About Us
Empowering Data-Driven Success with Expertise in Data Engineering
We are certified Data Engineers from AWS and GCP, holding the AWS Certified Data Engineer — Associate and GCP Professional Data Engineer certifications. As a specialized data lake and data warehouse consultancy, we help organizations rapidly design and implement complete ETL/ELT pipelines following industry best practices in Data Engineering.
Is your company looking to transform OLTP data into an OLAP system or a centralized cloud repository — commonly known as a Data Lake? We are the perfect partner. Our data engineering team designs and implements ETL/ELT pipelines that ingest data into a data lake or data warehouse on your preferred cloud platform — AWS, GCP, or Azure — and our simplified data-driven approach gets you from raw data to insights in the shortest time possible.
Our Expertise in Data Engineering
As specialists in large-scale Data Lake and data warehouse solutions (AWS Redshift, GCP BigQuery, Azure Synapse Analytics), we deliver comprehensive pipelines that unlock the potential of your data while maintaining security and compliance.
Scalable Data Pipelines:
We design high-performance data pipelines using Apache Spark and Apache Flink (Scala / PySpark) that handle large data volumes in both batch and streaming modes.ETL/ELT Implementation:
We extract data from structured, semi-structured, and unstructured sources and load it into a data lake or data warehouse. Transformations are driven by YAML/HOCON configuration files per pipeline, keeping raw data actionable without bespoke code.GCP Iceberg Lakehouse Pipelines:
Our newest products target Google Cloud Platform specifically. The GCP Cloud SQL to Iceberg Lakehouse ELT Pipeline ingests Cloud SQL (MySQL / PostgreSQL) databases into Apache Iceberg on GCS using Dataproc Serverless — with incremental checkpoints, upserts, and Airflow/Livy orchestration. It is up to 98% cheaper than Google Cloud Datastream. The GCS Files to Iceberg Lakehouse Pipeline handles Parquet, CSV, JSON, and Avro files on GCS with checkpoint-driven incremental ingestion, idempotency guards, and full schema inference — and is significantly cheaper than GCP Managed Iceberg Tables.Data Lake Optimization:
We fine-tune Parquet file sizes to avoid excessive costs on Athena, Trino, or BigQuery SQL interfaces. For CDC data management we use Apache Hudi, Apache Iceberg, or Delta Lake depending on the requirements and cloud platform.- Cloud-Native Technologies: We leverage cloud-native and serverless architectures for scalability and cost efficiency: AWS Glue / Serverless EMR, GCP Dataproc Serverless / Cloud Run, and Azure Data Factory. For data governance, we use AWS Glue Data Catalog, GCP Dataplex, and Unity Catalog. Orchestration is handled via Apache Airflow, Dagster, Prefect, AWS Step Functions, or AWS EventBridge.
- Ingestion to Data Warehouse: Our Data Lake to Data Warehouse pipeline reads from the data lake or other structured / semi-structured sources and loads into BigQuery, Redshift, or Azure Synapse Analytics. Configuration is purely YAML/HOCON-driven — a true Do-It-Yourself approach.
- Robust Data Management: We implement data governance frameworks covering ownership, quality, and security. On AWS we use Lake Formation for row-level and column-level access control. We integrate AWS Glue Data Catalog, GCP Dataplex, and Unity Catalog for centralized access control, lineage, and data discovery.
- Data Lineage and Impact Analysis: We provide tooling to track data lineage and assess the impact of schema changes. Unity Catalog, AWS Glue Data Catalog, and GCP Dataplex all provide lineage and impact analysis capabilities out of the box.
Our Products
We deliver production-ready, highly configurable ETL/ELT codebase that you deploy into your own cloud account. Current offerings include:
- RDBMS to Data Lake — batch, incremental, and CDC pipelines from MySQL, PostgreSQL, Oracle, SQL Server, AWS Aurora, and GCP Cloud SQL into Iceberg, Hudi, or Delta Lake.
- GCP Cloud SQL to Iceberg Lakehouse ELT Pipeline — HOCON-driven Spark JDBC-to-Iceberg pipeline on Dataproc Serverless with Airflow orchestration. Up to 98% cheaper than Google Cloud Datastream.
- GCS Files to Iceberg Lakehouse Pipeline — checkpoint-driven Spark pipeline for Parquet, CSV, JSON, and Avro files on GCS with idempotency guards and full schema inference.
- Semi-Structured Sources to Data Lake — MongoDB, DynamoDB, Firestore, Cassandra, JSON/XML feed ingestion on AWS, GCP, or Azure.
- Unstructured Data to Data Lake — file-based ingestion for CSV, Parquet, Avro, ORC, and web API responses.
- Data Lake to Data Warehouse — sync curated lake tables into BigQuery, Redshift, Snowflake, or Azure Synapse Analytics.
By combining data governance and data engineering expertise, we help organizations establish a solid foundation for data-driven initiatives while mitigating risks and ensuring compliance.
We have modular, parameterised codebases ready to deploy into your organization — bootstrapping your data lake or lakehouse solution in days, not months.
