Remove Data Lake Remove Data Transformation Remove Download
article thumbnail

Expanding data analysis and visualization options: Amazon DataZone now integrates with Tableau, Power BI, and more

AWS Big Data

Amazon DataZone now launched authentication supports through the Amazon Athena JDBC driver, allowing data users to seamlessly query their subscribed data lake assets via popular business intelligence (BI) and analytics tools like Tableau, Power BI, Excel, SQL Workbench, DBeaver, and more.

article thumbnail

Streamline AI-driven analytics with governance: Integrating Tableau with Amazon DataZone

AWS Big Data

With this integration, you can now seamlessly query your governed data lake assets in Amazon DataZone using popular business intelligence (BI) and analytics tools, including partner solutions like Tableau. Prerequisites To get started, complete these steps: Download and install the latest Athena JDBC driver for Tableau.

Analytics 118
Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

Empower your Jira data in a data lake with Amazon AppFlow and AWS Glue

AWS Big Data

Although Jira Cloud provides reporting capability, loading this data into a data lake will facilitate enrichment with other business data, as well as support the use of business intelligence (BI) tools and artificial intelligence (AI) and machine learning (ML) applications. Search for the Jira Cloud connector.

article thumbnail

Enriching metadata for accurate text-to-SQL generation for Amazon Athena

AWS Big Data

Enterprise data is brought into data lakes and data warehouses to carry out analytical, reporting, and data science use cases using AWS analytical services like Amazon Athena , Amazon Redshift , Amazon EMR , and so on. Navigate to the AWS Service Catalog console and choose Amazon SageMaker.

Metadata 105
article thumbnail

Introducing a new unified data connection experience with Amazon SageMaker Lakehouse unified data connectivity

AWS Big Data

With the ability to browse metadata, you can understand the structure and schema of the data source, identify relevant tables and fields, and discover useful data assets you may not be aware of. You can download the results as JSON or CSV files using the download icon at the bottom of the output cell. Choose Run all.

article thumbnail

Build a data lake with Apache Flink on Amazon EMR

AWS Big Data

The Amazon EMR Flink CDC connector reads the binlog data and processes the data. Transformed data can be stored in Amazon S3. We use the AWS Glue Data Catalog to store the metadata such as table schema and table location. Verify all table metadata is stored in the AWS Glue Data Catalog.

article thumbnail

Ingest data from Google Analytics 4 and Google Sheets to Amazon Redshift using Amazon AppFlow

AWS Big Data

With Amazon AppFlow, you can run data flows at nearly any scale and at the frequency you chooseon a schedule, in response to a business event, or on demand. You can configure data transformation capabilities such as filtering and validation to generate rich, ready-to-use data as part of the flow itself, without additional steps.