Architecture Big Data IoT 
Scalegrid
MAY 13, 2020
In this blog post, we explain what Greenplum is, and break down the Greenplum architecture, advantages, major use cases, and how to get started. It’s architecture was specially designed to manage large-scale data warehouses and business intelligence workloads by giving you the ability to spread your data out across a multitude of servers.
Dynatrace
JULY 6, 2020
Our customers have frequently requested support for this first new batch of services, which cover databases, big data, networks, and computing. See the health of your big data resources at a glance. Azure HDInsight supports a broad range of use cases including data warehousing, machine learning, and IoT analytics.
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Scalegrid
JUNE 28, 2024
It particularly stands out in several fields, such as: Telecommunications Healthcare Finance E-commerce IoT Within these domains, RabbitMQ harnesses its potential to process substantial data and manage real-time operations effectively. It’s utilized by financial entities to process transactional data at high volumes.
The Morning Paper
OCTOBER 26, 2020
Helios also serves as a reference architecture for how Microsoft envisions its next generation of distributed big-data processing systems being built. These two narratives of reference architecture and ingestion/indexing system are interwoven throughout the paper. Why do we need a new reference architecture?
ScaleOut Software
JULY 19, 2021
We are increasingly surrounded by intelligent IoT devices, which have become an essential part of our lives and an integral component of business and industrial infrastructures. Today’s streaming analytics architectures are not equipped to make sense of this rapidly changing information and react to it as it arrives.
Scalegrid
FEBRUARY 11, 2025
Key Takeaways MySQL is a relational database management system ideal for structured data and complex relationships, ensuring data integrity and reliability. Unlike relational databases, NoSQL databases do not require a fixed schema, allowing for more flexible data models.
All Things Distributed
NOVEMBER 15, 2016
The reality is that many traditional BI solutions are built on top of legacy desktop and on-premises architectures that are decades old. They require teams of data engineers to spend months building complex data models and synthesizing the data before they can generate their first report. Enter Amazon QuickSight.
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