Data engineering and analytics, delivered end to end
Lognatech helps clients build the same class of projects that define modern data teams: batch and streaming pipelines, real-time dashboards, warehouse and lakehouse platforms, and applied AI systems. Every engagement uses the tools your teams already deploy in production: Airflow, dbt, Kafka, Spark, Snowflake, Databricks, and the major cloud platforms.
How we help clients
Whether you are modernising a legacy warehouse, adding real-time streaming, or building your first end-to-end data platform, we deliver the complete picture: architecture, pipelines, transformations, orchestration, governance, and the dashboards that make it all visible.
Data pipelines
Batch and ELT pipelines with Airflow, dbt, Snowflake, and Redshift, delivering clean, analytics-ready data at scale.
Real-time streaming
Kafka, Spark Streaming, Flink, and Kinesis pipelines for fraud detection, live dashboards, and event-driven systems.
Cloud platforms
End-to-end builds on AWS, Azure, and GCP using the exact services companies deploy in production.
Lakehouse & warehousing
Modern lakehouse architectures with Delta Lake, Apache Iceberg, Snowflake, and BigQuery.
What a Lognatech project looks like
Every project we deliver follows the same proven pattern: ingest from source, land in a raw zone, transform in layers, orchestrate with Airflow, and serve analytics-ready data to the business. Here is how the pieces fit together.
Ingest
APIs, databases, logs, and event streams land in S3, ADLS, or GCS. Streaming sources go through Kafka or Kinesis.
Transform
PySpark, dbt, and SQL models clean, join, and enrich the data. Layer it into bronze, silver, and gold zones.
Orchestrate
Airflow, MWAA, or Azure Data Factory run the pipeline on schedule, handle retries, and alert on failures.
Serve
Dashboards in Power BI, QuickSight, Looker, or Grafana. Models served to apps and ML systems.
Recent projects and client outcomes
A selection of the platforms, pipelines, and analytics systems we have delivered for clients across healthcare, finance, logistics, ecommerce, and insurance. Every project below produced a measurable return.
Streaming clickstream and personalisation platform
We built a Kafka plus Spark Streaming pipeline that ingests millions of user events per hour, scores them with a personalisation model, and writes results to Snowflake. Conversion lifted 27 percent in the first quarter, with sub-second latency for the recommendation engine.
Discuss a similar buildEnd-to-end supply chain visibility with AWS and Airflow
We ingested shipment events from carriers and IoT sensors through Kinesis Firehose into S3, transformed them with Glue and PySpark, and orchestrated the entire flow with AWS MWAA. Late deliveries dropped 22 percent and fuel costs fell 14 percent within six months.
Discuss a similar buildHIPAA-compliant lakehouse on Azure with Delta Lake
We built a medallion architecture on Azure Data Lake Gen2 with Databricks notebooks, Delta Lake tables, and Unity Catalog governance. Claims processing time dropped 40 percent and the hospital group recovered 3.1 million dollars in previously denied claims.
Discuss a similar buildReal-time fraud detection with Kafka and Flink
A streaming pipeline that consumes transaction events from Kafka, applies complex event processing in Flink, and flags suspicious activity within milliseconds. False positives dropped by a third while genuine fraud detection improved significantly.
Discuss a similar buildUnderwriting analytics platform with dbt and Snowflake
We migrated a legacy reporting stack into a modern Snowflake warehouse with dbt models, layered staging, intermediate, and mart structures, and full test coverage. Report generation time fell from hours to minutes and data quality issues dropped to near zero.
Discuss a similar buildData platform modernisation on Google Cloud
We replaced fragmented on-premise pipelines with a GCP-native stack: Cloud Storage for landing, Dataflow for streaming and batch, BigQuery for warehousing, and Looker for self-service analytics. Analysts now ship insight in hours instead of weeks.
Discuss a similar buildThe kinds of projects we deliver
If you have seen it in a modern data engineering portfolio or job description, we have built it in production. Here are the categories we work in most often.
ETL and ELT pipelines
Airflow, dbt, Glue, Data Factory, and custom Python. Batch, incremental, and event-driven patterns.
Real-time streaming
Kafka, Spark Structured Streaming, Flink, Kinesis, and Event Hubs for low-latency event systems.
AWS, Azure, and GCP builds
End-to-end platforms using S3, Redshift, Glue, EMR, ADF, Databricks, Synapse, BigQuery, and Dataflow.
Lakehouse and warehousing
Delta Lake, Apache Iceberg, Snowflake, Redshift, Synapse, and BigQuery with proper layering and governance.
BI and dashboards
Power BI, QuickSight, Looker, Grafana, and Tableau on top of clean, governed, analytics-ready data.
Applied AI and ML
Feature stores, model training, and deployment embedded directly into your data platform.
Governance and lineage
Unity Catalog, Purview, and dbt tests so you always know where your data came from and whether it is trustworthy.
Slowly changing dimensions
SCD Type 1, 2, and 3 patterns implemented correctly in Snowflake, Redshift, and Databricks.
Senior teams, proven patterns, measurable outcomes
Every project starts with a business case and ends with measurable proof. We do not sell architecture diagrams: we ship working pipelines, models, and dashboards that your teams can actually use from week one.
We use the tools your teams already deploy
Airflow, dbt, Kafka, Spark, Snowflake, Databricks, Kubernetes, and the major cloud platforms. No exotic stacks that only we can maintain.
We layer properly, from bronze to gold
Raw data lands in a governed raw zone. Cleansed and enriched data moves through staging and intermediate layers. Business-ready data sits in curated marts. Every layer is documented, tested, and owned.
We build for the long term
Monitoring, alerting, lineage, and test coverage are part of the deliverable, not an afterthought. You will still be running these pipelines confidently two years from now.
Start your project200+
enterprises trust our project teams
180+
data connectors and APIs used in production
93%
predictive accuracy across delivered models
24/7
monitoring, alerting, and support