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Build Better Data Products By Creating Data, Not Consuming It

Data Engineering Podcast

In this episode Nick King discusses how you can be intentional about data creation in your applications and services to reduce the friction and errors involved in building data products and ML applications. There is an undeniable amount of value and utility in that information, but it also introduces significant cost and time requirements.

Building 130
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Kafka to MongoDB: Building a Streamlined Data Pipeline

Analytics Vidhya

We know that streaming data is data that is emitted at high volume […] The post Kafka to MongoDB: Building a Streamlined Data Pipeline appeared first on Analytics Vidhya. Introduction Data is fuel for the IT industry and the Data Science Project in today’s online world.

MongoDB 217
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Water-Scrum-Fall: Is it a Myth or Reality?

Knowledge Hut

The adoption was necessitated by this particular project where design and build activities were to be iterated based on changing requirements. The approach allowed necessary flexibility to implement varied design approaches and pass them quickly to build phase to avoid large-scale changes once the complete design was done.

IT 98
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Building Databricks Data Pipelines 101

Confessions of a Data Guy

Have you ever wondered at a high level what it’s like to build production-level data pipelines on Databricks? The post Building Databricks Data Pipelines 101 appeared first on Confessions of a Data Guy. What does it look like, what tools do you use?

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Build vs Buy: 10 Hidden Costs of Building Analytics with UI Components

Many teams, as a logical first step, choose to build their own analytics with the help of UI components. Consider these 10 factors when deciding whether you should build analytics features with UI components. But eventually you’ll find that doing it yourself and at scale has hidden costs.

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Build Trust In Your Data By Understanding Where It Comes From And How It Is Used With Stemma

Data Engineering Podcast

In this episode Mark Grover explains what he is building at Stemma, how it expands on the success of the Amundsen project, and why trust is the most important asset for data teams. It builds your customer data warehouse and your identity graph on your data warehouse, with support for Snowflake, Google BigQuery, Amazon Redshift, and more.

IT 130
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Building ETL Pipelines With Generative AI

Data Engineering Podcast

Now that AI has reached the level of sophistication seen in the various generative models it is being used to build new ETL workflows. In this episode Jay Mishra shares his experiences and insights building ETL pipelines with the help of generative AI. How can you get the best results for your use case?

Building 162
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5 Signs Its Time to Replace Your Homegrown Analytics

Follow this free guide for tips on making the build to buy transition. If you built your analytics in house, chances are your basic features are no longer enough for your end users. Is it time to move on to a more robust analytics solution with more advanced capabilities?

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Why “Build or Buy?” Is the Wrong Question for Analytics

Every time an application team gets caught up in the “build vs buy” debate, it stalls projects and delays time to revenue. Partnering with an analytics development platform gives you the freedom to customize a solution without the risks and long-term costs of building your own. There is a third option.

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How to Build Data Experiences for End Users

Organizational data literacy is regularly addressed, but it’s uncommon for product managers to consider users’ data literacy levels when building products. Product managers need to research and recognize their end users' data literacy when building an application with analytic features.

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The Essential Guide to Building Analytic Applications

Download this eBook to discover insights from 16 top product experts, and learn what it takes to build a successful application with analytics at its core. What should product managers keep in mind when adding an analytics project to their roadmap?

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Embedded Analytics Insights for 2024

To better understand the factors behind the decision to build or buy analytics, insightsoftware partnered with Hanover Research to survey IT, software development, and analytics professionals on why they make the embedded analytics choices they do.

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The Definitive Guide to Predictive Analytics

The Definitive Guide to Predictive Analytics has everything you need to get started, including real-world examples, steps to build your models, and solutions to common data challenges. What You'll Learn: 7 steps to embed predictive analytics in your application—from identifying a problem to solve to building your prototype.

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The Definitive Guide to Embedded Analytics

We hope this guide will transform how you build value for your products with embedded analytics. The Definitive Guide to Embedded Analytics is designed to answer any and all questions you have about the topic. It will show you what embedded analytics are and how they can help your company.

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The Essential Guide to Analytic Applications

We interviewed 16 experts across business intelligence, UI/UX, security and more to find out what it takes to build an application with analytics at its core. Embedding dashboards, reports and analytics in your application presents unique opportunities and poses unique challenges.