Data Pipelines
Data Pipelines: Ingest. Orchestrate. Scale.
Your analytics and AI depend on continuous, reliable data flows. Datafyze builds robust Data Pipelines that ingest from diverse sources, transform via ETL/ELT, and orchestrate complex workflows ensuring your data arrives on time, every time, at any scale.
Key Capabilities
Data Ingestion
Connect to databases, APIs, message queues, and IoT sources for batch or real-time capture.
Orchestration & Scheduling
Coordinate tasks across environments using Airflow, AWS Step Functions, or Azure Data Factory.
Monitoring & Alerting
Implement end-to-end observability to track data freshness, throughput, and error trends.
ETL/ELT Workflows
Automate extraction, transformation, and loading with cloud-native tools and custom scripts.
Scalability & Fault Tolerance
Design pipelines that auto-scale and recover gracefully from failures.
Proven Outcomes
99.9% pipeline uptime through automated retries and alerting.
60% reduction in data latency via optimized streaming ingestion.
50% faster delivery of new data sources with reusable pipeline templates.
FAQs
What sources can you ingest data from?
We connect to relational and NoSQL databases, REST and SOAP APIs, message queues (Kafka, Kinesis), file systems, and IoT streams—handling both batch and real-time data.
How do you orchestrate complex workflows?
We use orchestration frameworks like Apache Airflow, AWS Step Functions, and Azure Data Factory to define, schedule, and monitor multi-step ETL/ELT tasks with dependencies and retry logic.
Can these pipelines scale with growing data volumes?
Yes. We design pipelines with auto-scaling compute, partitioned processing, and parallelization to handle increasing data loads without performance degradation.