We deliver reliable, scalable, and resilient ELT & ETL data pipelines using cloud-first data stores and orchestration platforms.
Prepare your data for downstream consumption by analytics tools, machine learning/AI models, automation modules, and workflow executors. We use best-in-class data-prep, data quality assurance, and data wrangling tools and platforms to provide the necessary foundation to unlock value from your datasets.
We have deep expertise in the integration of disparate systems and applications to deliver unified data platforms primed for advanced analytics and hyper-automation. We lay a great emphasis on data mastering and identity resolution - rule-based and machine learning model-based - to provide the foundation to build a 360-degree view of your data.
Our data engineering teams have deep expertise in building batch, micro-batch, complex event processing, and steaming pipelines using best-in-class orchestration and choreography tools and platforms such as Airflow, Dagster, Spark, DBT, AWS Glue, and AWS Data Pipeline. We emphasise on maintainability and extensibility of the data pipeline architecture to ensure your data processing pipelines can evolve iteratively without disruptions.
We build high performance ETL data pipelines using widely adopted batch processing platforms such as DBT, AirFlow, Dagster et al. Our data pipeline architecture combines schema driven design approach, simplicity of SQL, and framework of orchestration to deliver extensible and robust ETL pipelines.
We build petabyte-scale, highly distributed data processing pipelines for advanced analytics and machine learning/AI use-cases with the ELT approach. The ELT architecture enables your product to iteratively build, test, refine, and release data powered features and capabilities. Our engineers have decades of collective experience in building robust and scalable ELT data pipelines using industry standard platforms such as Snowflake, Redshift, AirFlow, Dagster, Spark, Kafka et al.
We deliver analytics infrastructure which responds to the changing business needs and achieves high reliability through monitoring, observation, lineage/governance, remediation, and collaboration. We architect the pipelines to meet the availability SLAs your product commits to its users.
A ChurnAICo is an AI enabled Churn Analytics platform to help SaaS companies manage customer churn with appropriate reasoning. Our team helped ChurnAICo collect data from several systems of records, prepare time series data, train ensemble models, and inferencing using Apache Airflow, AWS Redshift, EMR, EC2 instances, and AWS RDS.