Mounir T.

Big Data Analytics Apache Hadoop: - Design distributed systems that manage "Big data" - Use HDFS and MapReduce for storing and analyzing data at scale - Use Apache Spark to create scripts to process data on a Hadoop cluster in more complex ways Hive: - Querying and Managing large datasets that reside in HDFS - Create Hive Datawarehouse, Databases and Loading different file formats in it - Write SQL queries in a native language Hive Query Language (HQL) - Querying complex structures to manipulate data Apache Spark: - Load distributed datasets from any storage system - Work with RDD and perform transformations & actions - Querying, Manipulate DataFrames & Datasets - Building Applications with SBT (Spark Build Tool) - Creating stream processing application - Build Machine Learning models (MlLib) Apache Kafka: - Build real-time streaming pipelines to get data between systems - Integrate Kafka with Spark Structured Streaming - Write a Spark application to consume data from the Kafka server • Power BI: - Clean, stage, and integrate data sources with Power Bi - Abstract data complexities and provide users with intuitive, self-service BI capabilities. - Build business logic and analysis clients’ solutions via the DAX programming language • Data analysis with Pandas: - Analysing and manipulate tabular data - Group, merge and aggregate data frames - Fix messing and invalid values in data - Querying, filtering and sorting data frames • Deep learning with PyTorch: - Exploring and preparing datasets - Working with multiple data types and choose the best algorithms for the model - Building models using multiple algorithms - Model training, evaluation, and make predictions • SSDT (SQL Server Data tools) SSIS, SSAS: - Build data marts, OLAP cubes, and tabular models - Load and cleanse data with SQL Server integration services - Manipulate and analyse data using MDX and DAX queries - Create KPIs and digital dashboards - Implement time-based analysis