable of owning it.

The rarest thing in data engineering is a partner who treats your pipeline as if their name is on the SLA.

Architecture First
Every engagement begins with a thorough architectural review, not a sprint.
Outcome Ownership
We commit to pipeline SLAs, not just delivery milestones.
Constant Communication
Daily async updates, weekly syncs, real-time observability dashboards.
Long Horizon Thinking
Decisions made for the system at 10× your current data volume.
Cloud platforms

Wherever your
data lives

Deep, certified expertise across the three major cloud providers by deployed polyglot or pure-play depending on your infrastructure strategy.

Amazon Web Services
AWS

Our deepest practice. EMR-native Spark and Hive workloads, Kinesis for streaming, Glue for cataloguing, Step Functions for orchestration, and S3-backed data lakes at exabyte scale.

EMR with Spark / Hive / Presto
Kinesis Data Streams & Firehose
AWS Glue & Lake Formation
Amazon Redshift & Athena
Step Functions & MWAA
Google Cloud Platform
GCP

BigQuery as the analytical backbone, Dataproc for managed Spark, Pub/Sub for event streaming, and Dataflow for unified batch and stream processing with Apache Beam.

Dataproc Spark / Hadoop
Pub/Sub & Dataflow (Beam)
BigQuery & BigQuery ML
Cloud Composer (Airflow)
Bigtable & Spanner
Microsoft Azure
Azure

HDInsight for enterprise Hadoop and Spark, Azure Databricks for lakehouse workloads, Event Hubs for high-throughput streaming, and Synapse Analytics as the unified analytics engine.

HDInsight with Spark / Hive / Storm
Azure Databricks
Event Hubs & Stream Analytics
Azure Synapse Analytics
Data Factory & ADLS Gen2
Start the conversation

Your data is moving.
Is your architecture keeping pace?

Whether you're scaling an existing pipeline or starting from scratch, we'll meet you where you are and build toward where you need to be.