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We're here to help you find itThe "Building Data lakes on AWS" course provides a comprehensive guide to creating, managing, and utilizing Data lakes on the AWS cloud platform. It is designed to help learners understand the value of Data lakes, differentiate them from data warehouses, and recognize the crucial components that make up a data lake. The course covers essential topics such as Data ingestion, Cataloging, preparation, and processing using a variety of AWS services, including AWS Glue, Amazon Athena, and AWS Lake Formation.
Learners will gain practical experience through hands-on labs, setting up a simple data lake, building a data lake with AWS Lake Formation, Automating data lake creation, and Data Visualization using Amazon QuickSight. By the end of the course, participants will have a solid understanding of building Data lakes on AWS, and will be equipped with the skills to build a data lake on AWS effectively, ensuring they can leverage the full potential of their data assets in the cloud.
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♱ Excluding VAT/GST
Classroom Training price is on request
You can request classroom training in any city on any date by Requesting More Information
To ensure that participants are well-prepared and can fully benefit from the Building Data Lakes on AWS course, the following prerequisites are recommended:
These prerequisites are intended to provide a foundation that will allow students to engage with the course content effectively. They are not meant to be barriers to entry, but rather to ensure that students have a positive and productive learning experience. Students with varying levels of prior knowledge have successfully completed the course by taking advantage of the resources provided and actively participating in the learning process.
This AWS data lake course offers in-depth training on setting up and managing data lakes, ideal for IT professionals focused on data management and analytics.
The Building Data Lakes on AWS course is designed to equip students with the skills needed to effectively construct, manage, and utilize data lakes on the AWS platform, focusing on concepts such as storage, processing, analysis, and security.
AWS Glue is a managed extract, transform, and load (ETL) service that helps you prepare and load your data for analytics. You can use AWS Glue to organize, cleanse, validate, and format large datasets. It is particularly useful in **building data lakes on AWS**, as it simplifies the process of extracting data from various sources, transforming it into a useful format, and loading it into a data lake. This integration facilitates easy data analysis and processing, making it an essential tool for managing data at scale within the AWS cloud ecosystem.
Amazon Athena is a query service that makes it easy to analyze data directly in Amazon Web Services (AWS) using standard SQL. It's particularly useful when building data lakes on AWS, as it doesn't require setting up complex processes to use it. With Athena, you can instantly query data stored in AWS, and pay only for the queries you run. This service allows companies to quickly access vast amounts of data without the need to build or maintain additional infrastructure, facilitating efficient data management and analysis in a cost-effective way.
AWS Lake Formation is a service designed to simplify building data lakes on AWS. It automates the process of collecting, storing, and securing large volumes of data from various sources. Once in the data lake, data can easily be analyzed using popular analytics and machine learning tools. Lake Formation ensures proper data access and security, allowing users to focus on extracting valuable insights rather than managing data storage. Thus, it significantly streamlines the process and reduces the complexity and time required to build a data lake in AWS.
Data Visualization is the process of converting data into graphical representations to make it easier to understand and interpret. It helps individuals and businesses to see trends, patterns, and outliers in their data. By using visual elements like charts, graphs, and maps, data visualization can help to explain complex data simply and effectively, aiding in faster decision-making and improved communication. This technique is critical in analyzing vast amounts of information and turning it into actionable insights, making it a valuable tool in various fields including marketing, finance, education, and healthcare.
Amazon QuickSight is a cloud-based business intelligence service from AWS that allows users to perform visual analysis and obtain insights from their data. Users can create interactive visualizations, dashboards, and perform ad-hoc analysis in a scalable, serverless environment. QuickSight seamlessly integrates with AWS data sources like S3, where companies often build data lakes, and other AWS services, making it efficient for building visuals directly on top of these data repositories. This integration supports organizations in quickly pivoting from data storage to insightful, actionable analytics without substantial setup or management overhead.
Automating data lake creation on AWS involves using cloud services to streamline the setup and management of large data storage repositories. When building a data lake on AWS, you deploy tools to automatically collect, store, and organize data from various sources, making it ready for analysis. This process enhances data accessibility and analysis without manual intervention, significantly reducing the complexity and time required to manage vast datasets. Using AWS for building data lakes ensures a scalable, secure, and cost-efficient infrastructure, tailored to meet evolving business needs.
Cataloging in a technical context refers to the systematic organization and classification of data, making it easily searchable and retrievable. Effective cataloging involves documenting metadata about data assets, which includes details like source, usage, relationships, and access constraints. This process is crucial in managing data across various systems, such as when building a data lake on AWS. By cataloging data effectively within an AWS data lake, organizations can enhance data discovery, governance, and compliance, ultimately facilitating better data-driven decision-making and optimized resource management.
A data lake is a centralized repository that allows you to store all your structured and unstructured data at any scale. You can keep data as-is, without having to first structure it, and run different types of analytics—from dashboards and visualizations to big data processing, real-time analytics, and machine learning to guide better decisions. Building data lakes on AWS helps streamline this process, providing scalable, secure, and cost-efficient solutions to manage vast amounts of data, enhancing your ability to extract insights and value from your information resources.
AWS (Amazon Web Services) cloud platform allows you to build a data lake, which is a centralized repository that stores large amounts of raw data in its native format. Building a data lake on AWS involves using its scalable infrastructure and services to handle diverse data types from various sources, making it easier for businesses to process, analyze, and secure large datasets efficiently. This platform supports the exploration and analysis of data to derive insights and make informed business decisions.
Data ingestion is the process of transporting data from various sources to a storage medium where it can be accessed, used, and analyzed by an organization. This data might come from various sources like databases, live feeds, and file archives, and is often ingested into systems like a data lake. Building a data lake on AWS involves setting up a centralized repository on Amazon Web Services where you can store all your structured and unstructured data at scale. This allows for efficient data management and analysis in a flexible, scalable environment.
This AWS data lake course offers in-depth training on setting up and managing data lakes, ideal for IT professionals focused on data management and analytics.
The Building Data Lakes on AWS course is designed to equip students with the skills needed to effectively construct, manage, and utilize data lakes on the AWS platform, focusing on concepts such as storage, processing, analysis, and security.
