Data Pipeline Development:
- Design, develop, and implement scalable data pipelines using Cloudera tools like Hadoop, Spark, Hive, Impala, and HDFS.
- Write and optimize ETL processes to extract, transform, and load data into data lakes or warehouses.
2. Big Data Application Development:
- Develop applications to process large datasets efficiently using frameworks such as Apache Spark and MapReduce.
- Build solutions for batch and real-time data processing.
3. Cluster Management:
- Work with Cloudera Manager for cluster setup, configuration, monitoring, and performance optimization.
- Ensure high availability and scalability of Cloudera clusters.
4. Data Storage and Management:
- Design and implement data storage strategies using HDFS, HBase, and other Cloudera-supported tools.
- Optimize data storage and retrieval processes to improve performance.
5. Security and Governance:
- Implement security measures using Cloudera tools like Apache Ranger and Sentry.
- Ensure compliance with data governance and security policies.
6. Performance Tuning:
- Monitor and optimize the performance of Hadoop and Spark jobs.
- Troubleshoot and resolve performance bottlenecks in data pipelines.
7. Integration and Collaboration:
- Integrate Cloudera solutions with external systems, databases, and APIs.
- Collaborate with data scientists, analysts, and other teams to understand requirements and deliver data solutions.
8. Documentation and Support:
- Document processes, workflows, and solutions for future reference.
- Provide support and troubleshoot issues related to Cloudera tools and big data applications.
9. Innovation and Optimization:
- Stay updated on emerging technologies and best practices in the big data ecosystem.
- Continuously seek ways to improve data architecture, performance, and reliability