What data platforms do you work with?

Snowflake, Databricks, BigQuery, Redshift, and Azure Synapse. We also work with Kafka, Airflow, dbt, and modern data stack tools. We recommend based on your needs.

Can you help with real-time data pipelines?

Yes. We build streaming pipelines with Kafka, Kinesis, or Pub/Sub for real-time analytics and event-driven architectures alongside batch processing.

How do you handle data quality?

Data quality is built into our pipelines. We implement validation rules, monitoring, data contracts, and automated testing to catch issues before they impact downstream systems.

What's the pricing for data engineering services?

Embedded team model: Precision Pod (€5-6k/month), Pair Pod (€10-11k/month), Mini-Team (€15-16k/month). All include project management and architecture reviews.

How fast can you start?

7-10 business days from signed agreement to engineer embedded in your team.

Blog - HST Solutions

Blogs & Insights

Our thoughts, perspective, and opinions

7 Critical Warning Signs Your AI Project Is Heading for Failure

10 Hidden Reasons Why Enterprise Software Projects Fail Despite Large Budgets

Clean vs Dirty Data: Measuring the Real Cost Impact on AI Model Accuracy

7 Regulatory Compliance Risks from Poor Downstream Data Reporting in Financial Services

How to Identify and Fix Data Quality Issues Before They Damage Your AI Models

What to Use Instead of Manual Data Cleaning for Enterprise AI Projects

5 Hidden Causes of Production Data Pipeline Failures Every CFO Should Know

Apache Kafka vs Traditional ETL Pipelines: Comparing Data Flow Reliability for Business-Critical Systems

How to Identify and Prevent the 7 Most Common AI Project Failures in Your Organisation

Proof of Concept vs Production-Ready AI: Comparing Failure Rates in European SMBs

How to Identify AI Project Red Flags Before They Cause Failure

AI Proof of Concept vs Production Deployment: Why 87% of Projects Never Scale

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Blogs & Insights

10 Hidden Reasons Why Enterprise Software Projects Fail Despite Large Budgets

7 Critical Warning Signs Your AI Project Is Heading for Failure

7 Regulatory Compliance Risks from Poor Downstream Data Reporting in Financial Services

Apache Kafka vs Traditional ETL Pipelines: Comparing Data Flow Reliability for Business-Critical Systems