See how Salesforce and Google are joining forces to simplify Big Data. Google Cloud Dataflow makes it easier than ever to process and enrich data, without ETL or data prep tools. Now you can push data directly to Wave Analytics and explore it on any device.

In this GCP Sketchnote, I sketch a quick overview of Google Cloud Databases across relational, non-relational and in-memory databases.
You will learn to pick a database from based on your use case:
Cloud SQL, Cloud Spanner, Bigtable, Firestore, Memorystore and Bare Metal solution.

�� Playlist – https://bit.ly/3jA8Ylz
�� Spanner Blog – https://goo.gle/3zmQMnj
�� Bigtable Blog – https://goo.gle/2TV3hWY
�� Firestore Blog – https://goo.gle/3i1yQGX
�� GitHub Repo – https://github.com/priyankavergadia/GCPSketchnote
�� Visit my website to download the sketchnote image – https://thecloudgirl.dev/
�� Follow me on Twitter – https://twitter.com/pvergadia
�� Follow me on Instagram – https://www.instagram.com/pvergadia/
�� Follow me on LinkedIn – www.linkedin.com/in/priyankavergadia

#GCPSketchnote #GooogleCloud #gcp #sketchnote #database

This video is part of an online course, Google Cloud Platform Big Data and Machine Learning Fundamentals from Google Cloud. Enroll today at https://www.coursera.org/learn/gcp-big-data-ml-fundamentals?utm_source=yt &utm_medium=social &utm_campaign=channel &utm_content=googlecloud to get access to the full course.

About this course:

This 1-week accelerated on-demand course introduces participants to the Big Data and Machine Learning capabilities of Google Cloud Platform (GCP). It provides a quick overview of the Google Cloud Platform and a deeper dive of the data processing capabilities.

At the end of this course, participants will be able to:
• Identify the purpose and value of the key Big Data and Machine Learning products in the Google Cloud Platform
• Use CloudSQL and Cloud Dataproc to migrate existing MySQL and Hadoop/Pig/Spark/Hive workloads to Google Cloud Platform
• Employ BigQuery and Cloud Datalab to carry out interactive data analysis
• Choose between Cloud SQL, BigTable and Datastore
• Train and use a neural network using TensorFlow
• Choose between different data processing products on the Google Cloud Platform

Visit https://www.coursera.org/learn/gcp-big-data-ml-fundamentals?utm_source=yt &utm_medium=social &utm_campaign=channel &utm_content=googlecloud to learn more!
Specialization: https://www.coursera.org/specializations/gcp-data-machine-learning?utm_source=yt &utm_medium=social &utm_campaign=channel &utm_content=googlecloud

Keep in touch with Coursera!
Twitter: https://twitter.com/coursera
Facebook: https://www.facebook.com/Coursera/

This session provides an overview of how customers are moving their on-premises Apache Hadoop clusters into Google Cloud Platform (GCP).

Key concepts discussed:
Best practices for running Hadoop distributions on GCP
Exploring opportunities for using Cloud Dataproc, GCP’s managed Hadoop and Spark service
Taking advantage of other GCP cloud-native solutions to handle streaming data, analytics, cold data storage, and machine learning

Build with Google Cloud → https://bit.ly/2TXBOOT

Watch more:
Next ’19 Data Analytics Sessions here → https://bit.ly/Next19DataAnalytics
Next ‘19 All Sessions playlist → https://bit.ly/Next19AllSessions

Subscribe to the GCP Channel → https://bit.ly/GCloudPlatform

Speaker(s): Christopher Crosbie, Blake DuBois

Session ID: DA105
product: Cloud – General; fullname: Christopher Crosbie, Blake DuBois; event: Google Cloud Next 2019;