Building a Modern Data Ingestion Pipeline Tools, Types & Best Practices

1. BUILDING A MODERN DATA INGESTION PIPELINE: TOOLS, TYPES & BEST PRACTICES Extracting Value: From Raw Source to Business Insight www.hexacorp.com

2. 01 02 03 Volume & Velocity THE DATA TSUNAMI: WHY MODERN INGESTION IS CRITICAL • Data is growing exponentially, demanding pipelines that can handle massive scale and speed. Decision Speed • Real-time business decisions require low-latency data, making timely ingestion non-negotiable. Data Trust • Inconsistent or siloed data leads to flawed analytics and poor strategic choices.

3. THE "WHAT": BATCH, REAL-TIME, AND MICRO-BATCHING Batch Real-Time/Streaming Micro-Batching • Processes large volumes periodically (daily/weekly). High latency. • Processes data continuously with near-zero latency (seconds). • Processes small groups of data very frequently (minutes). A balance of both. Choosing the right type is the foundation of pipeline design, determined by the required Data Latency.

4. Latency: Hours to days. BATCH PROCESSING: EFFICIENCY FOR NON-URGENT DATA Highly efficient resource usage for large volumes; lower cost; simpler complexity. Pros: Use Cases: Payroll, end-of-day reporting, historical analysis, monthly billing. Bridges the gap. It's more responsive than Batch without the high cost of pure Real-Time. Role: Use Cases: Near-real-time metrics, frequently updated dashboards.

5. REAL-TIME: INGESTION AT THE SPEED OF BUSINESS Milliseconds to seconds (near-zero delay). Data Latency: Highest cost and complexity due to continuous infrastructure operation. Cost Implications: Fraud detection, stock market trading, IoT sensor monitoring, and personalized customer experiences. Critical Use Cases: • Batch is a scheduled mail truck; Real-Time is a live video feed.

6. Choose the Right Ingestion Type: Implement Robust Data Quality Checks Leverage Cloud-Native Tools (ELT) Design for Scalability and Resilience THE 4 PILLARS OF MODERN INGESTION 1 2 • Automated Extraction and Loading, with Transformation happening within the cloud data warehouse. • Select Batch, Real-Time, or Micro-Batching based on the required data latency and analytical needs. 3 4 • Ensure data is accurate, consistent, and complete at the point of ingestion. • Use decoupled components to handle volume spikes and allow for graceful recovery from failures.

7. Managed Services: • Fivetran, Airbyte, Stitch. Cloud Services: AWS Kinesis, Google Cloud Pub/Sub, Azure Event Hubs. TOOLS OF THE TRADE: KEY TECHNOLOGIES Data Lakes: • AWS S3, Azure Data Lake Storage. Streaming: Apache Kafka. Cloud Warehouses (ELT): • Snowflake, Google BigQuery, Amazon Redshift. Code-Based: Apache Spark, dbt (Data Build Tool).

8. TRUST YOUR DATA: QUALITY AND VALIDATION • Check for missing values, correct data types, and adherence to schemas before data enters the warehouse. Validation at Source: • Set up automated alerts to flag anomalies or breaches in data quality in real-time. Monitoring: • Define clear ownership and documentation for data lineage (where data came from) to maintain trust. Governance: "Garbage in, garbage out" applies most to the ingestion layer.

9. SECURING THE PIPELINE: GOVERNANCE AND COMPLIANCE • Encryption • Access Control • Compliance • Ensure data is encrypted in transit (during movement) and at rest (in storage). • Use role-based access to limit who can see or modify sensitive data. • Design the pipeline to meet regulatory standards like GDPR, HIPAA, and CCPA. Automate PII (Personally Identifiable Information) masking and tokenization during the ingestion stage.

10. CONCLUSION • A modern data ingestion pipeline must strategically select Batch, Real-Time, or Micro-Batching based on the required data latency. This approach must Go Cloud-Native using ELT tooling for scalability, and Build Trust by embedding Data Quality and robust Security throughout every stage. Unlock instant insights: Start your modern data ingestion journey now. www.hexacorp.com