The adoption of Kubernetes-enabled infrastructures and applications has revealed the challenge involved in enabling persistent, reliable data storage in an ephemeral compute…
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GigaOm Radar for Data Pipelines
Today’s organizations are grappling with a growing volume of data—data of various types and formats that are stored in an increasing number…
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Commissioned Research
Autonomous Data Pipelines: Motivation, Approach and Efficacy
For decades now, data pipelines have been authored manually, either through hand-coding or interactive visual design. This has resulted in operational brittleness…
Watch NowKey Criteria for Evaluating Data Pipelines
In a data-obsessed world, the ability to leverage data for quick business insights is vital. At the same time, organizations must be…
Read MoreGigaOm Radar for Data Pipelines
In a data-obsessed world, dealing with big data has its advantages and challenges. In order to mitigate the challenges and make better…
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Commissioned Research
Making Data Ingest and Pipelines Use Case-Driven
How can an organization ensure it has access to the best data, of the highest quality, with as little delay as possible?…
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Cloud Data Pipeline Services: Foundational Platforms with Expandable Capabilities
Cloud Data Pipelines have matured and evolved to meet the increasing volume, diversity, and velocity of data flowing through the enterprise. In this report, GigaOm Analysts Andrew Brust and Yiannis Antoniou explore the basics of data pipeline platforms, including their legacy as ETL products and their evolution to the modern era of cloud-based, ELT-supporting extensible frameworks. They then analyze cloud data pipeline services from Microsoft, Amazon, and Google—Azure Data Factory, AWS Glue and AWS Data Pipeline, and GCP Dataflow and GCP Cloud Data Fusion.…
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