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[Update Links] PluralSight – Building Data driven Apps with AWS AppSync

PluralSight – Building Data driven Apps with AWS AppSync Bookware-KNiSO
English | Size: 127.16 MB
Category: Tutorial


Release Notes: Modern applications consume data from many different sources before displaying it to the user. Consuming many data sources means more upfront work to build an application and more potential breaking points. Also, security and permission become harder and harder to manage. In this course, Building Data-driven Apps with AWS AppSync, you ll learn how to leverage the power of AppSync to unify all your data sources so they can be accessed through one central place. First you ll explore how to create APIs and Data Sources via AppSync. Next, you’ll learn how to configure the Javascript AppSync client with our backend to query and mutate data via GraphQL APIs. Finally, you ll discover how to use Amplify Datastore to store data locally when users are offline, and sync the data when an internet connection becomes available By the end of this course, you’ll be able to build a data-driven application with realtime and offline support and build in authentication using AWS AppSync

PluralSight – Handling Streaming Data with Azure Databricks Using Spark Structured Streaming

PluralSight – Handling Streaming Data with Azure Databricks Using Spark Structured Streaming Bookware-KNiSO
English | Size: 235.66 MB
Category: Tutorial


In this course, you will deep-dive into Spark Structured Streaming, see its features in action, and use it to build end-to-end, complex & reliable streaming pipelines using PySpark. And you will be using Azure Databricks platform to build & run them

Lynda – 15 Mistakes to Avoid in Data Science

15 Mistakes to Avoid in Data Science
English | Size: 358.9 MB
Category: Tutorial


As a data scientist, your goal is to always be growing your skills. But, if you realize it or not, there are errors you may be making that are keeping you from moving to the next level. In this course, learn the top 15 data science mistakes: misunderstanding business problems, using the wrong tools, starting without a plan, and much more. Four leading data scientists share the hard-won lessons they’ve learned about alienating colleagues with technical jargon, moving too fast, and using sample sizes that are just too small. Find out why you should make your best effort to prevent bias-and avoid overpromising solutions to stakeholders. Plus, learn why writing custom code can lead to a big waste of time and why the most promising data science insights fall flat without a compelling story.