Remove Data Transformation Remove Interactive Remove Publishing Remove Visualization
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Introducing Cloudera DataFlow Designer: Self-service, No-Code Dataflow Design

Cloudera

Developers need to onboard new data sources, chain multiple data transformation steps together, and explore data as it travels through the flow. A reimagined visual editor to boost developer productivity and enable self service. Interactivity when needed while saving costs.

Testing 96
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Cloudera DataFlow Designer: The Key to Agile Data Pipeline Development

Cloudera

Once a draft has been created or opened, developers use the visual Designer to build their data flow logic and validate it using interactive test sessions. When you are developing a data flow in the Flow Designer, you can publish your work to the Catalog at any time to create a versioned flow definition.

Testing 81
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The Ultimate Guide to Modern Data Quality Management (DQM) For An Effective Data Quality Control Driven by The Right Metrics

datapine

He/she assists the organization by providing clarity and insight into advanced data technology solutions. As quality issues are often highlighted with the use of dashboard software , the change manager plays an important role in the visualization of data quality. It will indicate whether data is void of significant errors.

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Gain insights from historical location data using Amazon Location Service and AWS analytics services

AWS Big Data

Developers can use the support in Amazon Location Service for publishing device position updates to Amazon EventBridge to build a near-real-time data pipeline that stores locations of tracked assets in Amazon Simple Storage Service (Amazon S3). This solution uses distance-based filtering to reduce costs and jitter.

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End-to-end development lifecycle for data engineers to build a data integration pipeline using AWS Glue

AWS Big Data

At the time of publishing of this post, the AWS CDK has two versions of the AWS Glue module: @aws-cdk/aws-glue and @aws-cdk/aws-glue-alpha , containing L1 constructs and L2 constructs , respectively. Prerequisites You need the following resources: Python 3.9 jobs locally using a Docker container. aws:/home/glue_user/.aws

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Harnessing Streaming Data: Insights at the Speed of Life

Sisense

Upsolver encapsulates the streaming engineering complexity by empowering every technical user (data engineers, DBAs, analysts, scientists, developers) to ingest, discover, and prepare streaming data for analytics. Finally, click “Publish” in the upper right hand corner, and you’re ready to create a dashboard!

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Use AWS Glue DataBrew recipes in your AWS Glue Studio visual ETL jobs

AWS Big Data

AWS Glue Studio is a graphical interface that makes it easy to create, run, and monitor extract, transform, and load (ETL) jobs in AWS Glue. DataBrew is a visual data preparation tool that enables you to clean and normalize data without writing any code.