Real-time event streaming

Real-time event streaming is a pipeline that ingests events (orders, clicks, in-app actions, payments, device signals) and delivers them as a real-time stream to consumer systems: analytics, anti-fraud, personalization, monitoring, ML models. We set up a stream broker (Apache Kafka, Yandex Data Streams, RabbitMQ, etc.), producers and consumers, the event schema, processing, delivery and monitoring. Honestly about the main point: this is serious engineering infrastructure, not "flip a switch". It is justified when latency is truly critical and measured in seconds: anti-fraud during payment, dynamic pricing, risk scoring, equipment monitoring, here-and-now recommendations. For most business tasks (reports, campaigns, segments) real time is not needed — ordinary analytics and hourly/daily loading is enough and many times cheaper; we will not push streaming "just because". It is costly beyond setup: the stream must be kept alive — servers/cloud, monitoring, on-call, failure and re-delivery handling. Delivery guarantees are trade-offs: "do not lose" and "do not duplicate" cannot both be achieved instantly; a model is chosen (at-least-once/exactly-once) for the task, each with its own cost and complexity. Data quality decides: malformed or incomplete events in the stream mean malformed decisions at consumers in real time (garbage in, garbage out — just faster). Your developers and system access are needed — this is joint engineering work, not "setup from outside". The platform/cloud is your cost, separate from our price for design and implementation. Important about the law: events often contain personal data (anti-fraud, personalization), and stream services can be cloud-based and third-party — a check against 152-FZ/GDPR and localization is needed; the data controller and consent are on your side, we advise. Note platform dependency too: for example, Yandex Data Streams is a Yandex ecosystem, cloud solutions can carry vendor lock-in, migration cost and a risk of restricted availability in the RF — the platform choice affects long-term costs. And after launch the stream needs constant attention: when the event schema or code changes, producers break, so continuous quality monitoring is required, not "set and forget". If you have no real need for second-level latency, few events or no team to operate it, streaming is premature and expensive; we will honestly suggest batch analytics or reverse-ETL. Picture this: a suspicious payment is analyzed by anti-fraud within a fraction of a second before confirmation, not in a nightly report after the money is gone. The base price starts from 50,000 ₽; it depends on the event volume, delivery guarantees and your infrastructure.

• Data reaches analytics with hours of delay, but the decision is needed now.
• Anti-fraud/risk scoring fires after the fact, when it is too late.
• Events from different systems are not gathered into a single stream.
• You do not know whether you need real time or ordinary loading is enough.

• Design and setup of a stream broker (Kafka / Yandex Data Streams, etc.)
• Producers and consumers, event schema and validation
• Delivery to consumers: analytics, anti-fraud, personalization, ML
• Choosing a delivery-guarantee model (at-least-once / exactly-once) for the task
• Stream monitoring, failure and re-delivery handling
• Input event-quality control (anti-GIGO)
• An honest assessment of whether you need real time or batch loading is enough
• Documentation of the event schema and operations

• Events reach consumers in seconds, not hours
• Anti-fraud and personalization work at the moment of action
• A stream with monitoring and failure handling
• It is clear where the tech ends and event quality, your team and operations begin

• We check whether real time is really needed and assess the event volume
• We design the stream, schema, delivery guarantees and monitoring
• We implement with your developers, load-test, and launch

• Do we really need real time?

  Usually no, and we will say so honestly. Real time is justified when latency is critical in seconds: anti-fraud at payment, dynamic pricing, risk scoring, equipment monitoring. For reports, campaigns and segments, ordinary analytics and hourly/daily loading is enough — many times cheaper and simpler. We will not push streaming "just because".

• Is it "set it up and it runs"?

  No. The stream must be kept alive: servers/cloud, monitoring, failure and re-delivery handling, on-call. This is serious engineering operation (OPEX) and joint work with your developers, not setup from outside. The platform and cloud are your cost, separate from our price.

• Will data definitely not be lost or duplicated?

  It is a trade-off, not an absolute. "Lose nothing" and "duplicate nothing" cannot both be achieved instantly; a guarantee model is chosen (at-least-once or exactly-once) for your task, each with its own cost and complexity. And quality decides: malformed events in the stream produce malformed decisions at consumers, just faster.