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Five Modern Data Architecture Trends

David Menninger's Analyst Perspectives

I was recently asked to identify key modern data architecture trends. Data architectures have changed significantly to accommodate larger volumes of data as well as new types of data such as streaming and unstructured data. Here are some of the trends I see continuing to impact data architectures.

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Critical Components of Big Data Architecture for a Translation Company

Smart Data Collective

Big data technology has been instrumental in helping organizations translate between different languages. We covered the benefits of using machine learning and other big data tools in translations in the past. How Does Big Data Architecture Fit with a Translation Company?

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Manage concurrent write conflicts in Apache Iceberg on the AWS Glue Data Catalog

AWS Big Data

In modern data architectures, Apache Iceberg has emerged as a popular table format for data lakes, offering key features including ACID transactions and concurrent write support. Manage catalog commit conflicts Catalog commit conflicts are relatively straightforward to handle through table properties.

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The future of data: A 5-pillar approach to modern data management

CIO Business Intelligence

This organism is the cornerstone of a companys competitive advantage, necessitating careful and responsible nurturing and management. To succeed in todays landscape, every company small, mid-sized or large must embrace a data-centric mindset.

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Snowflake Builds on Its Success

David Menninger's Analyst Perspectives

Traditional on-premises data processing solutions have led to a hugely complex and expensive set of data silos where IT spends more time managing the infrastructure than extracting value from the data.

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Big Data Modeling Improves Business Intelligence

TDAN

Through big data modeling, data-driven organizations can better understand and manage the complexities of big data, improve business intelligence (BI), and enable organizations to benefit from actionable insight.

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6 Big Data Mistakes You Must Avoid At All Costs

Smart Data Collective

However, while doing so, you need to work with a lot of data and this could lead to some big data mistakes. But why use data-driven marketing in the first place? When you collect data about your audience and campaigns, you’ll be better placed to understand what works for them and what doesn’t. Using Small Datasets.

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