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Monitor data pipelines in a serverless data lake

AWS Big Data

The combination of a data lake in a serverless paradigm brings significant cost and performance benefits. By monitoring application logs, you can gain insights into job execution, troubleshoot issues promptly to ensure the overall health and reliability of data pipelines.

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Use Apache Iceberg in a data lake to support incremental data processing

AWS Big Data

Iceberg has become very popular for its support for ACID transactions in data lakes and features like schema and partition evolution, time travel, and rollback. Apache Iceberg integration is supported by AWS analytics services including Amazon EMR , Amazon Athena , and AWS Glue. AWS Glue 3.0

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Important Considerations When Migrating to a Data Lake

Smart Data Collective

Azure Data Lake Storage Gen2 is based on Azure Blob storage and offers a suite of big data analytics features. If you don’t understand the concept, you might want to check out our previous article on the difference between data lakes and data warehouses. Then, move your data.

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Here’s Why Automation For Data Lakes Could Be Important

Smart Data Collective

Data Lakes are among the most complex and sophisticated data storage and processing facilities we have available to us today as human beings. Analytics Magazine notes that data lakes are among the most useful tools that an enterprise may have at its disposal when aiming to compete with competitors via innovation.

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Data science vs data analytics: Unpacking the differences

IBM Big Data Hub

Though you may encounter the terms “data science” and “data analytics” being used interchangeably in conversations or online, they refer to two distinctly different concepts. Meanwhile, data analytics is the act of examining datasets to extract value and find answers to specific questions.

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Centralize Your Data Processes With a DataOps Process Hub

DataKitchen

Cloud computing has made it much easier to integrate data sets, but that’s only the beginning. Creating a data lake has become much easier, but that’s only ten percent of the job of delivering analytics to users. It often takes months to progress from a data lake to the final delivery of insights.

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Fire Your Super-Smart Data Consultants with DataOps

DataKitchen

Previously, the consulting team had been using a patchwork of ETL to consolidate data from disparate sources into a data lake. In data analytics, automated orchestrations can handle data operations, testing, observability, data integration and all manner of data pipelines.