Remove Data Integration Remove Data Processing Remove Data Warehouse Remove Enterprise
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Enhance monitoring and debugging for AWS Glue jobs using new job observability metrics, Part 3: Visualization and trend analysis using Amazon QuickSight

AWS Big Data

You can slice data by different dimensions like job name, see anomalies, and share reports securely across your organization. With these insights, teams have the visibility to make data integration pipelines more efficient. Typically, you have multiple accounts to manage and run resources for your data pipeline.

Metrics 106
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The DataOps Vendor Landscape, 2021

DataKitchen

This is not surprising given that DataOps enables enterprise data teams to generate significant business value from their data. DBT (Data Build Tool) — A command-line tool that enables data analysts and engineers to transform data in their warehouse more effectively. DataOps is a hot topic in 2021.

Testing 300
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Themes and Conferences per Pacoid, Episode 8

Domino Data Lab

It includes perspectives about current issues, themes, vendors, and products for data governance. My interest in data governance (DG) began with the recent industry surveys by O’Reilly Media about enterprise adoption of “ABC” (AI, Big Data, Cloud). for DG adoption in the enterprise. Rinse, lather, repeat.

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Enable data analytics with Talend and Amazon Redshift Serverless

AWS Big Data

The integration of Talend Cloud and Talend Stitch with Amazon Redshift Serverless can help you achieve successful business outcomes without data warehouse infrastructure management. Data scientists, developers, and data analysts can access meaningful insights and build data-driven applications with zero maintenance.

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The Modern Data Stack Explained: What The Future Holds

Alation

The modern data stack is a combination of various software tools used to collect, process, and store data on a well-integrated cloud-based data platform. It is known to have benefits in handling data due to its robustness, speed, and scalability. Data ingestion/integration services. Reverse ETL tools.

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Create an end-to-end data strategy for Customer 360 on AWS

AWS Big Data

Data ingestion You have to build ingestion pipelines based on factors like types of data sources (on-premises data stores, files, SaaS applications, third-party data), and flow of data (unbounded streams or batch data). Data exploration Data exploration helps unearth inconsistencies, outliers, or errors.

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Cloudera Data Engineering – Integration steps to leverage spark on Kubernetes

Cloudera

Precisely Data Integration, Change Data Capture and Data Quality tools support CDP Public Cloud as well as CDP Private Cloud. We believe customized engine images would allow greater flexibility to our partners to build cloud-native integrations and could potentially be leveraged by our enterprise customers as well.