Turn Raw Data Into Revenue-Driving Decisions
We build the data infrastructure that transforms terabytes of raw events into clear, actionable insights — enabling every team to make faster, smarter, data-driven decisions.
Big Data & AnalyticsServices & Capabilities
Data Lake & Warehouse
Design and implement scalable data lakes on S3/GCS and cloud warehouses (Snowflake, BigQuery, Redshift).
ETL/ELT Pipelines
Robust data pipelines using Apache Spark, Airflow, dbt, and Fivetran for batch and streaming ingestion.
Real-Time Streaming Analytics
Apache Kafka + Flink pipelines for sub-second analytics on live event streams.
BI Dashboards
Self-service Power BI, Tableau, Metabase, and Superset dashboards for business teams.
Data Quality & Governance
Great Expectations, dbt tests, and data catalog (Apache Atlas, Amundsen) for reliable, trusted data.
Predictive Analytics
Statistical models and ML pipelines on top of your data warehouse for forecasting and segmentation.
Technologies & Tools
Our Big Data & Analytics Process
Data Audit
Inventory all data sources, assess quality, and identify the highest-value analytics use cases.
Architecture Design
Design the lakehouse architecture, data model, and pipeline topology before building anything.
Pipeline Development
Build, test, and schedule ETL/ELT pipelines with automated data quality checks at every stage.
Dashboard Development
Build role-specific dashboards with drill-down, filters, and scheduled report delivery.
Training & Handover
Team training, runbooks, and data dictionary so your team can self-serve analytics going forward.
Why Choose Anovayx Technology Pvt Ltd
Eliminate conflicting reports — one unified data model everyone can trust.
Pre-computed aggregations and columnar storage make dashboards load in seconds, not hours.
Anomaly detection and threshold alerts notify the right teams before problems escalate.
Data lake architecture on S3/GCS reduces data storage costs by 60–80% vs. traditional databases.
Industries We Serve
Frequently Asked Questions
QWhat is the difference between a data lake and a data warehouse?
A data lake stores raw, unstructured data cheaply (S3/GCS) for flexible exploration. A data warehouse (Snowflake, BigQuery) stores structured, modeled data optimized for fast BI queries. Modern lakehouse architectures combine both.
QHow long does it take to build a data platform?
A basic BI dashboard on existing data takes 4–6 weeks. A full data lake + ETL + BI stack typically takes 3–5 months. We always deliver a working dashboard in the first 4 weeks.
QCan you work with our existing databases?
Yes. We connect to PostgreSQL, MySQL, Oracle, SQL Server, MongoDB, Salesforce, HubSpot, Google Analytics, and 150+ sources via Fivetran or custom connectors.
QIs our data secure in the cloud?
Yes. We implement encryption at rest and in transit, column-level security, row-level access controls, and VPC isolation. Data never leaves your cloud account.
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