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Top 10 Analytics And Business Intelligence Trends For 2020

datapine

The rise of self-service analytics democratized the data product chain. Suddenly advanced analytics wasn’t just for the analysts. Businesses of all sizes are no longer asking if they need increased access to business intelligence analytics but what is the best BI solution for their specific business.

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The DataOps Vendor Landscape, 2021

DataKitchen

Read the complete blog below for a more detailed description of the vendors and their capabilities. Process Analytics. DataOps needs a directed graph-based workflow that contains all the data access, integration, model and visualization steps in the data analytic production process. DataOps is a hot topic in 2021.

Testing 304
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Top 5 Tips For Conducting Successful BI Projects With Examples & Templates

datapine

BI projects aren’t just for the big fishes in the sea anymore; the technology has developed rapidly, the software has become more accessible while business intelligence and analytics projects implemented in various industries regularly, no matter the shape and size, small businesses or large enterprises. What Is A BI Project?

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The 10 Essential SaaS Trends You Should Watch Out For In 2020

datapine

“Software as a service” (SaaS) is becoming an increasingly viable choice for organizations looking for the accessibility and versatility of software solutions and online data analysis tools without the need to rely on installing and running applications on their own computer systems and data centers. Dispelling 3 Common SaaS Myths.

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Implementing a Pharma Data Mesh using DataOps

DataKitchen

Each distinct phase of the drug lifecycle requires a unique focus for analytics. The analytics team is under tremendous pressure during the early phases of the drug’s lifetime. The business users propose questions and ideas for new analytics and require rapid response time. Pharma Data Requirements.

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Recap of Amazon Redshift key product announcements in 2024

AWS Big Data

Amazon Redshift , launched in 2013, has undergone significant evolution since its inception, allowing customers to expand the horizons of data warehousing and SQL analytics. This allowed customers to scale read analytics workloads and offered isolation to help maintain SLAs for business-critical applications.

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Addressing Data Mesh Technical Challenges with DataOps

DataKitchen

We’ve talked about data mesh in organizational terms (see our first post, “ What is a Data Mesh? ”) and how team structure supports agility. The domain also includes code that acts upon the data, including tools, pipelines, and other artifacts that drive analytics execution. Data mesh does not replace or require any of these.

Testing 246