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=valuat a=cases or daf4'waa ehousFn w6rRl-o5dsTafidTeView c a studiesthatt'demo'ffstrate=implemeTtation df <br />AWS data and analytic services as part of a data warehousing solution. <br />Choose an appropriate Amazon Redshift node type and size for your data needs. <br />Discuss security features as they pertain to Amazon Redshift, such as encryption, IAM permissions, and database <br />permissions. <br />Launch an Amazon Redshift cluster and use the components, features, and functionality to implement a data <br />warehouse in the cloud. <br />Use other AWS data and analytic services, such as Amazon DynamoDB, Amazon EMR, Amazon Kinesis Firehose, <br />and Amazon S3, to contribute to the data warehousing solution. <br />Evaluate approaches and methodologies for designing data warehouses. <br />Identify data sources and assess requirements that affect the data warehouse design. <br />Design the data warehouse to make effective use of compression, data distribution, and sort methods. <br />Load and unload data and perform data maintenance tasks. <br />Write queries and evaluate query plans to optimize query performance. <br />Configure the database to allocate resources such as memory to query queues and define criteria to route certain <br />types of queries to your configured query queues for improved processing. <br />Use features and services, such as Amazon Redshift database audit logging, Amazon CloudTrail, Amazon <br />CloudWatch, and Amazon Simple Notification Service (Amazon SNS), to audit, monitor, and receive event <br />notifications about activities in the data warehouse. <br />Prepare for operational tasks, such as resizing Amazon Redshift clusters and using snapshots to back up and <br />restore clusters. <br />Use a business intelligence (BI) application to perform data analysis and visualization tasks against your data. <br />Data Wrangling with Python <br />Description <br />In this course you will start with the absolute basics of Python, focusing mainly on data structures. Then you will <br />delve into the fundamental tools of data wrangling like NumPy and Pandas libraries. You'll explore useful insights <br />into why you should stay away from traditional ways of data cleaning, as done in other languages, and take <br />advantage of the specialized pre -built routines in Python. <br />This combination of Python tips and tricks will also demonstrate how to use the same Python backend and <br />extract/transform data from an array of sources including the Internet, large database vaults, and Excel financial <br />tables. To help you prepare for more challenging scenarios, you'll cover how to handle missing or wrong data, and <br />reformat it based on the requirements from the downstream analytics tool. The course will further help you grasp <br />concepts through real -world examples and datasets. <br />Acquired Skills <br />Data structure using python <br />Advanced data structure <br />Numpy, Pandas and MatplotLlB <br />Sub setting, Filtering and Grouping <br />BeautifulSoup4 and Web Page Parsing <br />Zip Function <br />Web Scraping and BeautifulSoup Libraries <br />Reding Data for XML <br />RDBMS and SQL <br />Database Design - A Modern Approach <br />Description <br />This course introduces you to a process for effectively planning and designing a functional, efficient database. <br />Knowing how to plan a relational database is important to the success of the databases you create. Without <br />planning, you cannot possibly know what the database needs to do, or even what information to include in the <br />database. Planning a database is essential and prevents the extra work of fixing data maintenance problems later <br />on. <br />Acquired Skills <br />Follow an efficient process for designing a relational database <br />Define the database conceptual model <br />Define the database logical model <br />Apply database normalization methods to improve the initial design of a database <br />