Amazon QuickSight First Call Deck

The document describes a presentation on Amazon Athena, a serverless interactive query service that allows users to analyze data directly from Amazon S3 using standard SQL. The presentation will introduce Athena and demonstrate how it can be used to query data in S3 without having to load it into a database first. It will also discuss how Athena uses Presto and the Glue Data Catalog under the hood and show some customer use cases for log analysis, ETL workflows, and analytics reporting using Athena with other AWS services.

In this introductory session, we cover how to convert and migrate your relational databases, non-relational databases, and data warehouses to the cloud. AWS Database Migration Service (AWS DMS) and AWS Schema Conversion Tool (AWS SCT) have been used to migrate tens of thousands of databases across the world. This includes homogeneous migrations, such as PostgreSQL to PostgreSQL, and heterogeneous migrations between different database engines, such as Oracle or SQL Server to Amazon Aurora, Amazon DynamoDB, and Amazon Redshift. Learn how to quickly and securely migrate your data and procedural code, enjoy flexibility and cost savings, and minimize the downtime of your applications.

In this full-day workshop, you will learn strategies for planning and migrating existing workloads to the AWS Cloud, including basic knowledge of planning for a migration, Application Discovery Service, AWS Migration Hub, Migration Tools e.g. CloudEndure, how to do data transfer, and last but not least, AWS Database Migration Services. There are altogether 5 modules, each represents a deep dive on the topics suggested. The first half provides an overview of migration planning principles and best practices, and the second part focuses on migration design, tools and implementation, with hands-on labs to reinforce concepts.

Amazon QuickSight is a fast, cloud-powered business intelligence service that reduces the time and cost of traditional BI software. It requires no IT effort to set up, auto-discovers AWS data sources, and reduces time to first visualization to just one minute. QuickSight uses a parallel, in-memory calculation engine called SPICE to provide fast query response times in milliseconds. It connects to various AWS and third-party data sources and applications and allows easy data visualization, dashboard creation, and report sharing.

Capgemini Cloud Assessment offers a methodology and a roadmap for Cloud migration to reduce decision risks, promote rapid user adoption and lower TCO of IT investments. It leverages pre-built accelerators such as ROI calculators, risk models and portfolio analyzers and provides three powerful deliverables in just six to eight weeks:

The document provides an overview of a 1-day AWS Partner course on data analytics solutions on AWS. The course objectives are to identify AWS analytics services, describe data analytics architectures, discuss the AWS Data Pipeline and Data Flywheel models, and describe five technical solutions: modernizing a data warehouse with Redshift, data lakes, streaming data, data governance, and machine learning. It also notes that the course will help APN Partners engage with customers by providing sufficient technical knowledge of AWS analytics services.

The document discusses building a business case for adopting AWS. It explains that establishing an effective business case process can help remove adoption friction and identify costs. The summary will cover: types of business cases; collecting infrastructure, application, and other cost data; and analyzing the data to create outputs like total cost of ownership comparisons and financial models. Building a thorough business case requires discovery of current environments and workloads followed by normalizing and analyzing the collected data.

We’ve partnered with hundreds of customers on their large-scale migrations to AWS. This session outlines some of the common challenges that our customers face and how they’ve overcome these challenges. The session also describes the patterns we’ve observed that make legacy migrations successful and the mechanisms we’ve created to help customers migrate faster.

This document discusses big data analytics architectural patterns and best practices. It covers collecting and storing data from various sources, processing and analyzing data using tools like Amazon Redshift, Amazon Athena and Amazon EMR, and selecting the appropriate tools based on factors like data structure, access patterns, and data temperature. It also discusses stream/real-time analytics tools and machine learning approaches.

Determine your best way to modernize your organization’s applications with Microsoft Azure. Want to know more? Don't hesitate to download our White Paper 'Making the Move to Application Modernization; Your Compass to Cloud Native': http://bit.ly/39XylZp

In this session we will introduce key ETL features of AWS Glue and cover common use cases ranging from scheduled nightly data warehouse loads to near real-time, event-driven ETL flows for your data lake. We will also discuss how to build scalable, efficient, and serverless ETL pipelines.

More and more enterprise companies are migrating to the AWS Cloud and there are a number of reasons why. While every organization is going to have their own unique motivations, common drivers include exiting data centers, increasing business agility, improving workforce productivity, gaining transparency in operational costs and reducing risk. The AWS Migration Acceleration Program (MAP) is designed to help enterprises that are committed to a migration journey achieve a range of these business benefits by migrating existing workloads to Amazon Web Services. In this session, you will learn about proven migration patterns, methods and tools that AWS has delivered successfully to hundreds of enterprise customers globally that will help you accelerate migrations, reduce risk and quickly realize value.

Learning Objectives: - Get an introduction to Natural Language Processing (NLP) - Learn benefits of new approaches to analytics and technologies that help empower better decisions, e.g., NLP, data prep - Build a text analytics solution with Amazon Comprehend and Amazon Relational Database Service in a step by step demo

This document discusses building a modern data analytics architecture on AWS. It provides an overview of AWS services that can be used for ingesting, processing, storing, and analyzing large volumes of data in both real-time and batch scenarios. These include services like Amazon S3, Kinesis, EMR, Redshift, Athena, Elasticsearch, and Glue for ingesting, storing, processing, and querying data. Architectures shown include real-time data pipelines, data lakes, and batch ETL/ELT processes. Performance, cost effectiveness, and scalability benefits of AWS services are highlighted.

This document discusses how companies are increasingly data-centric and how data has become a strategic asset. It introduces several AWS database and data storage services like Amazon Aurora, DynamoDB, DocumentDB, ElastiCache, Neptune, Timestream, and QLDB. These services provide different data models and use cases like relational, key-value, document, in-memory, graph, time-series, and ledger data. The document highlights features of each service like performance, scalability, availability, security, and ease of use. It also discusses how the AWS Database Migration Service can help migrate databases to AWS.

This document provides an overview of modernizing enterprise applications with Azure Platform-as-a-Service (PaaS). It discusses reasons why businesses modernize like reducing technical debt and optimizing costs. It also covers challenges of modernization like fragmented security and conflicting priorities. The document then presents different approaches to application migration and modernization on Azure including migrating to IaaS, replacing with SaaS, staying on-premises but connected to cloud, and modernizing directly on PaaS. Key benefits of a successful modernization are also listed like prioritizing security, resilience, and performance as well as innovating faster. The document concludes with case studies of companies successfully modernizing applications on Azure.

Amazon QuickSight is a fast, cloud-powered business intelligence (BI) service that makes it easy to build visualizations, perform ad-hoc analysis, and quickly get business insights from your data. In this session, we demonstrate how you can point Amazon QuickSight to AWS data stores, flat files, or other third-party data sources and begin visualizing your data in minutes. We also introduce SPICE - a new Super-fast, Parallel, In-memory, Calculation Engine in Amazon QuickSight, which performs advanced calculations and render visualizations rapidly without requiring any additional infrastructure, SQL programming, or dimensional modeling, so you can seamlessly scale to hundreds of thousands of users and petabytes of data. Lastly, you will see how Amazon QuickSight provides you with smart visualizations and graphs that are optimized for your different data types, to ensure the most suitable and appropriate visualization to conduct your analysis, and how to share these visualization stories using the built-in collaboration tools. Presented by: Matthew McClean, AWS Partner Solutions Architect, Amazon Web Services

by Androski Spicer, Solutions Architect AWS AWS Data & Analytics Week is an opportunity to learn about Amazon’s family of managed analytics services. These services provide easy, scalable, reliable, and cost-effective ways to manage your data in the cloud. We explain the fundamentals and take a technical deep dive into Amazon Redshift data warehouse; Data Lake services including Amazon EMR, Amazon Athena, & Amazon Redshift Spectrum; Log Analytics with Amazon Elasticsearch Service; and data preparation and placement services with AWS Glue and Amazon Kinesis. You'll will learn how to get started, how to support applications, and how to scale.