Transform Raw Data into Actionable Intelligence
Data Lake & Data Warehouse Architecture
Design Modern, Scalable Data Platforms. We design and implement both data lakes and data warehouses based on your analytics and storage needs. Whether it’s raw unstructured data or highly structured business-critical data, our architects ensure optimal storage, performance, and cost-efficiency. Modern data lake solutions using AWS S3, Azure Data Lake, or GCP Cloud Storage. Data warehouse design using Snowflake, Redshift, BigQuery, Azure Synapse. Batch and real-time ingestion pipelines. Schema design, partitioning, and indexing strategies. Scalable and secure architecture to support diverse workloads.
ETL/ELT Pipeline Development
Automated Data Ingestion, Transformation & Integration. We build robust ETL/ELT pipelines to unify data from disparate sources and make it ready for downstream analytics, AI/ML models, or dashboards. Source-to-target mapping and pipeline design. Batch and streaming pipeline development using Apache Airflow, DBT, Talend, AWS Glue, Azure Data Factory, etc.. Data cleaning, transformation, normalization, and enrichment. Error handling, retry logic, and lineage tracking. Metadata and orchestration layer implementation.
Real-Time Data Streaming Solutions
Stream and Analyze Data as It Happens. Capture and process data in real-time to enable faster decision-making and event-driven systems. We implement low-latency data pipelines that allow you to react instantly to key business events. Real-time ingestion and processing using Apache Kafka, Kinesis, Azure Event Hubs, and Spark Streaming. Streaming ETL architecture and implementation. Integration with downstream systems (data lakes, ML models, alerting engines). Scalable architectures to support high-throughput workloads. Real-time monitoring and alerting integrations
Business Intelligence & Reporting
Empower Decision-Makers with Rich, Interactive Dashboards. We bring your data to life with insightful dashboards and automated reporting systems that help business users make data-driven decisions confidently. BI tool implementation using Power BI, Tableau, Looker, or Google Data Studio. Dashboard design and data modeling. KPI tracking and executive-level reporting. Scheduled and ad-hoc report automation. Self-service analytics enablement for business teams
Deliverables for Data Engineering & Analytics Services
- Current state assessment and target data architecture
- Data lake/warehouse reference architecture diagram
- Data modeling documentation (star/snowflake schema)
- Data governance and security architecture
- Cloud storage and compute provisioning plan
- Source-to-target mapping documents
- Data flow diagrams and pipeline architecture
- ETL/ELT scripts and workflows (SQL, Python, Airflow, etc.)
- Data validation and reconciliation reports
- Orchestration & automation configuration (e.g., Airflow DAGs, Glue jobs)
- Streaming platform setup and configuration (Kafka, Kinesis, etc.)
- Real-time ingestion pipeline scripts and topics configuration
- Stream processing logic (e.g., with Flink or Spark)
- Latency, throughput, and error metrics dashboards
- Integration with storage/analytics/alerting systems
- BI tool configuration and setup
- Dashboard wireframes and final visuals
- Data modeling and transformation logic (e.g., DAX for Power BI)
- Scheduled reports and distribution setup
- User access matrix and security roles for self-service BI
- Data quality checks and profiling reports
- Metadata and lineage documentation
- Data catalog integration (optional: Collibra, Alation)
- Role-based access policies and audit logs
- Data anonymization/masking setup (if required)
- End-user manuals for BI tools and self-service analytics
- Developer documentation for pipelines and architecture
- Training sessions for business analysts, data engineers, and admins
- KT sessions and handover plans
- Final delivery sign-off and SLA-based support model (optional)