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Simplify data integration with AWS Glue and zero-ETL to Amazon SageMaker Lakehouse

AWS Big Data

Prerequisites Complete the following prerequisites before setting up the solution: Create a bucket in Amazon S3 called zero-etl-demo- - (for example, zero-etl-demo-012345678901-us-east-1 ). Create an AWS Glue database , such as zero_etl_demo_db and associate the S3 bucket zero-etl-demo- - as a location of the database.

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Migrate from Amazon Kinesis Data Analytics for SQL Applications to Amazon Kinesis Data Analytics Studio

AWS Big Data

Amazon Kinesis Data Analytics makes it easy to transform and analyze streaming data in real time. In this post, we discuss why AWS recommends moving from Kinesis Data Analytics for SQL Applications to Amazon Kinesis Data Analytics for Apache Flink to take advantage of Apache Flink’s advanced streaming capabilities.

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How Data Analytics Helps Understand Customers During the Onboarding Process

Smart Data Collective

It’s no secret that the key to having a successful onboarding process is data. Hence, data analytics is the main basis for product management decisions. Let’s not wait any further and find out how data analytics can help us maximize the customer onboarding process to the maximum level. Wrapping it up.

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Simplify your query performance diagnostics in Amazon Redshift with Query profiler

AWS Big Data

However, if you would like to implement this demo in your existing Amazon Redshift data warehouse, download Redshift query editor v2 notebook, Redshift Query profiler demo , and refer to the Data Loading section later in this post. To change the distribution styles, run cell #14 of demo notebook to alter table commands.

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Data Analytics in the Cloud for Developers and Founders

Speaker: Javier Ramírez, Senior AWS Developer Advocate, AWS

In this session, we address common pitfalls of building data lakes and show how AWS can help you manage data and analytics more efficiently. Javier Ramirez will present: The typical steps for building a data lake. A live demo of lake formation. Navigating idle clusters and bottlenecks.

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Streamline data discovery with precise technical identifier search in Amazon SageMaker Unified Studio

AWS Big Data

Data engineers A platform engineer performs a search for "temp_" or "backup_" to identify and clean up unused or legacy assets created during extract, transform, and load (ETL) workflows. This supports data hygiene and infrastructure cost optimization. The following screenshot shows an example of the data product.

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Derive operational insights from application logs using Automated Data Analytics on AWS

AWS Big Data

Automated Data Analytics (ADA) on AWS is an AWS solution that enables you to derive meaningful insights from data in a matter of minutes through a simple and intuitive user interface. ADA offers an AWS-native data analytics platform that is ready to use out of the box by data analysts for a variety of use cases.