AlexP | Data Engineer

Clickhouse | dbt | Kafka | TiDB | MySQL
🔨 Real-time Data Engineer
📚 DWH, ML, streaming
🇨🇾 Build real-time, governed data platforms for FinTech

[Alexey Grigorev @Al_Grigor]

Nice to see ML Zoomcamp ranked #1 in @IAmMarinaWyss's review of machine learning courses.

Her main point was exactly what we wanted the course to be about: learning the practical parts around machine learning engineering.

She highlighted that ML Zoomcamp covers both theory and practice in one program:

- Regression, classification, evaluation metrics
- Decision trees and ensemble learning
- Neural networks and deep learning
- TensorFlow and PyTorch
- Model persistence
- APIs with FastAPI
- Docker
- Serverless deployment with AWS Lambda
- Kubernetes

The course is designed to be close to how ML work happens in practice.

Sometimes that means dealing with setup issues, deployment problems, broken containers, unclear errors, and debugging your way through them.

Thank you, Marina, for reviewing the course and for the thoughtful breakdown!

You can watch the review here:
https://t.co/jeRHh0bVpR

[Alexey Grigorev @Al_Grigor]

A new cohort of LLM Zoomcamp starts on June 8, 2026.

It’s a free 10-week course where you go from LLM basics to building a production-ready AI assistant.

For this cohort, I'll update the course content during a series of live workshops.

In the course, you'll learn:

- Retrieval-Augmented Generation
- Vector search and embeddings
- AI agents
- Function calling and tool use
- Evaluation of RAG and agentic systems
- Monitoring LLM applications

Join the new cohort and build your LLM application step by step: https://t.co/HgDzeIw0O1

[Alexey Grigorev @Al_Grigor]

Add June 8, 17:00 CET to your calendars!

https://t.co/23PVwrn5MH

[AlexP | Data Engineer @cymlai]

What I like most about this setup:

reproducible analytics
versioned transformations
tested data models
static public delivery
low-friction deployment

It’s a good pattern for shipping public data products from an engineering-first stack.

[AlexP | Data Engineer @cymlai]

Built an end-to-end analytics project for #DEZoomCamp

It turns raw GitHub repository metadata into a dashboard using:

Prefect for orchestration
ClickHouse as the warehouse
dbt for tested transformations
Evidence for the dashboard
Pages for publication

https://t.co/JW0oV7eMN0

[AlexP | Data Engineer @cymlai]

Tech stack:
• Prefect orchestrates the pipeline
• ClickHouse stores and serves analytics fast
• dbt handles transformations and testing
• Evidence turns curated data into a static analytics site
• GitHub Actions / Pages automate delivery

[Alexey Grigorev @Al_Grigor]

Got this message from a Data Engineering Zoomcamp participant.

John-Luke landed their first junior DE role while still working on the final project.

Always great to see participants finding real value in the material!

[AlexP | Data Engineer @cymlai]

Module 7 is a solid step into real-time data engineering: not just moving events, but actually extracting insight from live streams. Definitely one of the most engaging modules in the Zoomcamp so far

[AlexP | Data Engineer @cymlai]

Just wrapped up Module 7 of DE Zoomcamp - a really hands-on introduction to streaming data pipelines with Kafka-compatible tools, PyFlink, windowed aggregations and real event-driven processing.
#flink #kafka #dataengineering

[AlexP | Data Engineer @cymlai]

The most interesting part was working with tumbling and session windows. That’s where streaming starts to feel different from classic batch analytics

[AlexP | Data Engineer @cymlai]

Spark feels heavy until you start thinking in partitions + transformations + shuffle + actions
After this homework, I’m more confident in reading/writing Parquet and debugging jobs via Spark UI

[AlexP | Data Engineer @cymlai]

#DEZoomcamp (2026) Module 6 - Batch processing with Apache Spark.
This homework was a great “Spark fundamentals in practice” run: Parquet, partitions, DataFrames, Spark UI, and some real NYC taxi analytics

[AlexP | Data Engineer @cymlai]

Zone lookup join:
Load taxi_zone_lookup.csv into Spark, join on location ID, then find the least frequent pickup zone.
This is a clean join + groupBy/count exercise and shows how to enrich raw facts with dimensional data

[AlexP | Data Engineer @cymlai]

@DataTalksClub @dltHub Quick sanity checks via SQL:
- date range from trip_pickup_date_time
- % of credit card payments from payment_type
- total tips from tip_amt
If you’re learning ingestion/ELT, this workshop is a great win for you.
Course repo:
https://t.co/ptkOFfOtBf
#DEZoomCamp

[AlexP | Data Engineer @cymlai]

Just completed the @DataTalksClub Data Engineering Zoomcamp dlt workshop with @dltHub
I built a small ingestion pipeline from a paginated REST API to DuckDB using dlt.
Thread with the key steps and some insights

[AlexP | Data Engineer @cymlai]

@DataTalksClub @dltHub Another useful detail: some columns received no data during a load, so dlt couldn’t infer types.
Solution: add type hints in dlt.resource(columns).
After that, everything materializes cleanly and is easy to query in DuckDB.
#dataengineering