Data Engineering Network Processing Storage 
DZone
JULY 3, 2023
Data engineering projects often require the setup and management of complex infrastructures that support data processing, storage, and analysis. Traditionally, this process involved manual configuration, leading to potential inconsistencies, human errors, and time-consuming deployments.
DZone
JANUARY 31, 2024
SIEM platforms streamline incident response processes, allowing security teams to respond quickly and effectively to security incidents. SIEM systems enable early detection of security threats and suspicious activities by analyzing vast amounts of log data in real time.
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The Netflix TechBlog
DECEMBER 21, 2020
At this scale, we can gain a significant amount of performance and cost benefits by optimizing the storage layout (records, objects, partitions) as the data lands into our warehouse. On the other hand, these optimizations themselves need to be sufficiently inexpensive to justify their own processing cost over the gains they bring.
The Netflix TechBlog
NOVEMBER 22, 2019
This entertaining romp through the tech stack serves as an introduction to how we think about and design systems, the Netflix approach to operational challenges, and how other organizations can apply our thought processes and technologies. Technology advancements in content creation and consumption have also increased its data footprint.
The Netflix TechBlog
NOVEMBER 22, 2019
This entertaining romp through the tech stack serves as an introduction to how we think about and design systems, the Netflix approach to operational challenges, and how other organizations can apply our thought processes and technologies. Technology advancements in content creation and consumption have also increased its data footprint.
Abhishek Tiwari
DECEMBER 27, 2017
Kubernetes has emerged as go to container orchestration platform for data engineering teams. In 2018, a widespread adaptation of Kubernetes for big data processing is anitcipated. Organisations are already using Kubernetes for a variety of workloads [1] [2] and data workloads are up next. Storage provisioning.
Abhishek Tiwari
DECEMBER 27, 2017
A common theme across all these trends is to remove the complexity by simplifying data management as a whole. In 2018, we anticipate that ETL will either lose relevance or the ETL process will disintegrate and be consumed by new data architectures. Unified data management architecture. Common in-memory data interfaces.
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