Application Architecture Data Integrity Evaluation 
Pure Storage
OCTOBER 4, 2023
A data mesh is a novel approach to data management that revolutionizes how organizations handle data. It advocates for breaking down data silos and organizing data by specific domains. These teams become accountable for their data domains. This empowers them to make faster data-driven decisions.
Pure Storage
AUGUST 1, 2023
We’ll outline their features, benefits, and differences to help you make an informed choice for which one to use for your particular applications and/or business needs. Its schema-less architecture enables developers to adapt to changing data requirements without constraints, making it an excellent choice for agile development environments.
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Pure Storage
MAY 10, 2024
From 2012 to 2019, AFAs have risen in popularity and now drive approximately 80% or more of all storage shipments for performant application environments. Conversely, enterprises value performance, endurance, reliability, and data integrity—all characteristics that require deep engineering beyond the core consumer SSDs.
Erwood Group
FEBRUARY 4, 2024
Predictive Analytics for Risk Assessment: How it Works: AI algorithms analyze historical data, identify patterns, and predict potential risks and disruptions. Application: Predictive analytics enables organizations to rapidly assess risks and proactively implement measures to mitigate the impact of potential disruptions.
Pure Storage
SEPTEMBER 26, 2023
Organizations can quickly provision and scale databases according to their requirements, reducing time to market for applications. DBaaS is primarily focused on providing managed database services, while PaaS offers a broader platform for application development and deployment.
Pure Storage
AUGUST 3, 2023
Tampering: Tampering refers to the ability of an attacker to modify data or software without detection. This can be a serious threat to data integrity and system availability. Assets that are vulnerable to tampering include software binaries, configuration files, and data in transit.
Advancing Analytics
JANUARY 22, 2023
To scale out we need a scaled storage layer, such as a Data Lake, to which the architecture is typically based on Hadoop, HDFS. The sheer volume and variety of data being generated today has made it increasingly difficult for traditional RDBMS to keep up. Any single server idea has an upper limit (CPU/Memory/IO).
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