Flume, Spark, Programming Languages, Sql, DataFlow, exploratory queries, data pipelines, ETL systems, dimensional data modeling, data infrastructure

Spark, Databricks, Sql, ELT, SQL Server, Data Modeling, Azure Data Factory, Etl, MySQL, Oracle, Python, PostgreSQL, Airflow, dbt, metadata, data ingestion pipelines, lineage, data quality checks

Hadoop, Pyspark, Kafka, Hive, Linux, Sqoop, Spark, Apache Beam, Python, data fusion, CI-CD pipelines, Google Cloud Storage

data quality assurance , Database Systems, Data integration techniques, Validation methods, Data pipeline architecture, ETL tools and processes, Performance Testing Strategy, Data storage solutions

Azure Synapse, Cloud Architecture, Data Warehousing Concepts, Python, Sql, ETL processes, ADLS Gen2

Java, Hadoop, Scala, AWS Glue, Data Quality, Emr, Data Modeling, Sql, ELT, Hive, Data Integration, Spark, Python, Etl, AI capabilities, data presentation, Data pipelines

Azure Databricks, Numpy, Devops, Git, Azure Functions, Pandas, Flask, Integration Testing, FastAPI, Rest Apis, Python, Scikit-learn, ETL processes, Data Processing

snowflake , Hadoop, Power Bi, Kafka, Data Modeling, Sql, Apache Airflow, Git, Azure Data Factory, Docker, Spark, Python, data integration techniques, Flink, dbt, ETL processes

Github, Google Cloud Platform, Pyspark, Data Warehousing, Sql, Microservices, Devops, Data Governance, Microsoft Azure, Databricks, Python, AWS, automated web services, AI technologies, ETL processes, Data Validation

Apache Spark, Kafka, Apache Airflow, Pandas, Elasticsearch, MongoDB, pgvector, Polars, Vector databases, Qdrant, NoSQL databases, Data models and storage architectures, Distributed data systems, Data processing performance and optimisation, Data pipelines, Milvus