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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

This person (or group of individuals) ensures that the theory behind data quality is communicated to the development team. 2 – Data profiling. Data profiling is an essential process in the DQM lifecycle. These processes could include reports, campaigns, or financial documentation. date, month, and year).

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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. This allows developers to make changes to their processing logic on the fly while running some test data through their flow and validating that their changes work as intended.

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

Cloudera

Allows them to iteratively develop processing logic and test with as little overhead as possible. Plays nice with existing CI/CD processes to promote a data pipeline to production. Provides monitoring, alerting, and troubleshooting for production data pipelines.

Testing 81
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Data Preparation and Data Mapping: The Glue Between Data Management and Data Governance to Accelerate Insights and Reduce Risks

erwin

Organizations have spent a lot of time and money trying to harmonize data across diverse platforms , including cleansing, uploading metadata, converting code, defining business glossaries, tracking data transformations and so on. And there’s control of that landscape to facilitate insight and collaboration and limit risk.

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

AWS Big Data

You can also use the data transformation feature of Data Firehose to invoke a Lambda function to perform data transformation in batches. Athena is used to run geospatial queries on the location data stored in the S3 buckets. You can test this solution yourself using the AWS Samples GitHub repository.

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Alation and dbt Unlock Metadata and Increase Modern Data Stack Visibility

Alation

Data analysts and engineers use dbt to transform, test, and document data in the cloud data warehouse. Yet every dbt transformation contains vital metadata that is not captured – until now. Data Transformation in the Modern Data Stack. How did the data transform exactly?

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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.