Remove Data Transformation Remove Download Remove Metadata
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Enriching metadata for accurate text-to-SQL generation for Amazon Athena

AWS Big Data

These data processing and analytical services support Structured Query Language (SQL) to interact with the data. Writing SQL queries requires not just remembering the SQL syntax rules, but also knowledge of the tables metadata, which is data about table schemas, relationships among the tables, and possible column values.

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

AWS Big Data

This new JDBC connectivity feature enables our governed data to flow seamlessly into these tools, supporting productivity across our teams.” Getting started To get started, download and install the latest Athena JDBC driver for your tool of choice. Download the latest JDBC driver—version 3.x.

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How to Implement Data Lineage Mapping Techniques

Octopai

Look for the Metadata. In order to perform accurate data lineage mapping, every process in the system that transforms or touches the data must be recorded. This metadata (read: data about your data) is key to tracking your data. Data Lineage by Tagging or Self-Contained Data Lineage.

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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.

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Lay the groundwork now for advanced analytics and AI

CIO Business Intelligence

“You had to be an expert in the programming language that interacts with that data, and understand the relationships of each data element within each data source, let alone understand its relation to elements in other data sources,” he says. Without those templates, it’s hard to add such information after the fact.”

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

AWS Big Data

Solution overview This solution uses Amazon AppFlow to retrieve data from the Jira Cloud. The data is synchronized to an Amazon Simple Storage Service (Amazon S3) bucket using an initial full download and subsequent incremental downloads of changes. Leave Catalog your data in the AWS Glue Data Catalog unselected.

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Cloudera’s Open Data Lakehouse Supercharged with dbt Core(tm)

Cloudera

We’re excited to announce the general availability of the open source adapters for dbt for all the engines in CDP — Apache Hive , Apache Impala , and Apache Spark, with added support for Apache Livy and Cloudera Data Engineering. This variety can result in a lack of standardization, leading to data duplication and inconsistency.