datapine
SEPTEMBER 29, 2022
1) What Is Data Quality Management? 4) Data Quality Best Practices. 5) How Do You Measure Data Quality? 6) Data Quality Metrics Examples. 7) Data Quality Control: Use Case. 8) The Consequences Of Bad Data Quality. 9) 3 Sources Of Low-Quality Data. 10) Data Quality Solutions: Key Attributes.
Cloudera
JULY 21, 2022
Co-author: Mike Godwin, Head of Marketing, Rill Data. Cloudera has partnered with Rill Data, an expert in metrics at any scale, as Cloudera’s preferred ISV partner to provide technical expertise and support services for Apache Druid customers. Deploying metrics shouldn’t be so hard. Cloudera Data Warehouse).
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AWS Big Data
AUGUST 9, 2023
The advent of rapid adoption of serverless data lake architectures—with ever-growing datasets that need to be ingested from a variety of sources, followed by complex data transformation and machine learning (ML) pipelines—can present a challenge. These event changes are also routed to the same SNS topic.
AWS Big Data
JUNE 6, 2023
Furthermore, it allows for necessary actions to be taken, such as rectifying errors in the data source, refining data transformation processes, and updating data quality rules. The Lambda function is responsible for converting the data quality metrics and dispatching them to the designated email addresses via Amazon SNS.
AWS Big Data
APRIL 11, 2023
With the proliferation of IoT devices and the abundance of data generated by them, it has become possible to collect real-time data on inventory levels, customer behavior, and other key metrics. Data ingestion and storage Retail businesses have event-driven data that requires action from downstream processes.
AWS Big Data
MAY 25, 2023
Data transformation plays a pivotal role in providing the necessary data insights for businesses in any organization, small and large. To gain these insights, customers often perform ETL (extract, transform, and load) jobs from their source systems and output an enriched dataset.
Cloudera
MARCH 14, 2023
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. In the Designer, you have the ability to start and stop each step of the data pipeline, resulting in events being queued up in the connections that link the processing steps together.
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