Data Scientist — Big Data Technologies (Spark/Hadoop)

Master Spark SQL and the Catalyst optimizer. - Description: Focus on Spark SQL, its DataFrames API, and the Catalyst optimizer. Explore the query planning process, understand how Catalyst optimizes queries, and delve into performance tuning techniques for SQL and DataFrame-based applications. - Resources/Activities: - Study the Catalyst optimizer's architecture, including its logical and physical plans. - Analyze the execution plans generated by Spark SQL using EXPLAIN. - Experiment with different query optimization strategies (e.g., using hints, broadcasting joins). - Compare and contrast the performance of different data formats (Parquet, ORC, Avro). - Implement a custom rule in the Catalyst optimizer. - Expected Outcomes: Mastery of Spark SQL, a deep understanding of the Catalyst optimizer, ability to write efficient SQL and DataFrame queries, and skills in performance tuning Spark SQL applications.

Master real-time data processing with Spark Streaming. - Description: This day focuses on real-time data processing with Spark Streaming, covering both legacy Spark Streaming and the newer Structured Streaming. Explore advanced concepts like state management, windowing, and fault tolerance. Focus on custom stream processing logic and connectors. - Resources/Activities: - Study the difference between Spark Streaming and Structured Streaming. - Implement stateful stream processing operations using both APIs. - Design and implement custom streaming connectors (e.g., for Kafka, RabbitMQ, or other streaming sources). - Evaluate and optimize stream processing applications for low latency and high throughput. - Explore watermarking and event-time based processing. - Expected Outcomes: Proficiency in designing, developing, and deploying real-time data processing applications using Spark Streaming, understanding of advanced streaming concepts, and the ability to optimize stream processing performance.

Understand the core components of the Hadoop ecosystem. - Description: This day dives deep into the Hadoop ecosystem, focusing on YARN (Yet Another Resource Negotiator), HDFS (Hadoop Distributed File System), and resource management. Understand how these components work together to provide a distributed computing environment. - Resources/Activities: - Study the architecture of YARN and HDFS. - Configure and manage a Hadoop cluster (e.g., using a local cluster). - Experiment with different YARN scheduling policies (e.g., FairScheduler, CapacityScheduler). - Analyze HDFS performance and optimize data storage strategies. - Troubleshoot Hadoop cluster issues and understand common failure scenarios. - Expected Outcomes: A solid understanding of the Hadoop ecosystem, mastery of YARN and HDFS, and the ability to manage and optimize Hadoop clusters.

Leverage Spark for graph processing. - Description: Learn to use Spark for graph processing, focusing on the GraphX and GraphFrames APIs. Explore graph algorithms and data manipulation techniques in a distributed setting. - Resources/Activities: - Study the concepts behind graph databases and graph algorithms. - Implement graph algorithms using both GraphX and GraphFrames (e.g., PageRank, community detection). - Analyze the performance characteristics of different graph algorithms. - Explore data ingestion and transformation techniques for graph data. - Compare and contrast GraphX and GraphFrames for different use cases. - Expected Outcomes: Proficiency in using Spark for graph processing, ability to implement and optimize graph algorithms, and an understanding of graph data manipulation techniques.

Refine your Spark skills with advanced optimization. - Description: Consolidate knowledge of Spark performance tuning with advanced techniques. This includes memory management, garbage collection tuning, data serialization, and cluster configuration. Focus on specific optimization scenarios and best practices for production deployments. - Resources/Activities: - Review Spark configuration properties and understand their impact on performance. - Experiment with different memory management strategies (e.g., tuning executor memory, off-heap memory). - Analyze Spark logs and identify common performance bottlenecks (e.g., shuffle operations, data skew). - Implement advanced optimization techniques for specific workloads (e.g., join optimization, caching strategies). - Performance test Spark applications with different cluster configurations. - Expected Outcomes: Ability to diagnose and resolve Spark performance issues, expert knowledge of Spark tuning, and proficiency in deploying and managing Spark applications in a production environment.

Focus on security, productionization and CI/CD for Spark applications. - Description: Understand the security aspects of Spark, including authentication, authorization, and data encryption. Explore strategies for productionizing Spark applications, including monitoring, logging, and error handling. Learn about CI/CD pipelines for Spark. - Resources/Activities: - Study the security features of Spark, including integration with security frameworks (e.g., Kerberos). - Implement authentication and authorization for Spark applications. - Set up monitoring and logging for Spark applications using tools like Prometheus, Grafana, and ELK stack. - Design and implement CI/CD pipelines for Spark application deployments. - Explore advanced topics like data governance and data lineage within a Spark environment. - Expected Outcomes: A comprehensive understanding of Spark security best practices, the ability to productionize Spark applications, proficiency in CI/CD for Spark, and skills in data governance and data lineage.