Analytics & BI
Real-time data analytics development and implementation
I build and deploy real-time ETL/ELT pipelines: data from all sources is collected, processed and delivered to warehouses, BI and data marts automatically, without manual steps.
What is real-time data analytics
Real-time data analytics is a chain of ETL/ELT processes that collects data from various sources, processes it, and delivers it to analytics systems almost the moment it appears, with no manual exports. Data is automatically loaded into warehouses, BI systems, and analytical data marts and stays current in real time. At its core is a resilient, scalable data pipeline that handles growing load and volumes. This setup provides fresh figures for analytics, monitoring, reporting, and data-driven services.
Who it's for
What's included
Connecting sources (batch + streaming)
I connect data sources in batch and streaming modes: databases, APIs, queues, webhooks, ad accounts, CRM, and files. I set up reliable data ingestion in a single unified flow.
Real-time ETL/ELT
I build data loading and processing in real time. I choose between classic ETL and modern ELT with in-warehouse processing depending on the tasks and the load.
Stream event processing
I set up event stream processing through Kafka: filtering, enrichment, aggregation, and preparing data on the fly, before it reaches the data marts.
Delivery to DWH, BI, and data marts
I ensure automatic delivery of processed data to warehouses, BI systems, and analytical data marts. The data becomes available for reports and services with no manual steps.
Monitoring, logging, retry, and alerts
I build monitoring and logging into every stage of the pipeline, retries on failures, and error notifications. The setup stays observable and predictable.
Scaling for load
I design the pipeline to withstand growing volumes and peak loads. High-load data streams are processed without losing stability.
How it works
- 01
Discussing sources and requirements
We go through the data sources, expected volumes, latency requirements, and the target architecture. I capture the use cases and the load.
- 02
Designing the architecture
I choose between ETL and ELT and pick a stack to match the tasks: streaming, storage, orchestration. I design the data layers and routes.
- 03
Connecting sources
I set up data ingestion from all sources in batch and streaming modes via APIs, webhooks, CDC, and queues.
- 04
Stream processing and data marts
I implement event stream processing and build analytical data marts for specific metrics and reports.
- 05
Delivery to BI and services
I set up automatic data delivery to BI systems, warehouses, and adjacent data-driven services.
- 06
Monitoring, resilience, and support
I add monitoring, logging, retry, and alerts. I ensure the pipeline runs reliably and keeps evolving over time.
Real-time analytics architecture
Data flows from left to right: sources to ingestion to stream processing to storage on ClickHouse, Redis, and S3 to consumption in BI, data marts, and monitoring; cross-cutting services run alongside - orchestration on Airflow, monitoring, and retry. The setup is assembled from open-source components and deployed locally or in the cloud.
Sources
The systems and streams data comes from: databases, APIs, CRM, ad accounts, queues, webhooks, and files. This is the entry point of the whole setup - both batch exports and real-time events.
Ingestion (Kafka / CDC / webhooks)
Ingesting data as streams and batches: events through Kafka, database changes through CDC, external events through webhooks and APIs. It ensures reliable data delivery into processing.
Stream processing
Processing events on the fly: filtering, enrichment, aggregation, and preparation for loading. This is where raw streams turn into data ready for marts and analytics.
Storage (ClickHouse + Redis + S3)
ClickHouse holds analytical data and marts for fast queries, Redis speeds up access to hot data and caches aggregates, and S3 stores raw data and history. Together they cover both operational analytics and long-term storage.
Consumption (BI, data marts, monitoring)
BI dashboards, analytical data marts, and monitoring systems run on top of the storage. Data is available in real time with no manual exports or recalculations.
Cross-cutting services
These run across every layer: orchestration on Apache Airflow, monitoring and alerting, logging, and retries on failures. They make the pipeline observable and resilient to errors.
Tech stack
Clients
Testimonials
Fresh data is worth the most where decisions are made fast. I design and implement real-time data analytics so that data from all sources is collected, processed, and delivered to analytics automatically, with no manual exports or discrepancies in the numbers. As a result, BI dashboards, data marts, and services run on current data, and the team spends its time on analysis rather than on assembling reports.
Shall we discuss your task?
FAQ
What data sources can be connected? +
I connect databases, APIs, CRM, ad accounts, queues, webhooks, files, and external services. I work in both batch and streaming modes - depending on how data appears in the source and how fresh it needs to be.
Will the solution work for large data volumes? +
Yes. The pipeline is designed with growing volumes and peak loads in mind, so high-load streams are processed reliably. Scaling is built in at the ingestion, processing, and storage levels.
How is pipeline stability controlled? +
Monitoring and logging are built into every stage, along with retries on failures and error notifications. This lets you see the state of the setup and react to issues before they affect reporting.
Can you help with the architecture and technology choices? +
Yes. Before we start, we go through volumes, load, latency requirements, and analytics tasks, and on that basis I pick the optimal stack and architecture. I don't push redundant technologies where a simpler solution does the job.
What's the difference between batch and streaming, ETL and ELT, and when do you choose which? +
Batch processes data in portions on a schedule, while streaming processes it as a flow, as events appear; real-time usually calls for streaming. ETL processes data before loading it into the warehouse, while ELT loads the raw data and transforms it inside the warehouse, which is convenient for flexible analytics on large volumes. The specific choice depends on the sources, latency requirements, and load - I make it during the design stage.
What latencies are achievable? +
The setup is built in near-real-time mode: data appears in analytics almost immediately after it's created. The actual delay depends on the sources, volumes, and load, so I commit to specific figures after analyzing the conditions rather than promising them upfront.
Can a solution be built on top of an existing warehouse and BI? +
Yes. I build a real-time pipeline and data marts on top of an already running warehouse and BI without breaking current processes. The new data delivery setup is embedded alongside the existing one and gradually takes over the streams you need.