Data Engineering

Data

ENGINEERING

What we do?

Data Strategy and governance

Data Architecture and System Design

ETL/ELT & Data Pipeline Development

Data Migration (to Cloud) & Integration

Big Data processing and Real time Analytics

Data Quality, Security and Compliance

DevOps for Data

Support & Optimization

Our Process

Discovery and Alignment

Data Assessment and Planning

Solution Design

Implementation and Integration

Testing and Validation

Deployment and Training

Monitoring and Optimization

Why Choose us?

Deep Technical Expertise

Scalable and future ready solutions

Security and compliance focussed

Data Understanding

Relational databases (SQL)

NoSQL databases (MongoDB, Cassandra)

Data warehouses (Snowflake, Redshift, BigQuery)

Data Pipelines

Batch processing (Apache Hadoop, Spark)

Stream processing (Apache Kafka, Flink)

ETL/ELT processes

Data Integration

APIs for data access

Data federation

Data versioning

Big Data Ecosystem

Distributed systems (Hadoop, Spark)

File formats (Parquet, Avro, ORC)

Cluster management (Kubernetes, Docker, YARN)

Cloud Platforms

AWS (S3, EMR, Lambda)

Azure (Data Factory, Synapse Analytics)

Google Cloud (BigQuery, Dataflow)

Data Governance

Data security and encryption

Data quality management

Compliance (GDPR, CCPA)

Data Transformation

Data cleansing

Aggregation and summarization

Schema design and migration

Real-Time Processing

Real-time analytics

Event-driven architectures

Tools (Apache Kafka, RabbitMQ)

Monitoring and Debugging

Observability in data pipelines

Debugging data flows

Monitoring tools (Prometheus, Grafana)

Automation and Scheduling

Workflow orchestration (Apache Airflow, Prefect, Dagster)

Automation frameworks

Cron jobs and serverless automation

Scalable Architecture

Horizontal scaling of systems

Distributed databases

High availability and fault tolerance

Programming for Data Engineering

Python and Java for scripting

SQL for querying

Shell scripting for automation