Real-time Data Streaming Services
Act on orders, payments and events within seconds, not after tomorrow's report.
Stack we work in
- Apache Kafka
- Databricks
- Python
- Kubernetes
- Terraform
- Grafana
- Projects delivered
- 680+
- Clients served
- 310+
- Client retention
- 98%
- Certified engineers
- 20+
- Building since
- 2018
Figures as of Oct 2026
Trusted by
- Aralia
- Rosa Pistrina
- Lashbo
- Micromunchi
- Payroll Ninja
- POD
- Petdx
- Yokey
- Spin Up
- Starwear
- Udlins
- Vegpal
- Wander Wings
- Wish It
- Wisl
Signs you need streaming
Real-time data streaming lets your business react while things are happening: flag a suspicious payment, update stock as orders arrive, alert a driver, or refresh a dashboard within seconds.
Read the full overview
We design, build and run streaming platforms on Apache Kafka, Confluent Cloud, Amazon Kinesis and MSK, Google Pub/Sub and Azure Event Hubs. We capture changes from your databases with Debezium CDC, process streams with Apache Flink, Kafka Streams or Spark Structured Streaming, and land clean events in your warehouse or lakehouse. Schemas, delivery guarantees, monitoring and replay are part of the design, so the platform stays reliable as volumes grow.
Decisions made on yesterday's data
Stock, pricing or fraud checks run on figures that are hours or a day old.
Nightly loads that keep growing
Full reloads take longer every month and sometimes miss the morning deadline.
Heavy queries on production databases
Reports and integrations poll live databases and slow down your application.
Point-to-point integrations
Each system calls every other system directly, and one change breaks three integrations.
A Kafka cluster nobody owns
Kafka was set up for one project and now runs without schemas, monitoring or a plan.
Outcomes
What you get
- Events available to dashboards, alerts and applications within seconds
- Database changes captured with CDC instead of nightly full reloads
- Schemas enforced at the edge, so bad events do not break consumers
- Monitoring of lag, throughput and failed messages with alerting
- The ability to replay history when logic changes
What we deliver
What you receive
Streaming architecture
Platform choice, topic design, event contracts, delivery guarantees and retention rules, written down.
CDC and connectors
Debezium or managed CDC from your databases, plus sink connectors to your destinations.
Processing jobs
Flink, Kafka Streams or Spark jobs for enrichment, aggregation, alerts and deduplication.
Schema governance
Schemas in a registry with compatibility rules and review in source control.
Monitoring and runbooks
Dashboards and alerts for lag, errors and latency, plus runbooks for failure and replay.
Infrastructure as code
Clusters, topics, connectors and permissions defined in Terraform and deployed through CI/CD.
Event streaming platforms
- Apache Kafka and Confluent Cloud design and setup
- Amazon Kinesis and Amazon MSK
- Google Cloud Pub/Sub and Azure Event Hubs
- Redpanda for low-latency, Kafka-compatible workloads
- Topic design, partitioning, retention and sizing
Built with
- Apache Kafka
- Confluent Cloud
- Redpanda
- Amazon MSK
- Google Cloud Pub/Sub
- Azure Event Hubs
Change data capture
- CDC from PostgreSQL, MySQL, SQL Server, Oracle and MongoDB with Debezium
- Kafka Connect source and sink connectors
- Outbox pattern for reliable events from applications
- Initial snapshots plus continuous change streams
Built with
- Kafka Connect
- Debezium
- SQL
Stream processing
- Apache Flink and Flink SQL jobs
- Kafka Streams and ksqlDB
- Spark Structured Streaming on Databricks
- Windowing, joins, deduplication and late-arriving data
- Real-time features and alerts for fraud, stock and operations
Built with
- ksqlDB
- Kafka Streams
- Apache Flink
- Spark Structured Streaming
- Databricks
- SQL
Delivery & operations
- Streaming into Snowflake, Databricks, BigQuery, ClickHouse and Iceberg tables
- Schema Registry with Avro or Protobuf contracts
- Exactly-once and at-least-once delivery design
- Consumer lag, throughput and error monitoring
- Batch-to-streaming migration and replay strategy
Built with
- Schema Registry
- Databricks
- ClickHouse
- Avro
- Protobuf
- 01IngestBatch, CDC and streams
- Apache Kafka
- Kafka Connect
- Kafka Streams
- Debezium
- 02StoreWarehouse and lakehouse
- Fabric Real-Time Intelligence
- Snowflake Snowpipe Streaming
- Databricks
- Google BigQuery
- 03TransformModels and tests
- Spark Structured Streaming
- Apache Flink
- Amazon Managed Service for Apache Flink
- Python
- 04OrchestrateScheduling, CI/CD, catalog
- Terraform
Why teams choose Equitysoft for real-time data streaming
One stream live first
We put a single use case into production end to end before building more, so you see value and real numbers early.
Honest about batch
Where hourly or daily data is enough, we say so. Streaming is only used where seconds change a decision.
Replay and contracts by design
Schema Registry contracts, delivery guarantees and a replay strategy are part of the design, not an incident fix.
Load tested before go-live
Load tests, lag alerts and runbooks are in place before launch. 50+ engineers, 20+ of them certified on cloud and data platforms, with hours that overlap the US, UK and Europe.
How we work
Discover
We list the decisions that need fresh data, how fresh it must be, and the volumes involved.
You receive Use case and latency requirements
Design
Platform, topics, schemas, delivery guarantees and retention are agreed and written down.
You receive Streaming architecture and event contracts
Pilot
We put one real source through capture, processing and delivery to prove latency and cost.
You receive One stream live end to end
Build
Connectors, processing jobs and infrastructure are built as code and deployed through CI/CD.
You receive Production pipelines as code
Harden
We test failure and replay scenarios and set up monitoring before go-live.
You receive Load tests, alerts and runbooks
Run
We support the platform in production or train your team to run it.
You receive Managed support or handover
Tech stack
Technologies we use
Data platforms
Pipelines and orchestration
Languages
Cloud and DevOps
Other tools
Engagement models
How we can work together
Project-Based Delivery
A defined scope, timeline and deliverables, from design through go-live.
Fixed-Price Implementation Projects
An agreed price for a clearly scoped implementation, such as a migration or a new warehouse.
Contract and Subcontract Engineering
Our engineers work under your contract, or as a subcontractor to consulting firms and system integrators.
Dedicated Data and AI Engineers
Data engineers, data scientists and analysts who join your team and work in your tools and processes.
Remote Consulting and Development
Consultants and developers working remotely, with overlap in your working hours.
Architecture and Technical Advisory
Architecture reviews, platform choices and design guidance for your own team.
Production Support and Maintenance
Monitoring, incident handling and ongoing fixes for the pipelines, models and dashboards you run in production.
Proof of Concept and Assessments
A short, fixed-scope engagement to test an approach on your data, prove a model or plan a migration.
Questions, answered
Not for everything. Monthly finance reports are fine as batch. Streaming pays off where a delay costs money: fraud checks, stock levels, pricing, logistics tracking, customer alerts and live operations dashboards. We help you decide which flows need seconds and which can stay hourly or daily.
Self-managed or Amazon MSK Kafka gives control and the widest ecosystem. Confluent Cloud removes most of the operations work. Kinesis and Pub/Sub are simplest when you are already all-in on AWS or Google Cloud. We compare them on volume, latency, team skills and running cost.
CDC reads changes from a database's log as they happen and publishes them as events. Your warehouse and other systems stay in sync within seconds, without heavy queries against the production database. We usually use Debezium with Kafka Connect, or managed CDC from your cloud provider.
Apache Flink and Flink SQL for stateful, high-volume processing; Kafka Streams or ksqlDB when logic sits close to Kafka; and Spark Structured Streaming when the team already works on Databricks.
Replicated topics, idempotent producers, transactional or exactly-once processing where the tools support it, and idempotent writes at the destination. Dead-letter topics catch bad messages so they can be fixed and replayed.
Every topic has a schema in a Schema Registry with compatibility rules, so producers cannot publish changes that break consumers. Changes go through review like code.
Yes. We stream into Snowflake with Snowpipe Streaming, Databricks Delta tables, BigQuery, ClickHouse and Iceberg tables, so analysts see fresh data in the tools they already use.
Yes, in stages. We usually start with CDC for the busiest tables, run streaming alongside batch, compare results, then retire the batch jobs once numbers match.
We track consumer lag, throughput, error rates and end-to-end latency with Prometheus, Grafana or your cloud's monitoring, with alerts for stalled consumers and growing lag.
A pilot with one source live end to end usually takes 3 to 6 weeks. A production platform with several sources and processing jobs takes 2 to 4 months, depending on sources and security reviews.
It depends on volumes, the number of sources and how much processing logic is needed. Cloud or Confluent usage is billed to your own account. As a guide, a design and pilot usually costs $5,000 to $15,000, and a production streaming platform costs $20,000 to $70,000 or more (USD).
We start with one use case and its latency needs. We need access to the source databases or applications, a contact in each team that produces events, and someone from operations. After launch we hand over runbooks or run the platform under managed support.
More about our Real-time Data Streaming
Working togetherHow we work with your team
Start with one stream
A single valuable stream goes live first, so latency and cost are proven before scaling.
Application teams involved
Event design affects the teams who publish events, so we agree contracts with them early.
Batch where batch is fine
We only stream the flows that benefit from it, to keep the platform simple and cheap.
You own the platform
Code, configuration and documentation live in your repositories and cloud accounts.
Where streaming fits best
Financial services and fintech
Payment events checked for fraud and limits within seconds, with every event kept for audit.
Retail and ecommerce
Live stock, orders and pricing across web, stores and marketplaces.
Logistics
Vehicle and shipment events for live tracking, ETAs and exception alerts.
SaaS and IoT
Product usage and device telemetry processed as it arrives for alerts and live dashboards.
Talk to a streaming engineer
Tell us which events matter and how quickly you need to act on them.