Most organizations have more data than insight. At Kepler Megabyte, our data engineering and analytics practice helps client companies close that gap — building the pipelines, platforms, and analytics capabilities that turn raw operational data into reliable, decision-ready information.
Our work spans the full data lifecycle. On the ingestion side, we design and operate batch and streaming pipelines using tools like Apache Airflow, dbt, Apache Kafka, AWS Glue, Azure Data Factory, and Google Cloud Dataflow. We integrate cleanly with operational systems — ERPs, CRMs, transactional databases, SaaS platforms, and event streams — so data flows reach the warehouse with the quality, lineage, and governance enterprise users expect.
For storage and modeling, we build modern lakehouse and warehouse architectures on Snowflake, Databricks, BigQuery, Redshift, or Synapse, depending on your existing footprint. We apply dimensional modeling and modern data engineering patterns such as medallion architectures, slowly changing dimensions, and event-sourced fact tables. Data contracts, automated testing, and clearly versioned transformations are part of every project — because reliability is what separates a useful platform from an expensive one.
On the analytics layer, we deliver curated semantic models, executive dashboards, self-service analytics enablement, and embedded reporting that empower business users without overloading the data team. We work across Power BI, Tableau, Looker, and modern BI alternatives, and help client organizations choose what fits their workflows best.
Beyond technical delivery, our consultants help leaders design data governance frameworks, define ownership models, and establish data quality SLAs. Together, this turns data into a business asset rather than a cost center. Whether you need a new platform built, an existing one modernized, or senior data engineers embedded in your team, Kepler Megabyte delivers the depth and discipline enterprise data work requires.