ETL pipeline

1.How many pipelines does your team have?

I did not count. May have more than 50s.

2.What do your source files look like? Where are they normally landed?

The source files maybe CSV, XML, JSON, parquet. May land on FTP server, Blob storage, S3 Butket. APIs, Databases on-premise or on cloud

3.How do you convert to parquet files for those sources before your ETL pipeline (or in your pipeline)?

Extract raw data (often in CSV or from a database, or from some applications), (1) Python Pandas dataframe, df = pd.read_csv, df.to_parquet (2) pyspark, df = spark.read.csv, df.write.mode(‘overwrite’).parquet(…) (3) ASW Glue for Redshift (4) Snowflake, copy into command, file_format type=’PARQUET’

4.Walk me through how you update new features for your current pipeline?

To add new features, need to ensure changes are reliable, maintainable, and aligned with business goals.

(1) Define and Plan the New Feature

Clarify requirements: Work with stakeholders to specify what the new feature needs to do and how it will impact the pipeline.
Assess dependencies: Identify which parts of the pipeline (tables, scripts, data sources) will be affected by the change.

(2)Develop and Test Incrementally

Implement changes: Open a change ticke tin ServiceNow, attend CAB meeting. Update or add new ETL components (extract, transform, or load steps) as needed.
Automate testing: run automated tests to validate the new logic and ensure existing functionality isn’t broken
Validate data quality: Incorporate validation and cleaning steps to maintain data accuracy and consistency
Peer review: Submit your changes for code review to catch issues early and share knowledge
Branch source code: Create a new branch in version control system (e.g., Git) for the feature update. This isolates the changes and enables safe collaboration and review.
Track all changes: Ensure every code or configuration change is committed and documented.
CI/CD deployment: Use continuous integration/continuous deployment (CI/CD) pipelines to automate testing and deployment to production
Selective updates: Some platforms (like Databricks DLT, Snowflake) allow you to update only the affected tables or components, reducing risk and speeding up validation

(3)Monitor and Optimize

Performance monitoring: Use pipeline monitoring tools to track performance, resource usage, and detect bottlenecks after deploying changes
Incremental loading: If applicable, implement or update incremental loading to process only new or changed data, improving efficiency
Track for errors: Monitor logs and alerts for any issues after deployment
Iterate as needed: Be prepared to roll back or patch quickly if problems arise.

(4)Document and Communicate

Update documentation: Clearly document the new feature in Confluence, its purpose, and any changes to pipeline behavior
Notify stakeholders: Communicate updates and potential impacts to users and other teams.

5.What Is Data Pipeline Orchestration and Why You Need It? What to do before you trigger pipelines?

What Is Data Pipeline Orchestration?
Data pipeline orchestration is the automated scheduling, management, and control of data processing tasks as data moves through pipelines. It ensures that each task in the pipeline runs at the right time, in the correct order, and under the right conditions, turning fragmented data flows into reliable, efficient, and scalable processes.

Why Do We Need Data Pipeline Orchestration?
Automation: Orchestration automates repetitive and complex data tasks, reducing manual intervention and the risk of human error
Dependency Management: It ensures tasks are executed in the correct sequence, handling dependencies between pipeline steps
Monitoring & Reliability: Orchestration tools provide real-time monitoring, alerting, and error handling, improving data quality and reliability
Scalability: As data volumes and complexity grow, orchestration allows pipelines to scale efficiently without a proportional increase in manual work
Centralized Control: It provides a single platform for managing, scheduling, and tracking all data workflows, simplifying oversight and governance
Faster Insights: By automating and optimizing data flows, orchestration ensures that data is processed and made available quickly for analytics and decision-making
Data Governance & Compliance: Orchestration helps maintain data quality, lineage, and compliance with regulations through validation and audit trails

What To Do Before Triggering Pipelines?

Validate Data Sources: Confirm that all data sources are accessible and credentials are up to date
Check Data Quality: Run initial data validation or sanity checks to ensure incoming data meets expected standards
Review Dependencies: Make sure all upstream processes or prerequisite tasks have completed successfully
Resource Availability: Verify that the necessary compute, storage, and network resources are available and not over-allocated
Configuration & Parameters: Double-check pipeline configurations, environment variables, and parameters for correctness.
Error Handling Setup: Ensure error handling, retries, and alerting mechanisms are in place in case of failures
Version Control: Confirm that the pipeline code and configurations are versioned and up to date to avoid running outdated logic
Compliance Checks: Validate that data handling complies with governance and privacy requirements, especially if sensitive data is involved
Monitoring Enabled: Make sure monitoring and logging are active so you can track progress and troubleshoot issues in real time

6.Considering we are in the early stages of the initial ETL process, how would you go about estimating the cost for the entire pipeline?

“for pipeline using Databricks and AWS S3, break down the process into its key components and use available pricing calculators for each service.

1. Identify Cost Components

• Databricks Compute:
  Costs are based on Databricks Units (DBUs) and the underlying AWS EC2 instances. Pricing depends on cluster type, instance size, runtime hours, and Databricks edition (Standard, Premium, Enterprise)
• AWS S3 Storage:
  Includes storage costs (per GB per month), PUT/GET/other request costs, data transfer, and any advanced features like object monitoring or compaction
• Additional AWS Services:
  If your pipeline uses other AWS services (e.g., Lambda, VPC, IAM, CloudWatch), include their estimated costs

2. Use Pricing Calculators

• Databricks Pricing Calculator:
  Use the official Databricks calculator to estimate costs for DBUs, compute hours, and cluster configurations. Select AWS as your provider and input your expected usage patterns
• AWS Pricing Calculator:
  Use this for estimating S3 storage, data transfer, and any other AWS infrastructure costs (EC2, networking, etc.)

3. Gather Your Estimates

• Estimate Compute Needs:
  Number and type of Databricks clusters
  Instance type and size
  Average cluster runtime per day/month
  Expected DBU consumption
• Estimate Storage and Requests:
  Total data volume stored in S3
  Number of PUT/GET and other requests
  Data growth rate and retention period
  S3 features like object monitoring or compaction
• Estimate Data Transfer:
  Volume of data moved between Databricks and S3
  Any cross-region or internet egress

4. Example Calculation

Adjust these numbers based on your actual or projected usage.

5. Best Practices

• Start with conservative estimates and refine as your workload becomes clearer.
• Use both calculators: The Databricks calculator for DBU/compute costs, and the AWS calculator for S3 and infrastructure
• Monitor actual usage once you begin running ETL jobs, and adjust your estimates accordingly.

Summary Table

7.How to keep data consistency in your pipeline?

1.Data Validation at Every Stage

• Implement comprehensive validation checks during ingestion, transformation, and loading to ensure data is accurate, complete, and consistent
• Use constraints at the table level (e.g., NOT NULL, unique, value ranges) so that invalid data cannot enter your system
• Leverage automated data profiling and validation tools (like Great Expectations or built-in Databricks/Snowflake features) to enforce data quality rules

2.Automated Data Quality Checks and Alerts

• Set up automated data quality checks and anomaly detection to catch inconsistencies or unexpected patterns as soon as they occur
• Use monitoring dashboards and alerting systems to notify your team of data anomalies or pipeline failures in real time

3.Source Validation and Proactive Controls

• Validate data at the source before it enters your pipeline, ensuring integrity from the very beginning
• Reject or quarantine data that does not meet quality standards to prevent contamination of downstream processes

4.Maintain Data Lineage and Metadata

• Track data lineage to understand the origin and transformation history of your data, which aids in auditing and troubleshooting
• Use metadata management tools to document and monitor changes throughout the pipeline

5.Modular and Version-Controlled Architecture

• Design your pipeline in modular stages (extraction, transformation, loading) so each can be tested and maintained independently
• Use version control for pipeline code and configuration, with structured change management and code reviews to minimize errors

6.Continuous Testing and Auditing

• Implement unit and integration tests for pipeline components, and conduct periodic audits to ensure ongoing data consistency
• Regularly review and update validation logic as business requirements evolve

7.Automate and Monitor Pipeline Operations

• Automate repetitive validation and deployment tasks to reduce human error and increase reliability
• Continuously monitor pipeline health and performance, scaling resources as needed to maintain throughput and consistency

8.What is the data pipeline? How do you define it?

A data pipeline is a system or set of processes that automates the movement, transformation, and storage of raw data from various sources to a designated destination, such as a data warehouse, data lake, or business intelligence tool. The primary goal of a data pipeline is to convert raw, disparate data into high-quality, usable information for analysis, reporting, and decision-making
Definition
– A data pipeline is a series of tools and activities that:
– Ingest raw data from multiple sources (databases, APIs, files, IoT devices, etc.)
– Transform, clean, standardize, and enrich the data to fit business needs
– Load and store the processed data in a central repository for further analysis or application use
Key Components of a Data Pipeline
– Data Sources: Where raw data originates (e.g., databases, APIs, logs, sensors)
– Data Ingestion: The process of collecting or importing data into the pipeline, either in real-time (streaming) or batches
– Data Transformation: Cleaning, validating, standardizing, enriching, and reformatting data for its intended use
– Data Storage: Temporary or permanent storage used at various stages, such as staging areas, data lakes, or warehouses
– Data Orchestration: Scheduling and managing the sequence and dependencies of pipeline tasks to ensure smooth data flow
– Data Loading: Moving the transformed data to its final destination for analysis or application use
– Monitoring and Observability: Tracking pipeline health, performance, and data quality to detect and resolve issues early
– Data Cataloging and Governance: Maintaining metadata, lineage, and access controls to ensure data is discoverable, trustworthy, and secure