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| Name | Back to Basics: Building an Efficient Data Lake |
| Video Uploader | Video From Amazon Web Services |
| Upload Date | This Video Uploaded At 05-10-2023 16:52:12 |
| Video Discription | The amount of data generated by IoT, smart devices, cloud applications, and social is growing exponentially. You need ways to easily and cost-effectively analyze all of this data with minimal time-to-insight, regardless of the data source. Join Orit as she dives deep into the basics of data lake and lake house patterns. Additional Resources: What is a Data Lake: https://aws.amazon.com/big-data/datalakes-and-analytics/what-is-a-data-lake/?nc=sn&loc=2 Lake house architecture blogpost: https://aws.amazon.com/blogs/big-data/build-a-lake-house-architecture-on-aws/ Amazon Kinesis Data Firehose: https://docs.aws.amazon.com/firehose/latest/dev/what-is-this-service.html AWS Glue: https://docs.aws.amazon.com/glue/latest/dg/what-is-glue.html Amazon Athena: https://docs.aws.amazon.com/athena/latest/ug/what-is.html Amazon QuickSight: https://docs.aws.amazon.com/quicksight/latest/user/welcome.html Amazon Redshift: https://docs.aws.amazon.com/redshift/latest/mgmt/welcome.html Check out more resources for architecting in the #AWS cloud: http://amzn.to/3qXIsWN #AWS #AmazonWebServices #CloudComputing #BackToBasics #DataLake [6l8HqX-P_9o] |
| Category | Science & Technology |
| Tags | AWS | Amazon Web Services | Cloud | AWS Cloud | Cloud Computing | Amazon AWS | Amazon Simple Solution Service (S3) | Amazon Kinesis Data Firehose | Data Formatting | Data Partitioning | Data Compaction | AWS Glue | Amazon Athena | Amazon QuickSight | Relational DBs | Amazon Elastic Search | Apache Spark | Presto | Amazon Redshift | Data Lake | Back to Basics |
