With 2 years of professional experience, I have been actively involved in the design, development, and maintenance of significant business applications. My expertise lies in handling diverse projects, including data migration, integration, conversion, and comprehensive testing.Proficient in evaluating business rules, collaborating with stakeholders, and performing comprehensive source-to-target data mapping. Additionally, experienced in designing and reviewing data integration solutions.Experienced in optimizing Spark DataFrame performance by tuning various configuration settings, such as memory allocation, caching, and serialization. Expertise in using Spark DataFrame transformations and actions to process large-scale structured and semi-structured data sets, including filtering, mapping, reducing, grouping, and aggregating data.Skilled in using Spark DataFrame persistency and caching mechanisms to reduce data processing overhead and improve query performance.Familiarity with Spark DataFrame schema and data type operations, such as adding, renaming, and dropping columns, casting data types, and handling null values.Knowledge of Spark DataFrame optimization techniques, such as predicate pushdown, column pruning, and vectorized execution, and their impact on query performance and resource utilization.Ability to troubleshoot common issues with Spark DataFrame, such as data processing errors, performance bottlenecks, and scalability limitations.Familiarity with Spark DataFrame-based data processing libraries and frameworks, like Apache Spark SQL and their features and limitations.Familiarity with Spark DataFrame APIs and SQL syntax and ability to write complex SQL queries and DataFrame operations to solve business problemsExperienced in optimizing Spark SQL performance by tuning various configuration settings, such as memory allocation, caching, and serialization.Expertise in using Spark SQL to process large-scale structured and semi-structured data sets, including querying, filtering, mapping, reducing, grouping, and aggregating data.Skilled in using Spark SQL persistency and caching mechanisms to reduce data processing overhead and improve query performance.Familiarity with Spark SQL schema and data type operations, such as creating, modifying, and dropping tables, views, and indexes, and handling null values.Knowledge of Spark SQL optimization techniques, such as cost-based query optimization, column pruning, and predicate pushdown, and their impact on query performance and resource utilization.