major task categories of data engineering with examples and associated tools:

major task categories of data engineering with examples and associated tools:

1. Data Ingestion and Collection:

• Example: Ingesting log data from web servers.

• Tools: Apache NiFi, Flume, AWS Kinesis, Google Cloud Pub/Sub.

2. Data Extraction, Transformation, and Loading (ETL):

• Example: Extracting customer data from a CRM, transforming it to calculate customer lifetime value, and loading it into a data warehouse.

• Tools: Apache Spark, Apache Beam, Talend, Apache Flink.

3. Data Warehousing:

• Example: Storing structured data for reporting and analytics.

• Tools: Amazon Redshift, Google BigQuery, Snowflake, Microsoft Azure SQL Data Warehouse.

4. Data Modeling and Schema Design:

• Example: Designing a star schema for an e-commerce database.

https://abhib854.wixsite.com/website/post/data-modeling-and-schema-design-designing-a-star-schema

• Tools: Erwin Data Modeler, DbSchema, Lucidchart.

5. Data Quality Assurance and Validation:

• Example: Verifying that all email addresses follow a valid format.

• Tools: Great Expectations, Apache Griffin, Talend Data Quality.

6. Data Governance and Compliance:

• Example: Implementing policies for data privacy and security compliance (e.g., GDPR).

• Tools: Collibra, Informatica Axon, Alation.

7. Data Cataloging and Metadata Management:

• Example: Creating a catalog with metadata about datasets, including data types and sources.

• Tools: Apache Atlas, Collibra Catalog, AWS Glue Data Catalog.

8. Data Pipeline Orchestration:

• Example: Automating ETL workflows with dependencies and scheduling.

• Tools: Apache Airflow, Luigi, Prefect, Apache Oozie.

9. Streaming Data Processing:

• Example: Processing real-time sensor data from IoT devices.

• Tools: Apache Kafka, Apache Flink, Spark Streaming, AWS Kinesis.

10. Batch Data Processing:

• Example: Running a nightly batch job to aggregate daily sales data.

• Tools: Apache Spark, Hadoop MapReduce, Apache Flink.

11. Data Integration and Data Fusion:

• Example: Combining customer data from CRM with purchase history from an e-commerce platform.

• Tools: Talend, Apache NiFi, Informatica PowerCenter.

12. Data Versioning and Lineage Tracking:

• Example: Tracking changes to data with Git or a dedicated versioning tool.

• Tools: Apache Atlas, Data Version Control (DVC), Git.

13. Data Security and Privacy:

• Example: Encrypting sensitive customer information in a database.

• Tools: AWS Key Management Service (KMS), HashiCorp Vault, Thales CipherTrust.

14. Data Scaling and Optimization:

• Example: Implementing sharding in a distributed database.

• Tools: Apache Cassandra, MongoDB Sharding, Google Cloud Spanner.

15. Data Backup and Disaster Recovery:

• Example: Regular backups of critical databases and a disaster recovery plan.

• Tools: AWS Backup, Google Cloud Backup, Commvault.

16. Data Exploration and Analysis Support:

• Example: Providing analysts with a tool like Tableau for data visualization.

• Tools: Tableau, Power BI, Looker, QlikView.

17. Machine Learning Infrastructure:

• Example: Deploying and managing machine learning models in a production environment.

• Tools: MLflow, TensorFlow Serving, Kubeflow, SageMaker.

18. Monitoring and Performance Tuning:

• Example: Monitoring database performance and optimizing query execution plans.

• Tools: Prometheus, Grafana, New Relic, Datadog.

19. Collaboration and Communication:

• Example: Team meetings to discuss progress on data engineering projects.

• Tools: Slack, Microsoft Teams, Zoom, Jira.

20. Documentation and Knowledge Sharing:

• Example: Creating a wiki or documentation repository for data engineering processes.

• Tools: Confluence, GitHub Wiki, Notion.