What are new technologies to learn after ETL testing?

After gaining experience in ETL testing, you can expand your skill set by learning new technologies in the following areas: 1. *Data Analytics*: - Data visualization tools like Tableau, Power BI, or QlikView - Statistical programming languages like R or Python - Machine learning libraries like scikit-learn or TensorFlow 2. *Big Data*: - Hadoop ecosystem (HDFS, MapReduce, YARN) - NoSQL databases like MongoDB, Cassandra, or Couchbase - Big data processing frameworks like Spark, Flink, or Beam 3. *Cloud Computing*: - Amazon Web Services (AWS) or Microsoft Azure cloud platforms - Cloud-based data warehousing like Amazon Redshift or Google BigQuery - Cloud-based ETL tools like AWS Glue or Azure Data Factory 4. *Data Science*: - Python libraries like Pandas, NumPy, or SciPy - Data modeling and data mining techniques - Deep learning frameworks like Keras or PyTorch 5. *DevOps and Automation*: - Containerization using Docker or Kubernetes - Automation tools like Jenkins, Travis CI, or CircleCI - Infrastructure as Code (IaC) tools like Terraform or CloudFormation 6. *Data Governance and Quality*: - Data governance frameworks and best practices - Data quality tools like Talend or Trifacta - Data cataloging and metadata management tools like Alation or Collibra 7. *Artificial Intelligence and Machine Learning*: - Natural Language Processing (NLP) libraries like NLTK or spaCy - Computer Vision libraries like OpenCV or Pillow - AI and ML frameworks like TensorFlow or PyTorch Learning these technologies can help you transition into roles like: 1. Data Analyst or Scientist 2. Big Data Engineer or Architect 3. Cloud Data Engineer or Architect 4. Data Governance Specialist 5. AI/ML Engineer or Researcher 6. DevOps Engineer or Automation Specialist Remember to choose technologies that align with your interests and career goals. read less