v3.2.1
Documentation Core Platform Data Pipelines

Data Integration & Pipeline Architecture

Last updated: Dec 12, 2024 14.2k views Advanced

DataPulse's data pipeline framework provides a robust, scalable, and secure foundation for ingesting, transforming, and distributing analytics-ready datasets across your organization. This guide covers configuration, execution models, and best practices.

Note: Pipeline v3.2 introduces native support for Delta Lake and Iceberg table formats. Ensure your cluster nodes run SDK version 2.1 or higher.

Prerequisites & Installation

Before configuring pipelines, ensure your environment meets the following requirements:

  • Python 3.9+ or Java 17+
  • Supported cloud provider (AWS, Azure, GCP) or on-premise Kubernetes cluster
  • Active DataPulse Enterprise license key
  • Minimum 4vCPU / 16GB RAM per worker node
bash
# Install DataPulse CLI curl -fsSL https://install.datapulse.io/cli | bash # Authenticate your workspace dpctl auth --token YOUR_ENTERPRISE_TOKEN # Initialize pipeline project dpctl init my-analytics-pipeline cd my-analytics-pipeline

Pipeline Architecture Overview

The DataPulse pipeline engine follows a micro-batch streaming architecture with exactly-once processing guarantees. Data flows through four core stages:

Stage Function Latency Status
Ingestion Connects to sources (DBs, APIs, IoT, Logs) < 2s Stable
Transformation SQL/PySpark operations, schema validation 5-15s Stable
Orchestration DAG scheduling, dependency resolution N/A Updated
Distribution Routes to warehouses, lakes, or APIs < 3s Beta

Configuration Examples

Pipelines are defined using YAML configuration files. Below is a standard ETL workflow definition:

pipeline.yml
pipeline: name: customer_behavior_etl version: "2.4.1" schedule: "0 */4 * * *" # Every 4 hours sources: - type: postgresql connection: ${DB_CONNECTION_STRING} query: "SELECT * FROM user_events WHERE updated_at > ${LAST_SYNC}" transformations: - operation: deduplicate keys: ["user_id", "timestamp"] - operation: enrich lookup_table: geo_ip_mapping destinations: - type: snowflake table: ANALYTICS.CUSTOMER_EVENTS mode: merge
Security Warning: Never hardcode credentials in YAML files. Use environment variables or your cloud provider's secret manager. DataPulse supports AWS Secrets Manager, Azure Key Vault, and HashiCorp Vault out of the box.

Programmatic Control

Interact with pipelines via our REST API or Python SDK:

python
import datapulse_sdk as dp # Initialize client client = dp.Client(workspace="enterprise-prod") # Trigger pipeline run run = client.pipelines.trigger( pipeline_id="cust_behavior_etl_v2", parameters={"date_range": "7d"}, priority=2 ) print(f"Run ID: {run.id}, Status: {run.status}")

Common Issues & Solutions

  • Schema Drift Errors: Enable schema_evolution: adaptive in your pipeline config to auto-handle new columns.
  • Memory OOM on Workers: Adjust spark.executor.memory or enable auto-scaling in the orchestration layer.
  • Connection Timeouts: Verify VPC peering routes and security group rules allow outbound traffic to port 443.
Pro Tip: Use the dpctl validate command before deploying pipelines to catch configuration errors and security vulnerabilities early.
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